The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER.
CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nation's primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps prod...
| Publicado en: | American Journal of Public Health Vol. 104; no. 8; pp. 1386 - 1389 |
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| Autores principales: | , , |
| Formato: | Journal Article |
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American Public Health Association
Aug2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=107870973&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 107870973 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Aug2014 vid: 104 iid: 8 pid: 44 pub: American Public Health Association artinfo: ui: 107870973 10.2105/AJPH.2014.301900 ppf: 1386 ppct: 3 formats: tig: atl: The Impact of Data Suppression on Local Mortality Rates: The Case of CDC WONDER. aug: au: Tiwari, Chetan Beyer, Kirsten Rushton, Gerard su: United States Centers for Disease Control & Prevention (U.S.) Heart disease related mortality Mortality Census Health policy Algorithms Statistical correlation Databases Epidemiological research Maps Research evaluation Statistics sug: subj: Heart disease related mortality Mortality Census Health policy United States Centers for Disease Control & Prevention (U.S.) Administration of Public Health Programs Other printing Book, Periodical, and Newspaper Merchant Wholesalers Algorithms Statistical correlation Databases Epidemiological research Maps Research evaluation Statistics ab: CDC WONDER (Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research) is the nation's primary data repository for health statistics. Before WONDER data are released to the public, data cells with fewer than 10 case counts are suppressed. We showed that maps produced from suppressed data have predictable geographic biases that can be removed by applying population data in the system and an algorithm that uses regional rates to estimate missing data. By using CDC WONDER heart disease mortality data, we demonstrated that effects of suppression could be largely overcome. pubtype: Academic Journal doctype: Journal Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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