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Khedmati Morasae E, Derbyshire DW, Amini P, Ebrahimi T. Social determinants of spatial inequalities in COVID-19 outcomes across England: A multiscale geographically weighted regression analysis. SSM Popul Health 2024; 25:101621. [PMID: 38420111 PMCID: PMC10899060 DOI: 10.1016/j.ssmph.2024.101621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/01/2024] [Accepted: 02/02/2024] [Indexed: 03/02/2024] Open
Abstract
A variety of factors are associated with greater COVID-19 morbidity or mortality, due to how these factors influence exposure to (in the case of morbidity) or severity of (in the case of mortality) COVID-19 infections. We use multiscale geographically weighted regression to study spatial variation in the factors associated with COVID-19 morbidity and mortality rates at the local authority level across England (UK). We investigate the period between March 2020 and March 2021, prior to the rollout of the COVID-19 vaccination program. We consider a variety of factors including demographic (e.g. age, gender, and ethnicity), health (e.g. rates of smoking, obesity, and diabetes), social (e.g. Index of Multiple Deprivation), and economic (e.g. the Gini coefficient and economic complexity index) factors that have previously been found to impact COVID-19 morbidity and mortality. The Index of Multiple Deprivation has a significant impact on COVID-19 cases and deaths in all local authorities, although the effect is the strongest in the south of England. Higher proportions of ethnic minorities are associated with higher levels of COVID-19 mortality, with the strongest effect being found in the west of England. There is again a similar pattern in terms of cases, but strongest in the north of the country. Other factors including age and gender are also found to have significant effects on COVID-19 morbidity and mortality, with differential spatial effects across the country. The results provide insights into how national and local policymakers can take account of localized factors to address spatial health inequalities and address future infectious disease pandemics.
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Affiliation(s)
- Esmaeil Khedmati Morasae
- Research Fellow in Operational Research, Exeter University Business School, University of Exeter, UK
| | - Daniel W. Derbyshire
- Department of Public Health and Sports Science, Faculty of Health and Life Science, University of Exeter, UK
| | - Payam Amini
- School of Medicine, Keele University, Keele, Staffordshire, UK
| | - Tahera Ebrahimi
- Lecturer in Finance, Business School, Manchester Metropolitan University, UK
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Shi X, Ling GHT, Leng PC, Rusli N, Matusin AMRA. Associations between institutional-social-ecological factors and COVID -19 case-fatality: Evidence from 134 countries using multiscale geographically weighted regression (MGWR). One Health 2023; 16:100551. [PMID: 37153369 PMCID: PMC10141798 DOI: 10.1016/j.onehlt.2023.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/09/2023] Open
Abstract
During the period in which the Omicron coronavirus variant was rapidly spreading, the impact of the institutional-social-ecological dimensions on the case-fatality rate was rarely afforded attention. By adopting the diagnostic social-ecological system (SES) framework, the present paper aims to identify the impact of institutional-social-ecological factors on the case-fatality rate of COVID-19 in 134 countries and regions and test their spatial heterogeneity. Using statistical data from the Our World In Data website, the present study collected the cumulative case-fatality rate from 9 November 2021 to 23 June 2022, along with 11 country-level institutional-social-ecological factors. By comparing the goodness of fit of the multiple linear regression model and the multiscale geographically weighted regression (MGWR) model, the study demonstrated that the effects of SES factors exhibit significant spatial heterogeneity in relation to the case-fatality rate of COVID-19. After substituting the data into the MGWR model, six SES factors were identified with an R square of 0.470 based on the ascending effect size: COVID-19 vaccination policy, age dependency ratio, press freedom, gross domestic product (GDP), COVID-19 testing policy, and population density. The GWR model was used to test and confirm the robustness of the research results. Based on the analysis results, it is suggested that the world needs to meet four conditions to restore normal economic activity in the wake of the COVID-19 pandemic: (i) Countries should increase their COVID-19 vaccination coverage and maximize COVID-19 testing expansion. (ii) Countries should increase public health facilities available to provide COVID-19 treatment and subsidize the medical costs of COVID-19 patients. (iii) Countries should strictly review COVID-19 news reports and actively publicize COVID-19 pandemic prevention knowledge to the public through a range of media. (iv) Countries should adopt an internationalist spirit of cooperation and help each other to navigate the COVID-19 pandemic. The study further tests the applicability of the SES framework to the field of COVID-19 prevention and control based on the existing research, offering novel policy insights to cope with the COVID-19 pandemic that coexists with long-term human production and life for a long time.
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Affiliation(s)
- Xuerui Shi
- Department of Urban and Regional Planning, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
| | - Gabriel Hoh Teck Ling
- Department of Urban and Regional Planning, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
| | - Pau Chung Leng
- Department of Architecture, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
| | - Noradila Rusli
- Department of Urban and Regional Planning, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
- Centre for Innovative Planning and Development (CIPD), Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
| | - Ak Mohd Rafiq Ak Matusin
- Department of Urban and Regional Planning, Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
- Centre for Innovative Planning and Development (CIPD), Faculty of Built Environment & Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia
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Abstract
This paper develops the argument that post-COVID-19 recovery strategies need to focus on building back fairer cities and communities, and that this requires a strong embedding of 'age-friendly' principles to support marginalised groups of older people, especially those living in deprived urban neighbourhoods, trapped in poor quality housing. It shows that older people living in such areas are likely to experience a 'double lockdown' as a result of restrictions imposed by social distancing combined with the intensification of social and spatial inequalities. This argument is presented as follows: first, the paper examines the disproportionate impact of COVID-19 on older people, highlighting how the pandemic is both creating new and reinforcing existing inequalities in ageing along the lines of gender, class, ethnicity, race, ability and sexuality. Second, the paper explores the role of spatial inequalities in the context of COVID-19, highlighting how the pandemic is having a disproportionate impact on deprived urban areas already affected by cuts to public services, the loss of social infrastructure and pressures on the voluntary sector. Finally, the paper examines how interrelated social inequalities at both the individual and spatial level are affecting the lives of older people living in deprived urban neighbourhoods during the pandemic. The paper concludes by developing six principles for 'age-friendly' community recovery planning aimed at maintaining and improving the quality of life and wellbeing of older residents in the post-pandemic city.
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Bănică A, Muntele I. Local and regional factors of spatial differentiation of the excess mortality related to the COVID-19 pandemic in Romania. Lett Spat Resour Sci 2023; 16:23. [PMID: 37220627 PMCID: PMC10189221 DOI: 10.1007/s12076-023-00340-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 03/30/2023] [Indexed: 05/25/2023]
Abstract
COVID-19 revealed some major weaknesses and threats that are related to the level of territorial development. In Romania, the manifestation and the impact of the pandemic were not homogenous, which was influenced, to a large extent, by a diversity of sociodemographic, economic, and environmental/geographic factors. The paper is an exploratory analysis focused on selecting and integrating multiple indicators that could explain the spatial differentiation of COVID-19-related excess mortality (EXCMORT) in 2020 and 2021. These indicators include, among others, health infrastructure, population density and mobility, health services, education, the ageing population and distance to the closest urban center. We analyzed the data from local (LAU2) and county level (NUTS3) by applying multiple linear regression and geographically weighted regression models. The results show that mobility and lower social distancing were far more critical factors for higher mortality than the intrinsic vulnerability of the population, at least in the first two years of COVID-19. However, the highly differentiated patterns and specificities of different areas of Romania resulting from the modelling of EXCMORT factors drive to the conclusion that the decision-making approaches should be place-specific in order to have more efficiency in case of pandemics.
