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      COVID-19 among immigrants in Norway, notified infections, related hospitalizations and associated mortality: A register-based study

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          Abstract

          Aim: Research concerning COVID-19 among immigrants is limited. We present epidemiological data for all notified cases of COVID-19 among the 17 largest immigrant groups in Norway, and related hospitalizations and mortality. Methods: We used data on all notified COVID-19 cases in Norway up to 18 October 2020, and associated hospitalizations and mortality, from the emergency preparedness register (including Norwegian Surveillance System for Communicable Diseases) set up by The Norwegian Institute of Public Health to handle the pandemic. We report numbers and rates per 100,000 people for notified COVID-19 cases, and related hospitalizations and mortality in the 17 largest immigrant groups in Norway, crude and with age adjustment. Results: The notification, hospitalization and mortality rates per 100,000 were 251, 21 and five, respectively, for non-immigrants; 567, 62 and four among immigrants; 408, 27 and two, respectively, for immigrants from Europe, North-America and Oceania; and 773, 106 and six, respectively for immigrants from Africa, Asia and South America. The notification rate was highest among immigrants from Somalia (2057), Pakistan (1868) and Iraq (1616). Differences between immigrants and non-immigrants increased when adjusting for age, especially for mortality. Immigrants had a high number of hospitalizations relative to notified cases compared to non-immigrants. Although the overall COVID-19 notification rate was higher in Oslo than outside of Oslo, the notification rate among immigrants compared to non-immigrants was not higher in Oslo than outside. Conclusions: We observed a higher COVID-19 notification rate in immigrants compared to non-immigrants and much higher hospitalization rate, with major differences between different immigrant groups. Somali-, Pakistani- and Iraqi-born immigrants had especially high rates.

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          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            Risk factors for SARS-CoV-2 among patients in the Oxford Royal College of General Practitioners Research and Surveillance Centre primary care network: a cross-sectional study

            Summary Background There are few primary care studies of the COVID-19 pandemic. We aimed to identify demographic and clinical risk factors for testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within the Oxford Royal College of General Practitioners (RCGP) Research and Surveillance Centre primary care network. Methods We analysed routinely collected, pseudonymised data for patients in the RCGP Research and Surveillance Centre primary care sentinel network who were tested for SARS-CoV-2 between Jan 28 and April 4, 2020. We used multivariable logistic regression models with multiple imputation to identify risk factors for positive SARS-CoV-2 tests within this surveillance network. Findings We identified 3802 SARS-CoV-2 test results, of which 587 were positive. In multivariable analysis, male sex was independently associated with testing positive for SARS-CoV-2 (296 [18·4%] of 1612 men vs 291 [13·3%] of 2190 women; adjusted odds ratio [OR] 1·55, 95% CI 1·27–1·89). Adults were at increased risk of testing positive for SARS-CoV-2 compared with children, and people aged 40–64 years were at greatest risk in the multivariable model (243 [18·5%] of 1316 adults aged 40–64 years vs 23 [4·6%] of 499 children; adjusted OR 5·36, 95% CI 3·28–8·76). Compared with white people, the adjusted odds of a positive test were greater in black people (388 [15·5%] of 2497 white people vs 36 [62·1%] of 58 black people; adjusted OR 4·75, 95% CI 2·65–8·51). People living in urban areas versus rural areas (476 [26·2%] of 1816 in urban areas vs 111 [5·6%] of 1986 in rural areas; adjusted OR 4·59, 95% CI 3·57–5·90) and in more deprived areas (197 [29·5%] of 668 in most deprived vs 143 [7·7%] of 1855 in least deprived; adjusted OR 2·03, 95% CI 1·51–2·71) were more likely to test positive. People with chronic kidney disease were more likely to test positive in the adjusted analysis (68 [32·9%] of 207 with chronic kidney disease vs 519 [14·4%] of 3595 without; adjusted OR 1·91, 95% CI 1·31–2·78), but there was no significant association with other chronic conditions in that analysis. We found increased odds of a positive test among people who are obese (142 [20·9%] of 680 people with obesity vs 171 [13·2%] of 1296 normal-weight people; adjusted OR 1·41, 95% CI 1·04–1·91). Notably, active smoking was linked with decreased odds of a positive test result (47 [11·4%] of 413 active smokers vs 201 [17·9%] of 1125 non-smokers; adjusted OR 0·49, 95% CI 0·34–0·71). Interpretation A positive SARS-CoV-2 test result in this primary care cohort was associated with similar risk factors as observed for severe outcomes of COVID-19 in hospital settings, except for smoking. We provide evidence of potential sociodemographic factors associated with a positive test, including deprivation, population density, ethnicity, and chronic kidney disease. Funding Wellcome Trust.
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              GIS-based spatial modeling of COVID-19 incidence rate in the continental United States

              During the first 90 days of the COVID-19 outbreak in the United States, over 675,000 confirmed cases of the disease have been announced, posing unprecedented socioeconomic burden to the country. Due to inadequate research on geographic modeling of COVID-19, we investigated county-level variations of disease incidence across the continental United States. We compiled a geodatabase of 35 environmental, socioeconomic, topographic, and demographic variables that could explain the spatial variability of disease incidence. Further, we employed spatial lag and spatial error models to investigate spatial dependence and geographically weighted regression (GWR) and multiscale GWR (MGWR) models to locally examine spatial non-stationarity. The results suggested that even though incorporating spatial autocorrelation could significantly improve the performance of the global ordinary least square model; these models still represent a significantly poor performance compared to the local models. Moreover, MGWR could explain the highest variations (adj. R2: 68.1%) with the lowest AICc compared to the others. Mapping the effects of significant explanatory variables (i.e., income inequality, median household income, the proportion of black females, and the proportion of nurse practitioners) on spatial variability of COVID-19 incidence rates using MGWR could provide useful insights to policymakers for targeted interventions.
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                Author and article information

                Journal
                Scand J Public Health
                Scand J Public Health
                SJP
                spsjp
                Scandinavian Journal of Public Health
                SAGE Publications (Sage UK: London, England )
                1403-4948
                1651-1905
                7 January 2021
                February 2021
                : 49
                : 1 , Special Issue: The social, economic and health-related consequences of COVID-19 – Part I
                : 48-56
                Affiliations
                [1 ]Norwegian Institute of Public Health, Health Services Research, Oslo, Norway
                [2 ]Division of Infection Control and Environmental Health, Norwegian Institute of Public Health, Oslo, Norway
                Author notes
                [*]Thor Indseth, Norwegian Institute of Public Health, Postboks 222 Skøyen, 0213 Oslo, Norway. E-mail: thor.indseth@ 123456fhi.no
                Author information
                https://orcid.org/0000-0002-2727-332X
                Article
                10.1177_1403494820984026
                10.1177/1403494820984026
                7859570
                33406993
                0a6fc40d-2e3d-474c-b6a8-bb530b4f6dd2
                © Author(s) 2021

                This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 28 August 2020
                : 30 November 2020
                : 6 December 2020
                Categories
                Empirical Articles
                Custom metadata
                ts1

                Public health
                covid-19,immigrants,migrants,hospitalization,mortality
                Public health
                covid-19, immigrants, migrants, hospitalization, mortality

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