{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T18:21:38Z","timestamp":1764872498118,"version":"3.41.2"},"reference-count":20,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T00:00:00Z","timestamp":1621036800000},"content-version":"vor","delay-in-days":134,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH-NCI","award":["P30CA006973"],"award-info":[{"award-number":["P30CA006973"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Since the beginning of the coronavirus disease-2019 (COVID-19) pandemic in 2020, there has been a tremendous accumulation of data capturing different statistics including the number of tests, confirmed cases and deaths. This data wealth offers a great opportunity for researchers to model the effect of certain variables on COVID-19 morbidity and mortality and to get a better understanding of the disease at the epidemiological level. However, in order to draw any reliable and unbiased estimate, models also need to take into account other variables and metrics available from a plurality of official and unofficial heterogenous resources. In this study, we introduce covid19census, an R package that extracts from many different repositories and combines together COVID-19 metrics and other demographic, environment- and health-related variables of the USA and Italy at the county and regional levels, respectively. The package is equipped with a number of user-friendly functions that dynamically extract the data over different timepoints and contains a detailed description of the included variables. To demonstrate the utility of this tool, we used it to extract and combine different county-level data from the USA, which we subsequently used to model the effect of diabetes on COVID-19 mortality at the county level, taking into account other variables that may influence such effects. In conclusion, it was observed that the \u2018covid19census\u2019 package allows to easily extract area-level data from both the USA and Italy using few functions. These comprehensive data can be used to provide reliable estimates of the effect of certain variables on COVID-19 outcomes.<\/jats:p><jats:p>Database URL: https:\/\/github.com\/c1au6i0\/covid19census<\/jats:p>","DOI":"10.1093\/database\/baab027","type":"journal-article","created":{"date-parts":[[2021,5,4]],"date-time":"2021-05-04T11:17:05Z","timestamp":1620127025000},"source":"Crossref","is-referenced-by-count":3,"title":["covid19census: U.S. and Italy COVID-19 metrics and other epidemiological data"],"prefix":"10.1093","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5043-8033","authenticated-orcid":false,"given":"Claudio","family":"Zanettini","sequence":"first","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8068-1920","authenticated-orcid":false,"given":"Mohamed","family":"Omar","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA"}]},{"given":"Wikum","family":"Dinalankara","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA"}]},{"given":"Eddie Luidy","family":"Imada","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA"}]},{"given":"Elizabeth","family":"Colantuoni","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8783-5961","authenticated-orcid":false,"given":"Giovanni","family":"Parmigiani","sequence":"additional","affiliation":[{"name":"Department of Data Sciences, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA 02215, USA"},{"name":"Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, USA"}]},{"given":"Luigi","family":"Marchionni","sequence":"additional","affiliation":[{"name":"Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,5,15]]},"reference":[{"volume-title":"COVID Data Tracker","year":"2020","author":"CDC","key":"2021070818441941900_R1"},{"key":"2021070818441941900_R2","article-title":"Coronavirus (Covid-19) data in the United States","volume-title":"The New York Times","author":"The New York Times","year":"2021"},{"key":"2021070818441941900_R3","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/S1473-3099(20)30120-1","article-title":"An interactive web-based dashboard to track COVID-19 in real time","volume":"20","author":"Dong","year":"2020","journal-title":"Lancet Infect. Dis."},{"author":"The COVID Tracking Project","key":"2021070818441941900_R4"},{"key":"2021070818441941900_R5","doi-asserted-by":"crossref","DOI":"10.21105\/joss.02376","article-title":"COVID-19 data hub","volume":"5","author":"Guidotti","year":"2020","journal-title":"J. Open Source Softw."},{"volume-title":"COVID-19 Italian Dashboard","year":"2021","author":"Presidenza del Consiglio dei Ministri and Protezione Civile","key":"2021070818441941900_R6"},{"key":"2021070818441941900_R7","doi-asserted-by":"crossref","DOI":"10.32614\/CRAN.package.covid19italy","article-title":"covid19italy: the 2019 novel coronavirus COVID-19 (2019-nCoV) Italy dataset","author":"Krispin","year":"2020"},{"article-title":"coronavirus: the 2019 novel coronavirus COVID-19 (2019-nCoV) dataset","year":"2021","author":"Krispin","key":"2021070818441941900_R8"},{"article-title":"covid19nytimes: pulls the Covid-19 data from the New York Times public data source","year":"2021","author":"Byrnes","key":"2021070818441941900_R9"},{"key":"2021070818441941900_R10","first-page":"257","article-title":"Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe","volume-title":"Nature","author":"Flaxman","year":"2020"},{"key":"2021070818441941900_R11","doi-asserted-by":"crossref","first-page":"e261","DOI":"10.1016\/S2468-2667(20)30073-6","article-title":"The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study","volume":"5","author":"Prem","year":"2020","journal-title":"Lancet Public Health"},{"key":"2021070818441941900_R12","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1126\/science.abb5793","article-title":"Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period","volume":"368","author":"Kissler","year":"2020","journal-title":"Science"},{"key":"2021070818441941900_R13","article-title":"Correlation between universal BCG vaccination policy and reduced morbidity and mortality for COVID-19: an epidemiological study","author":"Miller","year":"2020","journal-title":"medRxiv"},{"key":"2021070818441941900_R14","article-title":"Differential COVID-19-attributable mortality and BCG vaccine use in countries","author":"Shet","year":"2020","journal-title":"medRxiv"},{"key":"2021070818441941900_R15","article-title":"Does TB vaccination reduce COVID-19 infection? No evidence from a regression discontinuity analysis","author":"Fukui","year":"2020","journal-title":"medRxiv"},{"key":"2021070818441941900_R16","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/S2213-8587(20)30272-2","article-title":"Associations of type 1 and type 2 diabetes with COVID-19-related mortality in England: a whole-population study","volume":"8","author":"Barron","year":"2020","journal-title":"Lancet Diabetes Endocrinol."},{"key":"2021070818441941900_R17","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1038\/s41586-020-2521-4","article-title":"Factors associated with COVID-19-related death using OpenSAFELY","volume":"584","author":"Williamson","year":"2020","journal-title":"Nature"},{"key":"2021070818441941900_R18","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1080\/00273171.2011.568786","article-title":"An introduction to propensity score methods for reducing the effects of confounding in observational studies","volume":"46","author":"Austin","year":"2011","journal-title":"Multivar. Behav. 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