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Affiliation(s)
- Alexandru Bănică
- Alexandru Ioan Cuza” University of Iași, Iași, Romania
- Geographic Research Center, Romanian Academy, Iași Branch, Iași, Romania
| | - Ionel Muntele
- Alexandru Ioan Cuza” University of Iași, Iași, Romania
- Geographic Research Center, Romanian Academy, Iași Branch, Iași, Romania
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Park J, Michels A, Lyu F, Han SY, Wang S. Daily changes in spatial accessibility to ICU beds and their relationship with the case-fatality ratio of COVID-19 in the state of Texas, USA. Appl Geogr 2023; 154:102929. [PMID: 36960405 PMCID: PMC10011039 DOI: 10.1016/j.apgeog.2023.102929] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/08/2023] [Accepted: 03/12/2023] [Indexed: 06/18/2023]
Abstract
During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios.
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Affiliation(s)
- Jinwoo Park
- Department of Geography and Geographic Information Science, University of Illinois Urbana- Champaign, Urbana, IL, USA
- CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois Urbana- Champaign, Urbana, IL, USA
| | - Alexander Michels
- Department of Geography and Geographic Information Science, University of Illinois Urbana- Champaign, Urbana, IL, USA
- CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois Urbana- Champaign, Urbana, IL, USA
| | - Fangzheng Lyu
- Department of Geography and Geographic Information Science, University of Illinois Urbana- Champaign, Urbana, IL, USA
- CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois Urbana- Champaign, Urbana, IL, USA
| | - Su Yeon Han
- Department of Geography and Environmental Studies, Texas State University, San Marcos, TX, USA
| | - Shaowen Wang
- Department of Geography and Geographic Information Science, University of Illinois Urbana- Champaign, Urbana, IL, USA
- CyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois Urbana- Champaign, Urbana, IL, USA
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Feng DZ. Spatiotemporal pattern of COVID-19 mortality and its relationship with socioeconomic and environmental factors in England. Spat Spatiotemporal Epidemiol 2023. [DOI: 10.1016/j.sste.2023.100579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Zsichla L, Müller V. Risk Factors of Severe COVID-19: A Review of Host, Viral and Environmental Factors. Viruses 2023; 15:175. [PMID: 36680215 PMCID: PMC9863423 DOI: 10.3390/v15010175] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 01/04/2023] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
The clinical course and outcome of COVID-19 are highly variable, ranging from asymptomatic infections to severe disease and death. Understanding the risk factors of severe COVID-19 is relevant both in the clinical setting and at the epidemiological level. Here, we provide an overview of host, viral and environmental factors that have been shown or (in some cases) hypothesized to be associated with severe clinical outcomes. The factors considered in detail include the age and frailty, genetic polymorphisms, biological sex (and pregnancy), co- and superinfections, non-communicable comorbidities, immunological history, microbiota, and lifestyle of the patient; viral genetic variation and infecting dose; socioeconomic factors; and air pollution. For each category, we compile (sometimes conflicting) evidence for the association of the factor with COVID-19 outcomes (including the strength of the effect) and outline possible action mechanisms. We also discuss the complex interactions between the various risk factors.
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Affiliation(s)
- Levente Zsichla
- Institute of Biology, Eötvös Loránd University, 1117 Budapest, Hungary
- National Laboratory for Health Security, Eötvös Loránd University, 1117 Budapest, Hungary
| | - Viktor Müller
- Institute of Biology, Eötvös Loránd University, 1117 Budapest, Hungary
- National Laboratory for Health Security, Eötvös Loránd University, 1117 Budapest, Hungary
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Rodriguez-Villamizar LA, Marín D, Piñeros-Jiménez JG, Rojas-Sánchez OA, Serrano-Lomelin J, Herrera V. Intraurban Geographic and Socioeconomic Inequalities of Mortality in Four Cities in Colombia. Int J Environ Res Public Health 2023; 20:992. [PMID: 36673751 PMCID: PMC9859133 DOI: 10.3390/ijerph20020992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 12/22/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Mortality inequalities have been described across Latin American countries, but less is known about inequalities within cities, where most populations live. We aimed to identify geographic and socioeconomic inequalities in mortality within the urban areas of four main cities in Colombia. We analyzed mortality due to non-violent causes of diseases in adults between 2015 and 2019 using census sectors as unit of analysis in Barranquilla, Bogotá, Cali, and Medellín. We calculated smoothed Bayesian mortality rates as main health outcomes and used concentration indexes (CInd) for assessing inequalities using the multidimensional poverty index (MPI) as the socioeconomic measure. Moran eigenvector spatial filters were calculated to capture the spatial patterns of mortality and then used in multivariable models of the association between mortality rates and quintiles of MPI. Social inequalities were evident but not consistent across cities. The most disadvantaged groups showed the highest mortality rates in Cali. Geographic inequalities in mortality rates, regardless of the adults and poverty distribution, were identified in each city, suggesting that other social, environmental, or individual conditions are impacting the spatial distribution of mortality rates within the four cities.
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Affiliation(s)
| | - Diana Marín
- School of Medicine, Universidad Pontificia Bolivariana, Medellin 050031, Colombia
| | | | - Oscar Alberto Rojas-Sánchez
- Division of Public Health Research, Project Bank Team, National Institute of Health-INS Colombia, Bogotá 111321, Colombia
| | | | - Victor Herrera
- Department of Public Health, School of Medicine, Universidad Industrial de Santander, Bucaramanga 681012, Colombia
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McGowan VJ, Bambra C. COVID-19 mortality and deprivation: pandemic, syndemic, and endemic health inequalities. Lancet Public Health 2022; 7:e966-e975. [PMID: 36334610 PMCID: PMC9629845 DOI: 10.1016/s2468-2667(22)00223-7] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 08/23/2022] [Accepted: 08/23/2022] [Indexed: 11/06/2022]
Abstract
COVID-19 has exacerbated endemic health inequalities resulting in a syndemic pandemic of higher mortality and morbidity rates among the most socially disadvantaged. We did a scoping review to identify and synthesise published evidence on geographical inequalities in COVID-19 mortality rates globally. We included peer-reviewed studies, from any country, written in English that showed any area-level (eg, neighbourhood, town, city, municipality, or region) inequalities in mortality by socioeconomic deprivation (ie, measured via indices of multiple deprivation: the percentage of people living in poverty or proxy factors including the Gini coefficient, employment rates, or housing tenure). 95 papers from five WHO global regions were included in the final synthesis. A large majority of the studies (n=86) found that COVID-19 mortality rates were higher in areas of socioeconomic disadvantage than in affluent areas. The subsequent discussion reflects on how the unequal nature of the pandemic has resulted from a syndemic of COVID-19 and endemic inequalities in chronic disease burden.
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Affiliation(s)
- Victoria J McGowan
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK; Fuse-The Centre for Translational Research in Public Health, Newcastle Upon Tyne, UK
| | - Clare Bambra
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK; Fuse-The Centre for Translational Research in Public Health, Newcastle Upon Tyne, UK.
