Crude incidence and prevalence of MS in Hungary between 2010 and 2015.
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xlsUpdated Jun 4, 2023
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EmailClick to copy linkLink copiedCiteAnna Iljicsov; Dániel Milanovich; András Ajtay; Ferenc Oberfrank; Mónika Bálint; Balázs Dobi; Dániel Bereczki; Magdolna Simó (2023). Crude incidence and prevalence of MS in Hungary between 2010 and 2015. [Dataset]. http://doi.org/10.1371/journal.pone.0236432.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0236432.t002Dataset updatedJun 4, 2023AuthorsAnna Iljicsov; Dániel Milanovich; András Ajtay; Ferenc Oberfrank; Mónika Bálint; Balázs Dobi; Dániel Bereczki; Magdolna SimóLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredHungaryDescriptionCrude incidence and prevalence of MS in Hungary between 2010 and 2015.
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500 Cities: Obesity
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Updated Aug 1, 2018ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJHU_CLF (2018). 500 Cities: Obesity [Dataset]. https://arc-gis-hub-home-arcgishub.hub.arcgis.com/datasets/fe1a6795c36c4b3c988b8f4980171af9Dataset updatedAug 1, 2018Dataset authored and provided byJHU_CLFArea coveredDescriptionThe crude prevalence rate of obesity is defined as the ratio of respondents that are 18 years or older who have a body mass index (BMI) of 30.0 kg/m2 or greater over the total number of respondents in the study (excluding those who refused to answer or those whose information was unknown”). Respondents were excluded if their height was less than 3 ft or equal to/greater than 8 ft; they weighed less than 50 lbs or more than 650 lbs; they had a BMI of less than 12 kg/m2 or 100 kg/m2 and greater; and/or were pregnant.Prevalence data are derived from the Behavioral Risk Factor Surveillance System (BRFSS) 2012.The 500 Cities Project seeks to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States.Data source: CDC (Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion)Date: 2015
Self-reported and EHR-documented COVID-19 prevalence by sociodemographic and...
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xlsUpdated Jun 4, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. Croen (2023). Self-reported and EHR-documented COVID-19 prevalence by sociodemographic and clinical factors, crude and adjusted prevalence ratios. [Dataset]. http://doi.org/10.1371/journal.pone.0256891.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0256891.t002Dataset updatedJun 4, 2023AuthorsJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. CroenLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionSelf-reported and EHR-documented COVID-19 prevalence by sociodemographic and clinical factors, crude and adjusted prevalence ratios.
Frequency of self-reported receipt of COVID-19 test and test-positivity...
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xlsUpdated Jun 10, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. Croen (2023). Frequency of self-reported receipt of COVID-19 test and test-positivity among women who were currently pregnant at time of survey completion. [Dataset]. http://doi.org/10.1371/journal.pone.0256891.t003xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0256891.t003Dataset updatedJun 10, 2023AuthorsJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. CroenLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionFrequency of self-reported receipt of COVID-19 test and test-positivity among women who were currently pregnant at time of survey completion.
Characteristics of survey respondents, Kaiser Permanente Northern...
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xlsUpdated Jun 9, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. Croen (2023). Characteristics of survey respondents, Kaiser Permanente Northern California. [Dataset]. http://doi.org/10.1371/journal.pone.0256891.t001xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0256891.t001Dataset updatedJun 9, 2023AuthorsJennifer L. Ames; Assiamira Ferrara; Lyndsay A. Avalos; Sylvia E. Badon; Mara B. Greenberg; Monique M. Hedderson; Michael W. Kuzniewicz; Yinge Qian; Kelly C. Young-Wolff; Ousseny Zerbo; Yeyi Zhu; Lisa A. CroenLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredCaliforniaDescriptionCharacteristics of survey respondents, Kaiser Permanente Northern California.
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Crude and adjusted prevalence ratios for hypertension.
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xlsUpdated Jun 17, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteDustin G. Gibson; Ankita Meghani; Charles Ssemagabo; Adaeze Wosu; Gulam Muhammed Al Kibria; Tryphena Nareeba; Collins Gyezaho; Edward Galiwango; Judith Kaija Nanyonga; George W. Pariyo; Dan Kajungu; Elizeus Rutebemberwa; Adnan Ali Hyder (2024). Crude and adjusted prevalence ratios for hypertension. [Dataset]. http://doi.org/10.1371/journal.pgph.0002998.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pgph.0002998.t004Dataset updatedJun 17, 2024Dataset provided byPLOS Global Public HealthAuthorsDustin G. Gibson; Ankita Meghani; Charles Ssemagabo; Adaeze Wosu; Gulam Muhammed Al Kibria; Tryphena Nareeba; Collins Gyezaho; Edward Galiwango; Judith Kaija Nanyonga; George W. Pariyo; Dan Kajungu; Elizeus Rutebemberwa; Adnan Ali HyderLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionCrude and adjusted prevalence ratios for hypertension.
