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5 results for “Covid-19 hospital incidence”
Human contact network analytics and COVID-19 hospital incidence in France
<p>This data set contains COVID-19 hospital incidence, temperature and human mobility and contact data recorded between 2020-03-24 and 2021-03-30 used in the paper:</p> <p>Selinger et al. 2021: Predicting COVID-19 incidence in French hospitals using human contact network analytics. 10.1016/j.ijid.2021.08.029</p> <p>See methods in the article for detailed descriptions and the data curation process.</p> <p> </p> <p><strong>1) cov_mob_tst_national.csv contains national-level data</strong><br> </p> <p>The columns comprise:</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>within_departement_colocation_X%: X%-quantile of colocation probabilities with départements</p> <p>between_departement_colocation_X%: X%-quantile of colocation probabilities between départements</p> <p>fb_population_coverage_X%: X%-quantile of ratio of fb_population over census population in département</p> <p>null_links_X%: X%-quantile of null links across départements</p> <p>clustering_X%: X%-quantile of clustering coefficients across départements</p> <p>ricci_X%: X%-quantile of curvature across départements</p> <p>ricci_min_X%: X%-quantile of minimum curvature across départements</p> <p>ricci_mean_X%: X%-quantile of average curvature across départements</p> <p>ricci_max_X%: X%-quantile of maximum curvature across départements</p> <p>strength_X%: X%-quantile of network strengths across départements</p> <p>betweenness_centrality_X%: X%-quantile of betweenness_centrality scores across départements</p> <p>positive_test_ratio_weekly: ratio of weekly cumulated positive tested over weekly cumulated tests</p> <p>retail_and_recreation_percent_change_from_baseline: Google Mobility Reports</p> <p>grocery_and_pharmacy_percent_change_from_baseline: Google Mobility Reports</p> <p>parks_percent_change_from_baseline: Google Mobility Reports </p> <p>transit_stations_percent_change_from_baseline: Google Mobility Reports</p> <p>workplaces_percent_change_from_baseline: Google Mobility Reports</p> <p>residential_percent_change_from_baseline: Google Mobility Reports</p> <p>mean_temperature_X%: X% quantile of mean daily temperatures averaged over the week across départements</p> <p>min_temperature_X%: X% quantile of minimum daily temperatures averaged over the week across départements</p> <p>max_temperature_X%: X% quantile of maximum daily temperatures averaged over the week across départements</p> <p> </p> <p> </p> <p><strong>2) cov_mob_dep.csv contains département-level data</strong></p> <p>The columns comprise:</p> <p>dep: département code</p> <p>incid_hosp: hospital admission incidence </p> <p>incid_rea: ICU admission incidence</p> <p>incid_dc: hospital death incidence </p> <p>incid_rad: incidence of those returned home</p> <p>week: week (matched to colocation data recording usually on Tuesdays)</p> <p>dep_name: name of the département</p> <p>null_links: number of null links</p> <p>betweenness_centrality: betweenness centrality</p> <p>clustering: clustering coefficient</p> <p>strength: network strength</p> <p>ricci_mean: minimum curvature among all edges incident to a département</p> <p>ricci_min: mean curvature across all edges incident to a département </p> <p>ricci_X%: X%-quantile curvature among all edges incident to a département</p> <p>fb_population: number of facebook users </p> <p>facebook_colocation_within_dep: colocation probability within département</p> <p>fb_population_coverage: ratio of fb_population over census population in département</p> <p>facebook_colocation_between_dep_X%: X%-quantile of facebook colocation among all edges incident to the département</p> <p>min_temperature: minimum daily temperature averaged over the week</p> <p>max_temperature: maximum daily temperature averaged over the week</p> <p>mean_temperature: mean daily temperature averaged over the week</p> <p>incid_hosp_Y: incidence of hospital admission from Ynd most colocated département</p> <p>incid_rea_Y: incidence of ICU admission from Ynd most colocated département</p> <p>incid_dc_Y: incidence of hospital deaths from Ynd most colocated département</p> <p>incid_rad_Y: incidence of returned home from Ynd most colocated département</p> <p> </p>
Raw data and SPSS analysis for the article Bacterial and fungal co-infections among ICU COVID-19 hospitalized patients in a Palestinian hospital: Incidence and antimicrobial stewardship
<p>The attached data is related to a study with proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single-center cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine. The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥20 years,</p> <p><b>Background:</b> Diagnosis of co-infections with multiple pathogens among hospitalized COVID-19 patients can be jointly challenging and very essential for appropriate treatment, shortening hospital stay and preventing antimicrobial resistance. This study proposes to investigate the burden of bacterial and fungal co-infections outcomes on COVID-19 patients. It is a single center cross-sectional study of hospitalized COVID-19 patients at Beit-Jala hospital in Palestine.</p> <p><b>Methods: </b>The study included 321 hospitalized patients admitted to the ICU between June 2020 and March 2021 aged ≥20 years, with a confirmed diagnosis of COVID-19 via RT-PCR conducted on a nasopharyngeal swab. The patient's information was gathered using graded data forms from electronic medical reports.</p> <p><b>Results:</b> The diagnosis of bacterial and fungal infection was proved through the patient`s clinical presentation and positive blood or sputum culture results. All cases had received empirical antimicrobial therapy before the ICU admission, and different regimens during the ICU stay. The rate of bacterial co-infection was 51.1%, mainly from gram-negative isolates (Enterobacter species and K.pneumoniae). The rate of fungal co-infection caused by A.fumigatus was 48.9%, and the mortality rate was 8.1%. However, it is unclear if it had been attributed to SARS-CoV-2 or coincidental.</p>
Raw data and SPSS analysis for the article Bacterial and fungal co-infections among ICU COVID-19 hospitalized patients in a Palestinian hospital: Incidence and antimicrobial stewardship
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Incidence of Deep Vein Thrombosis at Doppler Echo in Covid-19 Patients With SARS-Cov-2 Pneumopathy Hospitalized in ICU
ClinicalTrials.gov study NCT04363528. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Seroprevalence of SARS-Cov-2 in the Setting of a Non-dedicated COVID-19 Hospital in a Low CoV-2 Incidence Area: Implications for Surgery
ClinicalTrials.gov study NCT04480580. IPD Sharing: NO. Countries: 1. Publications: 0.
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