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Nazia N, Law J, Butt ZA. Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada. Spat Spatiotemporal Epidemiol 2022; 43:100534. [PMID: 36460444 PMCID: PMC9411108 DOI: 10.1016/j.sste.2022.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/19/2022] [Accepted: 08/24/2022] [Indexed: 12/15/2022]
Abstract
The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada,Corresponding author at: School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada,School of Planning, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave W., Waterloo, ON N2L3G1, Canada
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Schmitz A, Garten C, Kühne S, Brandt M. Worries about inadequate medical treatment in case of a COVID-19 infection: the role of social inequalities, COVID-19 prevalence and healthcare infrastructure. BMC Public Health 2022; 22:1761. [PMID: 36114486 PMCID: PMC9482236 DOI: 10.1186/s12889-022-14024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/16/2022] [Indexed: 11/23/2022] Open
Abstract
Background This study investigates individual and regional determinants of worries about inadequate medical treatment in case of a COVID-19 infection, an important indicator of mental wellbeing in pandemic times as it potentially affects the compliance with mitigation measures and the willingness to get vaccinated. The analyses shed light on the following questions: Are there social inequalities in worries about inadequate medical treatment in case of a COVID-19 infection? What is the role of the regional spread of COVID-19 infections and regional healthcare capacities? Methods Based on data derived from the German Socioeconomic Panel (SOEP), a representative sample of the German population aged 18 years and over, we estimated multilevel logistic regression models with individual-level (level 1) and regional-level (level 2) variables. The regional variables of interest were (a) the number of COVID-19 infections, (b) the number of hospital beds as an overall measure of the regional healthcare capacities, and (c) the number of free intensive care units as a measure of the actual capacities for treating patients with severe courses of COVID-19. Results Women, older respondents, persons with migrant background and those with a lower socioeconomic status were more likely to report worries about inadequate medical treatment in case of a COVID-19 infection. Moreover, respondents with chronic illness, lower subjective health and those who consider COVID-19 as a threat for their own health were more likely to report worries. In addition, also regional characteristics were relevant. Worries were more common in poorer regions with higher COVID-19 infections and worse health infrastructure as indicated by the number of hospital beds. Conclusions The analysis not only indicates that several social groups are more concerned about inadequate medical treatment in case of a COVID-19 infection, but also highlights the need for considering regional-level influences, such as the spread of the virus, poverty rates and healthcare infrastructure, when analyzing the social and health-related consequences of the pandemic.
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Welsh C, Albani V, Matthews F, Bambra C. Inequalities in the evolution of the COVID-19 pandemic: an ecological study of inequalities in mortality in the first wave and the effects of the first national lockdown in England. BMJ Open 2022; 12:e058658. [PMID: 35948380 PMCID: PMC9378950 DOI: 10.1136/bmjopen-2021-058658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES To examine how ecological inequalities in COVID-19 mortality rates evolved in England, and whether the first national lockdown impacted them. This analysis aimed to provide evidence for important lessons to inform public health planning to reduce inequalities in any future pandemics. DESIGN Longitudinal ecological study. SETTING 307 lower-tier local authorities in England. PRIMARY OUTCOME MEASURE Age-standardised COVID-19 mortality rates by local authority, regressed on Index of Multiple Deprivation (IMD) and relevant epidemic dynamics. RESULTS Local authorities that started recording COVID-19 deaths earlier were more deprived, and more deprived authorities saw faster increases in their death rates. By 6 April 2020 (week 15, the earliest time that the 23 March lockdown could have begun affecting death rates) the cumulative death rate in local authorities in the two most deprived deciles of IMD was 54% higher than the rate in the two least deprived deciles. By 4 July 2020 (week 27), this gap had narrowed to 29%. Thus, inequalities in mortality rates by decile of deprivation persisted throughout the first wave, but reduced during the lockdown. CONCLUSIONS This study found significant differences in the dynamics of COVID-19 mortality at the local authority level, resulting in inequalities in cumulative mortality rates during the first wave of the pandemic. The first lockdown in England was fairly strict-and the study found that it particularly benefited those living in more deprived local authorities. Care should be taken to implement lockdowns early enough, in the right places-and at a sufficiently strict level-to maximally benefit all communities, and reduce inequalities.
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Affiliation(s)
- Claire Welsh
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Viviana Albani
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Fiona Matthews
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Clare Bambra
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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Ahdika A, Primandari AH, Adlin FN. Considering the temporal interdependence of human mobility and COVID-19 concerning Indonesia’s large-scale social distancing policies. Qual Quant 2022; 57:2791-2810. [PMID: 35966132 PMCID: PMC9362535 DOI: 10.1007/s11135-022-01497-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/23/2022] [Indexed: 11/29/2022]
Affiliation(s)
- Atina Ahdika
- Department of Statistics, Universitas Islam Indonesia, Jalan Kaliurang Km 14.5, Sleman Yogyakarta, 55584 Indonesia
| | - Arum Handini Primandari
- Department of Statistics, Universitas Islam Indonesia, Jalan Kaliurang Km 14.5, Sleman Yogyakarta, 55584 Indonesia
| | - Falah Novayanda Adlin
- Department of Statistics, Universitas Islam Indonesia, Jalan Kaliurang Km 14.5, Sleman Yogyakarta, 55584 Indonesia
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Nazia N, Butt ZA, Bedard ML, Tang W, Sehar H, Law J. Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review. IJERPH 2022; 19:8267. [PMID: 35886114 PMCID: PMC9324591 DOI: 10.3390/ijerph19148267] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/01/2022] [Accepted: 07/04/2022] [Indexed: 02/04/2023]
Abstract
The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.
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Nazia N, Law J, Butt ZA. Identifying spatiotemporal patterns of COVID-19 transmissions and the drivers of the patterns in Toronto: a Bayesian hierarchical spatiotemporal modelling. Sci Rep 2022; 12:9369. [PMID: 35672355 PMCID: PMC9172088 DOI: 10.1038/s41598-022-13403-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Accepted: 05/24/2022] [Indexed: 01/08/2023] Open
Abstract
Spatiotemporal patterns and trends of COVID-19 at a local spatial scale using Bayesian approaches are hardly observed in literature. Also, studies rarely use satellite-derived long time-series data on the environment to predict COVID-19 risk at a spatial scale. In this study, we modelled the COVID-19 pandemic risk using a Bayesian hierarchical spatiotemporal model that incorporates satellite-derived remote sensing data on land surface temperature (LST) from January 2020 to October 2021 (89 weeks) and several socioeconomic covariates of the 140 neighbourhoods in Toronto. The spatial patterns of risk were heterogeneous in space with multiple high-risk neighbourhoods in Western and Southern Toronto. Higher risk was observed during Spring 2021. The spatiotemporal risk patterns identified 60% of neighbourhoods had a stable, 37% had an increasing, and 2% had a decreasing trend over the study period. LST was positively, and higher education was negatively associated with the COVID-19 incidence. We believe the use of Bayesian spatial modelling and the remote sensing technologies in this study provided a strong versatility and strengthened our analysis in identifying the spatial risk of COVID-19. The findings would help in prevention planning, and the framework of this study may be replicated in other highly transmissible infectious diseases.
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Affiliation(s)
- Nushrat Nazia
- School of Public Health Sciences, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada.
| | - Jane Law
- School of Public Health Sciences, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada
- School of Planning, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, 200 University Ave., Waterloo, ON, N2L3G1, Canada
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16
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Polo G, Soler-Tovar D, Villamil Jimenez LC, Benavides-Ortiz E, Mera Acosta C. Bayesian spatial modeling of COVID-19 case-fatality rate inequalities. Spat Spatiotemporal Epidemiol 2022; 41:100494. [PMID: 35691638 PMCID: PMC8956344 DOI: 10.1016/j.sste.2022.100494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/04/2021] [Accepted: 02/28/2022] [Indexed: 11/17/2022]
Abstract
The ongoing outbreak of COVID-19 challenges the health systems and epidemiological responses of all countries worldwide. Although preventive measures have been globally considered, the spatial heterogeneity of its effectiveness is evident, underscoring global health inequalities. Using Bayesian-based Markov chain Monte Carlo simulations, we identify the spatial association of socioeconomic factors and the risk for dying from COVID-19 in Colombia. We confirm that from March 16 to October 04, 2020, the COVID-19 case-fatality rate and the multidimensional poverty index have a heterogeneous spatial distribution. Spatial analysis reveals that the risk of dying from COVID-19 increases in regions with a higher proportion of poor people with dwelling (RR 1.74 95%CI = 1.54–9.75), educational (RR 1.69 95%CI = 1.36–5.94), childhood/youth (RR 1.35 95%CI = 1.08–4.03), and health (RR 1.16 95%CI = 1.06–2.04) deprivations. These findings evidence the vulnerability of most disadvantaged members of society to dying in a pandemic and assist the spatial planning of preventive strategies focused on vulnerable communities.