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Crude and population-weighted prevalence of chronic diseases and means of...
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xlsUpdated Jul 18, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteMonsermin Gualan; Irina Chis Ster; Tatiana Veloz; Emily Granadillo; Luz M. Llangari-Arizo; Alejandro Rodriguez; Julia A. Critchley; Peter Whincup; Miguel Martin; Natalia Romero-Sandoval; Philip J. Cooper (2024). Crude and population-weighted prevalence of chronic diseases and means of blood pressure and glucose measures in study population of 931 adults stratified by sex. [Dataset]. http://doi.org/10.1371/journal.pone.0307403.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0307403.t002Dataset updatedJul 18, 2024Dataset provided byPLOS ONEAuthorsMonsermin Gualan; Irina Chis Ster; Tatiana Veloz; Emily Granadillo; Luz M. Llangari-Arizo; Alejandro Rodriguez; Julia A. Critchley; Peter Whincup; Miguel Martin; Natalia Romero-Sandoval; Philip J. CooperLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionCrude and population-weighted prevalence of chronic diseases and means of blood pressure and glucose measures in study population of 931 adults stratified by sex.
Additional file 1 of Annual dialysis data report 2019, JSDT Renal Data...
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Updated Aug 16, 2024ShareFacebookTwitterEmailClick to copy linkLink copiedCiteNorio Hanafusa; Masanori Abe; Nobuhiko Joki; Tetsuya Ogawa; Eiichiro Kanda; Kan Kikuchi; Shunsuke Goto; Masatomo Taniguchi; Shigeru Nakai; Toshihide Naganuma; Takeshi Hasegawa; Junichi Hoshino; Kenichiro Miura; Atsushi Wada; Yoshiaki Takemoto (2024). Additional file 1 of Annual dialysis data report 2019, JSDT Renal Data Registry [Dataset]. http://doi.org/10.6084/m9.figshare.24165077.v1Unique identifierhttps://doi.org/10.6084/m9.figshare.24165077.v1Dataset updatedAug 16, 2024AuthorsNorio Hanafusa; Masanori Abe; Nobuhiko Joki; Tetsuya Ogawa; Eiichiro Kanda; Kan Kikuchi; Shunsuke Goto; Masatomo Taniguchi; Shigeru Nakai; Toshihide Naganuma; Takeshi Hasegawa; Junichi Hoshino; Kenichiro Miura; Atsushi Wada; Yoshiaki TakemotoDescriptionAdditional file 1: The detailed data presented in the text as figures are proved in supplementary tables as Additional File 1.
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Prevalence and population attributable fraction (PAF) for modifiable risk...
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xlsUpdated Jun 16, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteKamal Sadeghi; Jalal Poorolajal; Amin Doosti-Irani (2023). Prevalence and population attributable fraction (PAF) for modifiable risk factors of TB. [Dataset]. http://doi.org/10.1371/journal.pone.0271511.t004xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0271511.t004Dataset updatedJun 16, 2023Dataset provided byPLOS ONEAuthorsKamal Sadeghi; Jalal Poorolajal; Amin Doosti-IraniLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionPrevalence and population attributable fraction (PAF) for modifiable risk factors of TB.
Regressive models to explain mean age of Kawasaki disease patients pooled...
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xlsUpdated May 31, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteYoshiro Nagao (2023). Regressive models to explain mean age of Kawasaki disease patients pooled between 1979 and 2010. [Dataset]. http://doi.org/10.1371/journal.pone.0067934.t003xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0067934.t003Dataset updatedMay 31, 2023AuthorsYoshiro NagaoLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescription*Only TFR remained as the statistically significant contributor to the conventional multivariate models for both crude and adjusted mean patient age.†In spatial regression, no variable remained as a significant contributor to crude mean age and only TFR remained as the significant contributor to adjusted mean age.
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Crude and adjusted mean difference (β) of BMI change from before to 4 years...
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xlsUpdated Jun 1, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteAna Henriques; Elisabete Alves; Henrique Barros; Ana Azevedo (2023). Crude and adjusted mean difference (β) of BMI change from before to 4 years after pregnancy estimated by linear regression, according to body image satisfaction, in normal, overweight and obese women before pregnancy. [Dataset]. http://doi.org/10.1371/journal.pone.0070230.t002xlsAvailable download formatsUnique identifierhttps://doi.org/10.1371/journal.pone.0070230.t002Dataset updatedJun 1, 2023Dataset provided byPLOS ONEAuthorsAna Henriques; Elisabete Alves; Henrique Barros; Ana AzevedoLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescription95%CI, 95% confidence interval; BMI, body mass index.*Adjusted for age (
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License information was derived automatically
Crude incidence and prevalence of MS in Hungary between 2010 and 2015.