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17
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de Souza APG, Mota CMDM, Rosa AGF, de Figueiredo CJJ, Candeias ALB. A spatial-temporal analysis at the early stages of the COVID-19 pandemic and its determinants: The case of Recife neighborhoods, Brazil. PLoS One 2022; 17:e0268538. [PMID: 35580093 PMCID: PMC9113566 DOI: 10.1371/journal.pone.0268538] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 04/30/2022] [Indexed: 12/11/2022] Open
Abstract
The outbreak of COVID-19 has led to there being a worldwide socio-economic crisis, with major impacts on developing countries. Understanding the dynamics of the disease and its driving factors, on a small spatial scale, might support strategies to control infections. This paper explores the impact of the COVID-19 on neighborhoods of Recife, Brazil, for which we examine a set of drivers that combines socio-economic factors and the presence of non-stop services. A three-stage methodology was conducted by conducting a statistical and spatial analysis, including clusters and regression models. COVID-19 data were investigated concerning ten dates between April and July 2020. Hotspots of the most affected regions and their determinant effects were highlighted. We have identified that clusters of confirmed cases were carried from a well-developed neighborhood to socially deprived areas, along with the emergence of hotspots of the case-fatality rate. The influence of age-groups, income, level of education, and the access to essential services on the spread of COVID-19 was also verified. The recognition of variables that influence the spatial spread of the disease becomes vital for pinpointing the most vulnerable areas. Consequently, specific prevention actions can be developed for these places, especially in heterogeneous cities.
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Affiliation(s)
| | - Caroline Maria de Miranda Mota
- Programa de Pós-graduação em Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
- Departamento de Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
- * E-mail:
| | - Amanda Gadelha Ferreira Rosa
- Programa de Pós-graduação em Engenharia de Produção, Universidade Federal de Pernambuco, Recife, Pernambuco, Brazil
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18
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Li H, Zhang G, Cao Y. Forest Area, CO2 Emission, and COVID-19 Case-Fatality Rate: A Worldwide Ecological Study Using Spatial Regression Analysis. Forests 2022; 13:736. [DOI: 10.3390/f13050736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Spatial analysis is essential to understand the spreading of the COVID-19 pandemic. Due to numerous factors of multi-disciplines involved, the current pandemic is yet fully known. Hence, the current study aimed to expand the knowledge on the pandemic by exploring the roles of forests and CO2 emission in the COVID-19 case-fatality rate (CFR) at the global level. Data were captured on the forest coverage rate and CO2 emission per capita from 237 countries. Meanwhile, extra demographic and socioeconomic variables were also included to adjust for potential confounding. Associations between the forest coverage rate and CO2 emission per capita and the COVID-19 CFR were assessed using spatial regression analysis, and the results were further stratified by country income levels. Although no distinct association between the COVID-19 CFR and forest coverage rate or CO2 emission per capita was found worldwide, we found that a 10% increase in forest coverage rates was associated with a 2.37‰ (95%CI: 3.12, 1.62) decrease in COVID-19 CFRs in low-income countries; and a 10% increase in CO2 emission per capita was associated with a 0.94‰ (95%CI: 1.46, 0.42) decrease in COVID-19 CFRs in low-middle-income countries. Since a strong correlation was observed between the CO2 emission per capita and GDP per capita (r = 0.89), we replaced CO2 emission with GDP and obtained similar results. Our findings suggest a higher forest coverage may be a protective factor in low-income countries, which may be related to their low urbanization levels and high forest accessibilities. On the other hand, CO2 can be a surrogate of GDP, which may be a critical factor likely to decrease the COVID-19 CFR in lower-middle-income countries.
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Quispe Mamani JC, Flores Turpo GA, Calcina Álvarez DA, Yapuchura Saico CR, Velásquez Velásquez WL, Aguilar Pinto SL, Quispe Quispe B, Quispe Maquera NB, Cutipa Quilca BE. Gap and Inequality in the Economic Income of Independent Workers in the Region of Puno-Peru and the Effect of the Pandemic, 2019-2020. Front Sociol 2022; 7:858331. [PMID: 35495574 PMCID: PMC9043954 DOI: 10.3389/fsoc.2022.858331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
OBJECTIVE This article seeks to determine the social determinants of inequality in economic income in independent workers in the Puno region in the periods 2019 and 2020. METHODS For which the quantitative approach was used, with descriptive and correlational design, considering the multiple regression model. RESULTS It was determined that there is a very significant income gap by educational level due to the productive differential that coronavirus disease 2019 (COVID-19) affected all the households; there is inequality in the economic income of independent workers, since in 2019, there was a greater inequality of economic income among independent workers (Gini = 0.6142) in relation to the national level (Gini = 0.415) and in 2020, the inequality of economic income increased due to COVID-19 problem, where the Gini coefficient amounted to 0.7136 in relation to the national level (Gini = 0.431). CONCLUSION The determining factors of the economic income of the independent worker in the region of Puno in the periods 2019 and 2020 are the age that explains in 5.19 and 1.72%, the level of education that explains in 20.74 and 34.86% and the sex that explains in 37 and 14.19%, respectively.
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Affiliation(s)
| | | | | | | | | | | | - Betsy Quispe Quispe
- Facultad de Ciencias de la Salud, Escuela Profesional de Odontología, Universidad Nacional del Altiplano, Puno, Peru
| | - Nelly B. Quispe Maquera
- Facultad de Ciencias de la Salud, Escuela Profesional de Odontología, Universidad Nacional del Altiplano, Puno, Peru
| | - Balbina E. Cutipa Quilca
- Facultad de Ciencias Contables, Escuela Profesional de Ciencias Contables, Universidad Nacional del Altiplano, Puno, Peru
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20
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Yoo DS, Hwang M, Chun BC, Kim SJ, Son M, Seo NK, Ki M. Socioeconomic Inequalities in COVID-19 Incidence During Different Epidemic Phases in South Korea. Front Med (Lausanne) 2022; 9:840685. [PMID: 35345769 PMCID: PMC8957264 DOI: 10.3389/fmed.2022.840685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 01/31/2022] [Indexed: 12/02/2022] Open
Abstract
Objective Area-level socioeconomic status (SES) is associated with coronavirus disease 2019 (COVID-19) incidence. However, the underlying mechanism of the association is context-specific, and the choice of measure is still important. We aimed to evaluate the socioeconomic gradient regarding COVID-19 incidence in Korea based on several area-level SES measures. Methods COVID-19 incidence and area-level SES measures across 229 Korean municipalities were derived from various administrative regional data collected between 2015 and 2020. The Bayesian negative binomial model with a spatial autocorrelation term was used to estimate the incidence rate ratio (IRR) and relative index of inequality (RII) of each SES factor, with adjustment for covariates. The magnitude of association was compared between two epidemic phases: a low phase (<100 daily cases, from May 6 to August 14, 2020) and a rebound phase (>100 daily cases, from August 15 to December 31, 2020). Results Area-level socioeconomic inequalities in COVID-19 incidence between the most disadvantaged region and the least disadvantaged region were observed for nonemployment rates [RII = 1.40, 95% credible interval (Crl) = 1.01–1.95] and basic livelihood security recipients (RII = 2.66, 95% Crl = 1.12–5.97), but were not observed for other measures in the low phase. However, the magnitude of the inequalities of these SES variables diminished in the rebound phase. A higher area-level mobility showed a higher risk of COVID-19 incidence in both the low (IRR = 1.67, 95% Crl = 1.26–2.17) and rebound phases (IRR = 1.28, 95% Crl = 1.14–1.44). When SES and mobility measures were simultaneously adjusted, the association of SES with COVID-19 incidence remained significant but only in the low phase, indicating they were mutually independent in the low phase. Conclusion The level of basic livelihood benefit recipients and nonemployment rate showed social stratification of COVID-19 incidence in Korea. Explanation of area-level inequalities in COVID-19 incidence may not be derived only from mobility differences in Korea but, instead, from the country's own context.
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Affiliation(s)
- Dae-Sung Yoo
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, South Korea
| | - Minji Hwang
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea
| | - Byung Chul Chun
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea.,Department of Preventive Medicine, College of Medicine, Korea University, Seoul, South Korea
| | - Su Jin Kim
- Department of Emergency Medicine, College of Medicine, Korea University, Seoul, South Korea
| | - Mia Son
- Department of Preventive Medicine, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Nam-Kyu Seo
- Department of Non-Benefits Management, National Health Insurance Service/Health Insurance Policy Research Institute, Wonju, South Korea
| | - Myung Ki
- Department of Public Health, Korea University Graduate School, Seoul, South Korea.,BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea.,Department of Preventive Medicine, College of Medicine, Korea University, Seoul, South Korea
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21
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Raeesi A, Kiani B, Hesami A, Goshayeshi L, Firouraghi N, MohammadEbrahimi S, Hashtarkhani S. Access to the COVID-19 services during the pandemic - a scoping review. Geospat Health 2022; 17. [PMID: 35352541 DOI: 10.4081/gh.2022.1079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Appropriate accessibility to coronavirus disease 2019 (COVID-19) services is essential in the efficient management of the pandemic. Different geospatial methods and approaches have been used to measure accessibility to COVID-19 health-related services. This scoping review aimed to summarize and synthesize the geospatial studies conducted to measure accessibility to COVID-19 healthcare services. Web of Science, Scopus, and PubMed were searched to find relevant studies. From 1113 retrieved unique citations, 26 articles were selected to be reviewed. Most of the studies were conducted in the USA and floating catchment area methods were mostly used to measure the spatial accessibility to COVID-19 services including vaccination centres, Intensive Care Unit beds, hospitals and test sites. More attention is needed to measure the accessibility of COVID-19 services to different types of users especially with combining different non-spatial factors which could lead to better allocation of resources especially in populations with limited resources.
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Affiliation(s)
- Ahmad Raeesi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad.
| | - Behzad Kiani
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad.
| | - Azam Hesami
- Lab Solutions company Located at Science and Technology Park, Shahid Beheshti University, Tehran.
| | - Ladan Goshayeshi
- Surgical Oncology Research Center, Imam Reza Hospital, School of Medicine, Mashhad University of Medical Sciences, Mashhad; Department of Gastroenterology and Hepatology, School of Medicine, Mashhad University of Medical Sciences, Mashhad.
| | - Neda Firouraghi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad.
| | - Shahab MohammadEbrahimi
- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad.
| | - Soheil Hashtarkhani
- Department of Health Information Technology, Neyshabur University of Medical Sciences, Neyshabur.
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22
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Shankar PR, Nadarajah VD, Wilson IG. Implications of the ongoing coronavirus disease 2019 pandemic for primary care. Aust J Prim Health 2022; 28:200-203. [PMID: 35331366 DOI: 10.1071/py21096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022]
Abstract
The coronavirus disease 2019 pandemic has caused widespread global disruption. In this article, the authors put forward lessons from the pandemic for primary care. Among these are primary healthcare requires substantial investment; big data should be carefully regulated and used to strengthen primary care; primary care physicians can support media to provide impartial, objective information; protecting the health of vulnerable populations is important; and infectious diseases are still relevant today. Travel and tourism significantly impact health and primary care. Pandemics may be more common in the future due to climate change, increased human population and habitat loss, among other reasons. We should apply the lessons learned from the current pandemic to better prepare for future pandemics.
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Affiliation(s)
- Pathiyil Ravi Shankar
- IMU Centre for Education, International Medical University, Kuala Lumpur, Federal Territory of Kuala Lumpur 57000, Malaysia
| | - Vishna D Nadarajah
- Institutional Development and International, International Medical University, Kuala Lumpur, Federal Territory of Kuala Lumpur 57000, Malaysia
| | - Ian G Wilson
- IMU Centre for Education, International Medical University, Kuala Lumpur, Federal Territory of Kuala Lumpur 57000, Malaysia
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23
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Guo M, Yang L, Shen F, Zhang L, Li A, Cai Y, Zhou C. Impact of socio-economic environment and its interaction on the initial spread of COVID-19 in mainland China. Geospat Health 2022; 17. [PMID: 35735947 DOI: 10.4081/gh.2022.1060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 03/05/2022] [Indexed: 06/15/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has strongly impacted society since it was first reported in mainland China in December 2020. Understanding its spread and consequence is crucial to pandemic control, yet difficult to achieve because we deal with a complex context of social environment and variable human behaviour. However, few efforts have been made to comprehensively analyse the socio-economic influences on viral spread and how it promotes the infection numbers in a region. Here we investigated the effect of socio-economic factors and found a strong linear relationship between the gross domestic product (GDP) and the cumulative number of confirmed COVID-19 cases with a high value of R2 (between 0.57 and 0.88). Structural equation models were constructed to further analyse the social-economic interaction mechanism of the spread of COVID-19. The results show that the total effect of GDP (0.87) on viral spread exceeds that of population influx (0.58) in the central cities of mainland China and that the spread mainly occurred through its interplay with other factors, such as socio-economic development. This evidence can be generalized as socio-economic factors can accelerate the spread of any infectious disease in a megacity environment. Thus, the world is in urgent need of a new plan to prepare for current and future pandemics.
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Affiliation(s)
- Mao Guo
- School of Geography and Ocean Science, Nanjing University, Nanjing; Collaborative Innovation Centre of South China Sea Studies, Nanjing University.
| | - Lin Yang
- School of Geography and Ocean Science, Nanjing University, Nanjing.
| | - Feixue Shen
- School of Geography and Ocean Science, Nanjing University, Nanjing.
| | - Lei Zhang
- School of Geography and Ocean Science, Nanjing University, Nanjing.
| | - Anqi Li
- School of Geography and Ocean Science, Nanjing University, Nanjing.
| | - Yanyan Cai
- School of Geography and Ocean Science, Nanjing University, Nanjing.
| | - Chenghu Zhou
- School of Geography and Ocean Science, Nanjing University, Nanjing; Collaborative Innovation Centre of South China Sea Studies, Nanjing University; State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing.
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24
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Yu H, Lao X, Gu H, Zhao Z, He H. Understanding the Geography of COVID-19 Case Fatality Rates in China: A Spatial Autoregressive Probit-Log Linear Hurdle Analysis. Front Public Health 2022; 10:751768. [PMID: 35242729 PMCID: PMC8885593 DOI: 10.3389/fpubh.2022.751768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/10/2022] [Indexed: 12/23/2022] Open
Abstract
This study employs a spatial autoregressive probit-log linear (SAP-Log) hurdle model to investigate the influencing factors on the probability of death and case fatality rate (CFR) of coronavirus disease 2019 (COVID-19) at the city level in China. The results demonstrate that the probability of death from COVID-19 and the CFR level are 2 different processes with different determinants. The number of confirmed cases and the number of doctors are closely associated with the death probability and CFR, and there exist differences in the CFR and its determinants between cities within Hubei Province and outside Hubei Province. The spatial probit model also presents positive spatial autocorrelation in death probabilities. It is worth noting that the medical resource sharing among cities and enjoyment of free medical treatment services of citizens makes China different from other countries. This study contributes to the growing literature on determinants of CFR with COVID-19 and has significant practical implications.
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Affiliation(s)
- Hanchen Yu
- Center for Geographic Analysis, Harvard University, Cambridge, MA, United States
| | - Xin Lao
- School of Economics and Management, China University of Geosciences, Beijing, China
- *Correspondence: Xin Lao
| | - Hengyu Gu
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhihao Zhao
- School of Economics and Management, China University of Geosciences, Beijing, China
| | - Honghao He
- School of Software and Microelectronics, Peking University, Beijing, China
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25
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Siljander M, Uusitalo R, Pellikka P, Isosomppi S, Vapalahti O. Spatiotemporal clustering patterns and sociodemographic determinants of COVID-19 (SARS-CoV-2) infections in Helsinki, Finland. Spat Spatiotemporal Epidemiol 2022. [PMID: 35691637 PMCID: PMC8817446 DOI: 10.1016/j.sste.2022.100493] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 01/21/2022] [Accepted: 02/04/2022] [Indexed: 12/22/2022]
Abstract
This study aims to elucidate the variations in spatiotemporal patterns and sociodemographic determinants of SARS-CoV-2 infections in Helsinki, Finland. Global and local spatial autocorrelation were inspected with Moran's I and LISA statistics, and Getis-Ord Gi* statistics was used to identify the hot spot areas. Space-time statistics were used to detect clusters of high relative risk and regression models were implemented to explain sociodemographic determinants for the clusters. The findings revealed the presence of spatial autocorrelation and clustering of COVID-19 cases. High–high clusters and high relative risk areas emerged primarily in Helsinki's eastern neighborhoods, which are socioeconomically vulnerable, with a few exceptions revealing local outbreaks in other areas. The variation in COVID-19 rates was largely explained by median income and the number of foreign citizens in the population. Furthermore, the use of multiple spatiotemporal analysis methods are recommended to gain deeper insights into the complex spatiotemporal clustering patterns and sociodemographic determinants of the COVID-19 cases.
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26
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He Z, Lv Y, Zheng S, Pu Y, Lin Q, Zhou H, Dong M, Wang J, Fan J, Ye Y, Chen H, Qian R, Jin J, Chen Y, Chen G, He G, Cheng S, Hu J, Xiao J, Ma W, Su X, Liu T. Association of COVID-19 Lockdown With Gestational Diabetes Mellitus. Front Endocrinol (Lausanne) 2022; 13:824245. [PMID: 35432191 PMCID: PMC9005639 DOI: 10.3389/fendo.2022.824245] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 02/28/2022] [Indexed: 12/23/2022] Open
Abstract
IMPORTANCE The ongoing pandemic of COVID-19 is still affecting our life, but the effects of lockdown measures on gestational diabetes mellitus (GDM) in pregnant women remain unclear. AIM To investigate the association between COVID-19 lockdown and GDM. SUBJECTS AND METHODS Medical records of 140844 pregnant women during 2015-2020 were extracted from 5 hospitals in Guangdong Province, China. Pregnant women who underwent the COVID-19 Level I lockdown (1/23 - 2/24/2020) during pregnancy were defined as the exposed group (N=20472) and pregnant women who underwent the same calendar months during 2015-2019 (1/23 - 2/24) were defined as the unexposed group (N=120372). Subgroup analyses were used to explore the potential susceptible exposure window of COVID-19 lockdown on GDM. Cumulative exposure is quantitatively estimated by assigning different weights to response periods with different exposure intensities. A logistic regression model was used to estimate the association between COVID-19 lockdown exposure and GDM. RESULTS The rates of GDM in the exposed and unexposed groups were 15.2% and 12.4%, respectively. The overall analyses showed positive associations (odds ratio, OR=1.22, 95%CI: 1.17, 1.27) between lockdown exposure and GDM risk in all pregnant women. More pronounced associations were found in women who underwent the COVID-19 lockdown in their first four months of pregnancy, and the adjusted OR values ranged from 1.24 (95%CI: 1.10, 1.39) in women with 5-8 gestational weeks (GWs) to 1.35 (95%CI: 1.20, 1.52) with < 5 GWs. In addition, we found a positive exposure-response association of cumulative lockdown exposure with the risk of GDM. CONCLUSIONS The COVID-19 lockdown was associated with an increased risk of GDM, and the first four months of pregnancy may be the window for sensitive exposure.
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Affiliation(s)
- Zhongrong He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yanyun Lv
- Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, China
| | - Suijin Zheng
- The Affiliated Houjie Hospital, Guangdong Medical University, Dongguan, China
| | - Yudong Pu
- Central Laboratory, Songshan Lake Central Hospital of Dongguan City, Dongguan, China
| | - Qingmei Lin
- Foshan Women and Children Hospital Affiliated to Southern Medical University, Foshan, China
| | - He Zhou
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Moran Dong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jiaqi Wang
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jingjie Fan
- Department of Prevention and Health Care, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China
| | - Yufeng Ye
- Radiological Department, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Hanwei Chen
- Radiological Department, Guangzhou Panyu Central Hospital, Guangzhou, China
| | - Rui Qian
- Technology Department, Statistical Information Center for Health and Family Planning Bureau of Foshan, Foshan, China
| | - Juan Jin
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yumeng Chen
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Guimin Chen
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Guanhao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Shouzhen Cheng
- Nursing Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou, China
| | - Xi Su
- Foshan Women and Children Hospital Affiliated to Southern Medical University, Foshan, China
- *Correspondence: Tao Liu, ; Xi Su,
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, China
- Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou, China
- *Correspondence: Tao Liu, ; Xi Su,
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Qiu J, Li R, Han D, Shao Q, Han Y, Luo X, Wu Y. A multiplicity of environmental, economic and social factor analyses to understand COVID-19 diffusion. One Health 2021; 13:100335. [PMID: 34632042 PMCID: PMC8490135 DOI: 10.1016/j.onehlt.2021.100335] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/16/2021] [Accepted: 10/03/2021] [Indexed: 12/12/2022] Open
Abstract
Research on the impact of the environment on COVID-19 diffusion lacks a full-comprehensive perspective, and neglecting the multiplicity of the human-environment system can lead to misleading conclusions. We attempted to reveal all pre-existing environmental-to-human and human-to-human determinants that influence the transmission of COVID-19. As such, We estimated the daily case incidence ratios (CIR) of COVID-19 for prefectures across mainland China, and used a mixed-effects mixed-distribution model to study the association between the CIR and 114 factors related to climate, atmospheric environmental quality, terrain, population, economic, human mobility as well as non-pharmaceutical interventions (NPIs). Not only the changes in determinants over time as the pandemic progresses but also their lag and interaction effects were examined. CO, O3, PM10 and PM2.5 were found positively linked with CIR, but the effect of NO2 was negative. The temperature had no significant association with CIR, and the daily minimum humidity was a significant negatively predictor. NPIs' level was negatively associated with CIR until with a lag of 15 days. Higher accumulated destination migration scale flow from the epicenter and lower distance to the epicenter (DisWH) were associated with a higher CIR, however, the interaction between DisWH and the time was positive. The more economically developed and more densely populated cities have a higher probability of CIR occurrence, but they may not have a higher CIR intensity.The COVID-19 diffusion are caused by a multiplicity of environmental, economic, social factors as well as NPIs. First, multiple pollutants carried simultaneously on particulate matter affect COVID-19 transmission. Second, the temperature has a limited impact on the spread of the epidemic. Third, NPIs must last for at least 15 days or longer before the effect has been apparent. Fourth, the impact of population movement from the epicenter on COVID-19 gradually diminished over time and intraregional migration deserves more attention.
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Affiliation(s)
- Juan Qiu
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Rendong Li
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
| | - Dongfeng Han
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qihui Shao
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yifei Han
- Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiyue Luo
- Faculty of Resources and Environmental Science, Hubei University, Wuhan, China
| | - Yanlin Wu
- Faculty of Resources and Environmental Science, Hubei University, Wuhan, China
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28
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Dong M, Qian R, Wang J, Fan J, Ye Y, Zhou H, Win B, Reid E, Zheng S, Lv Y, Pu Y, Chen H, Jin J, Lin Q, Luo X, Chen G, Chen Y, He Z, He G, Cheng S, Hu J, Xiao J, Ma W, Liu T, Wen X. Associations of COVID-19 lockdown with gestational length and preterm birth in China. BMC Pregnancy Childbirth 2021; 21:795. [PMID: 34837991 PMCID: PMC8626761 DOI: 10.1186/s12884-021-04268-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 11/11/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The effects of COVID-19 lockdown measures on maternal and fetal health remain unclear. We examined the associations of COVID-19 lockdown with gestational length and preterm birth (PTB) in a Chinese population. METHODS We obtained medical records of 595,396 singleton live infants born between 2015 and 2020 in 5 cities in Guangdong Province, South China. The exposed group (N = 101,900) included women who experienced the COVID-19 Level I lockdown (1/23-2/24/2020) during pregnancy, while the unexposed group (N = 493,496) included women who were pregnant during the same calendar months in 2015-2019. Cumulative exposure was calculated based on days exposed to different levels of emergency responses with different weighting. Generalized linear regression models were applied to estimate the associations of lockdown exposure with gestational length and risk of PTB (< 37 weeks). RESULTS The exposed group had a shorter mean gestational length than the unexposed group (38.66 vs 38.74 weeks: adjusted β = - 0.06 week [95%CI, - 0.07, - 0.05 week]). The exposed group also had a higher risk of PTB (5.7% vs 5.3%; adjusted OR = 1.08 [95%CI, 1.05, 1.11]). These associations seemed to be stronger when exposure occurred before or during the 23rd gestational week (GW) than during or after the 24th GW. Similarly, higher cumulative lockdown exposure was associated with a shorter gestational length and a higher risk of PTB. CONCLUSIONS The COVID-19 lockdown measures were associated with a slightly shorter gestational length and a moderately higher risk of PTB. Early and middle pregnancy periods may be a more susceptible exposure window.
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Affiliation(s)
- Moran Dong
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
- School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Rui Qian
- Statistical Information Center for Health and Family Planning Bureau of Foshan, Foshan, 528000, China
| | - Jiaqi Wang
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510080, China
| | - Jingjie Fan
- Department of Prevention and Health Care, Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen, 518028, China
| | - Yufeng Ye
- Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - He Zhou
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510080, China
| | - Brian Win
- Division of Behavioral Medicine, Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - Eve Reid
- Division of Behavioral Medicine, Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA
| | - Suijin Zheng
- The Affiliated Houjie Hospital, Guangdong Medical University, Dongguan, 523945, China
| | - Yanyun Lv
- Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, 529030, China
| | - Yudong Pu
- Songshan Lake Central Hospital of Dongguan City, Dongguan, 523808, China
| | - Hanwei Chen
- Guangzhou Panyu Central Hospital, Guangzhou, 511400, China
| | - Juan Jin
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510080, China
| | - Qingmei Lin
- Foshan Women and Children Hospital Affiliated to Southern Medical University, Foshan, 528000, China
| | - Xiaoyang Luo
- Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangzhou, 510000, China
| | - Guimin Chen
- School of Public Health, Southern Medical University, Guangzhou, 510515, China
| | - Yumeng Chen
- School of Public Health, Guangdong Pharmaceutical University, Guangzhou, 510080, China
| | - Zhongrong He
- School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Guanhao He
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Shouzhen Cheng
- Nursing Department, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China
| | - Jianxiong Hu
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Jianpeng Xiao
- Guangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, 511430, China
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 West, Huangpu Road, Tianhe District, Guangzhou, 510632, China
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, No.601 West, Huangpu Road, Tianhe District, Guangzhou, 510632, China.
| | - Xiaozhong Wen
- Division of Behavioral Medicine, Department of Pediatrics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, 14214, USA
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Samany NN, Toomanian A, Maher A, Hanani K, Zali AR. The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city. Land use policy 2021; 109:105725. [PMID: 34483431 PMCID: PMC8403664 DOI: 10.1016/j.landusepol.2021.105725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 08/16/2021] [Accepted: 08/26/2021] [Indexed: 05/09/2023]
Abstract
Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.
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Affiliation(s)
| | - Ara Toomanian
- Department of GIS & RS, Faculty of Geography, University of Tehran, Iran
| | - Ali Maher
- School of Management and Medical Education, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Khatereh Hanani
- Master of Statistics, Statistics & Information Technology Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ali Reza Zali
- Department of Neurosurgery, School of Medicine, Functional Neurosurgery Research Center Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Geetha S, Narayanamoorthy S, Manirathinam T, Kang D. Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period. Expert Syst Appl 2021; 178:114997. [PMID: 33846668 PMCID: PMC8028601 DOI: 10.1016/j.eswa.2021.114997] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 02/19/2021] [Accepted: 04/02/2021] [Indexed: 05/03/2023]
Abstract
In this research article, we introduced an algorithm to evaluate COVID-19 patients admission in hospitals at source shortage period. Many researchers have expressed their conclusions from different perspectives on various factors such as spatial changes, climate risks, preparedness, blood type, age and comorbidities that may be contributing to COVID-19 mortality rate. However, as the number of people coming to the hospital for COVID-19 treatment increases, the mortality rate is likely to increase due to the lack of medical facilities. In order to provide medical assistance in this situation, we need to consider not only the extent of the disease impact, but also other important factors. No method has yet been proposed to calculate the priority of patients taking into account all the factors. We have provided a solution to this in this research article. Based on eight key factors, we provide a way to determine priorities. In order to achieve the effectiveness and practicability of the proposed method, we studied individuals with different results on all factors. The sigmoid function helps to easily construct factors at different levels. In addition, the cobweb solution model allows us to see the potential of our proposed algorithm very clearly. Using the method we introduced, it is easier to sort high-risk individuals to low-risk individuals. This will make it easier to deal with problems that arise when the number of patients in hospitals continues to increase. It can reduce the mortality of COVID-19 patients. Medical professionals can be very helpful in making the best decisions.
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Affiliation(s)
- Selvaraj Geetha
- Department of Mathematics, Bharathiar University, Coimbatore 641046, TamilNadu, India
| | | | | | - Daekook Kang
- Department of Industrial and Management Engineering, Institute of Digital Anti-aging Health care, Inje University, 197, Inje-ro, Gimhae-si, Gyeongsangnam-do, Republic of Korea
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31
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Dhewantara PW, Puspita T, Marina R, Lasut D, Riandi MU, Wahono T, Ridwan W, Ruliansyah A. Geo-clusters and socio-demographic profiles at village-level associated with COVID-19 incidence in the metropolitan city of Jakarta: An ecological study. Transbound Emerg Dis 2021; 69:e362-e373. [PMID: 34486234 PMCID: PMC8661770 DOI: 10.1111/tbed.14313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 08/25/2021] [Accepted: 09/03/2021] [Indexed: 11/30/2022]
Abstract
The Special Capital Region of Jakarta is the epicentre of the transmission of COVID‐19 in Indonesia. However, much remains unknown about the spatial and temporal patterns of COVID‐19 incidence and related socio‐demographic factors explaining the variations of COVID‐19 incidence at local level. COVID‐19 cases at the village level of Jakarta from March 2020 to June 2021 were analyzed from the local public COVID‐19 dashboard. Global and local spatial clustering of COVID‐19 incidence was examined using the Moran's I and local Moran analysis. Socio‐demographic profiles of identified hotspots were elaborated. The association between village characteristics and COVID‐19 incidence was evaluated. The COVID‐19 incidence was significantly clustered based on the geographical village level (Moran's I = 0.174; p = .002). Seventeen COVID‐19 high‐risk clusters were found and dynamically shifted over the study period. The proportion of people aged 20–49 (incidence rate ratio [IRR] = 1.016; 95% confidence interval [CI]: 1.012–1.019), proportion of elderly (≥50 years) (IRR = 1.045; 95% CI = 1.041–1.050), number of households (IRR = 1.196; 95% CI = 1.193–1.200), access to metered water for washing, and the main occupation of the residents were village level socio‐demographic factors associated with the risk of COVID‐19. Targeted public health responses such as restriction, improved testing and contact tracing, and improved access to health services for those vulnerable populations are essential in areas with high‐risk COVID‐19.
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Affiliation(s)
- Pandji Wibawa Dhewantara
- Centre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia
| | - Tities Puspita
- Centre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia
| | - Rina Marina
- Centre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia
| | - Doni Lasut
- Centre for Research and Development of Public Health Efforts, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, Jakarta, Indonesia
| | - Muhammad Umar Riandi
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia
| | - Tri Wahono
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia
| | - Wawan Ridwan
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia
| | - Andri Ruliansyah
- Pangandaran Unit for Health Research and Development, National Institute of Health Research and Development (NIHRD), Indonesian Ministry of Health, West Java, Indonesia
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32
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Benita F, Gasca-Sanchez F. The main factors influencing COVID-19 spread and deaths in Mexico: A comparison between phases I and II. Appl Geogr 2021; 134:102523. [PMID: 34334843 PMCID: PMC8313543 DOI: 10.1016/j.apgeog.2021.102523] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 07/11/2021] [Accepted: 07/23/2021] [Indexed: 05/05/2023]
Abstract
This article investigates the geographical spread of confirmed COVID-19 cases and deaths across municipalities in Mexico. It focuses on the spread dynamics and containment of the virus between Phase I (from March 23 to May 31, 2020) and Phase II (from June 1 to August 22, 2020) of the social distancing measures. It also examines municipal-level factors associated with cumulative COVID-19 cases and deaths to understand the spatial determinants of the pandemic. The analysis of the geographic pattern of the pandemic via spatial scan statistics revealed a fast spread among municipalities. During Phase I, clusters of infections and deaths were mainly located at the country's center, whereas in Phase II, these clusters dispersed to the rest of the country. The regression results from the zero-inflated negative binomial regression analysis suggested that income inequality, the prevalence of obesity and diabetes, and concentration of fine particulate matter (PM 2.5) are strongly positively associated with confirmed cases and deaths regardless of lockdown.
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Affiliation(s)
- Francisco Benita
- Engineering Systems and Design, Singapore University of Technology and Design, Singapore
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33
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Romero Starke K, Mauer R, Karskens E, Pretzsch A, Reissig D, Nienhaus A, Seidler AL, Seidler A. The Effect of Ambient Environmental Conditions on COVID-19 Mortality: A Systematic Review. Int J Environ Res Public Health 2021; 18:ijerph18126665. [PMID: 34205714 PMCID: PMC8296503 DOI: 10.3390/ijerph18126665] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/18/2021] [Accepted: 06/19/2021] [Indexed: 02/06/2023]
Abstract
Weather conditions may have an impact on SARS-CoV-2 virus transmission, as has been shown for seasonal influenza. Virus transmission most likely favors low temperature and low humidity conditions. This systematic review aimed to collect evidence on the impact of temperature and humidity on COVID-19 mortality. This review was registered with PROSPERO (registration no. CRD42020196055). We searched the Pubmed, Embase, and Cochrane COVID-19 databases for observational epidemiological studies. Two independent reviewers screened the title/abstracts and full texts of the studies. Two reviewers also performed data extraction and quality assessment. From 5051 identified studies, 11 were included in the review. Although the results were inconsistent, most studies imply that a decrease in temperature and humidity contributes to an increase in mortality. To establish the association with greater certainty, future studies should consider accurate exposure measurements and important covariates, such as government lockdowns and population density, sufficient lag times, and non-linear associations.
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Affiliation(s)
- Karla Romero Starke
- Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (E.K.); (A.P.); (D.R.); (A.S.)
- Institute of Sociology, Faculty of Behavioral and Social Sciences, Chemnitz University of Technology, Thüringer Weg 9, 09126 Chemnitz, Germany
- Correspondence:
| | - René Mauer
- Institute for Medical Informatics and Biometry (IMB), Faculty of Medicine Carl Gustav Carus, Technische Universität, 01307 Dresden, Germany;
| | - Ethel Karskens
- Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (E.K.); (A.P.); (D.R.); (A.S.)
| | - Anna Pretzsch
- Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (E.K.); (A.P.); (D.R.); (A.S.)
| | - David Reissig
- Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (E.K.); (A.P.); (D.R.); (A.S.)
| | - Albert Nienhaus
- Department of Occupational Medicine, Toxic Substances and Health Research, Institution for Statutory Social Accident Insurance and Prevention in the Health Care and Welfare Services (BGW), 22089 Hamburg, Germany;
- Competence Centre for Epidemiology and Health Services Research for Healthcare Professionals (CVcare), Institute for Health Service Research in Dermatology and Nursing (IVDP), University Medical Centre Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany
| | - Anna Lene Seidler
- NHMRC Clinical Trials Centre, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2006, Australia;
| | - Andreas Seidler
- Institute and Policlinic of Occupational and Social Medicine (IPAS), Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany; (E.K.); (A.P.); (D.R.); (A.S.)
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Zhang J, Wu X, Chow TE. Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties. Int J Environ Res Public Health 2021; 18:5541. [PMID: 34067291 DOI: 10.3390/ijerph18115541] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/28/2021] [Accepted: 05/20/2021] [Indexed: 01/30/2023]
Abstract
As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact's indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).
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Abstract
Factors underlying neighborhood variation in COVID-19 mortality are important to assess in order to prioritize resourcing and policy intervention. As well as characteristics of area populations, such as health status and ethnic mix, it is important to assess the role of more specifically environmental variables (e.g., air quality, green space access). The analysis of this study focuses on neighborhood mortality variations during the first wave of the COVID-19 epidemic in England against a range of postulated area risk factors, both socio-demographic and environmental. We assess mortality gradients across levels of each risk factor and use regression methods to control for multicollinearity and spatially correlated unobserved risks. An analysis of spatial clustering is based on relative mortality risks estimated from the regression. We find mortality gradients in most risk factors showing appreciable differences in COVID mortality risk between English neighborhoods. A regression analysis shows that after allowing for health deprivation, ethnic mix, and ethnic segregation, environment (especially air quality) is an important influence on COVID mortality. Hence, environmental influences on COVID mortality risk in the UK first wave are substantial, after allowing for socio-demographic factors. Spatial clustering of high mortality shows a pronounced metropolitan-rural contrast, reflecting especially ethnic composition and air quality.
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