Literature DB >> 24922161

The impact of data suppression on local mortality rates: the case of CDC WONDER.

Chetan Tiwari1, Kirsten Beyer, Gerard Rushton.   

Abstract

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.

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Year:  2014        PMID: 24922161      PMCID: PMC4103252          DOI: 10.2105/AJPH.2014.301900

Source DB:  PubMed          Journal:  Am J Public Health        ISSN: 0090-0036            Impact factor:   9.308


  4 in total

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Journal:  Stat Med       Date:  1999-12-15       Impact factor: 2.373

2.  Direct standardization (age-adjusted death rates).

Authors:  L R Curtin; R J Klein
Journal:  Healthy People 2000 Stat Notes       Date:  1995-03

3.  Empirical Bayes estimates of age-standardized relative risks for use in disease mapping.

Authors:  D Clayton; J Kaldor
Journal:  Biometrics       Date:  1987-09       Impact factor: 2.571

4.  Effects of the choice of age-adjustment method on maps of death rates.

Authors:  L W Pickle; A A White
Journal:  Stat Med       Date:  1995 Mar 15-Apr 15       Impact factor: 2.373

  4 in total
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2.  Ethical Machine Learning in Healthcare.

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Journal:  Annu Rev Biomed Data Sci       Date:  2021-05-06

3.  Opioid-related overdose mortality in the era of fentanyl: Monitoring a shifting epidemic by person, place, and time.

Authors:  Keri N Althoff; Kathryn M Leifheit; Ju Nyeong Park; Aruna Chandran; Susan G Sherman
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4.  County-Level Trends in Suicide Rates in the U.S., 2005-2015.

Authors:  Lauren M Rossen; Holly Hedegaard; Diba Khan; Margaret Warner
Journal:  Am J Prev Med       Date:  2018-05-14       Impact factor: 5.043

5.  County-Level Concentration of Selected Chronic Conditions Among Medicare Fee-for-Service Beneficiaries and Its Association with Medicare Spending in the United States, 2017.

Authors:  Kevin A Matthews; Anne H Gaglioti; James B Holt; Lisa C McGuire; Kurt J Greenlund
Journal:  Popul Health Manag       Date:  2020-04-01       Impact factor: 2.459

6.  Suicide Hot Spots: Leveraging County-Level Data and Local Agencies to Target Prevention in High-Risk Areas.

Authors:  Tanner J Bommersbach; Robert A Rosenheck; Anita S Everett
Journal:  Public Health Rep       Date:  2021-05-19       Impact factor: 3.117

7.  Estimating County-Level Mortality Rates Using Highly Censored Data From CDC WONDER.

Authors:  Harrison Quick
Journal:  Prev Chronic Dis       Date:  2019-06-13       Impact factor: 2.830

8.  Population projections for U.S. counties by age, sex, and race controlled to shared socioeconomic pathway.

Authors:  Mathew E Hauer
Journal:  Sci Data       Date:  2019-02-05       Impact factor: 6.444

9.  Racial residential segregation, socioeconomic disparities, and the White-Black survival gap.

Authors:  Ioana Popescu; Erin Duffy; Joshua Mendelsohn; José J Escarce
Journal:  PLoS One       Date:  2018-02-23       Impact factor: 3.240

10.  A novel approach to analyzing lung cancer mortality disparities: Using the exposome and a graph-theoretical toolchain.

Authors:  Paul D Juarez; Darryl B Hood; Gary L Rogers; Suzanne H Baktash; Arnold M Saxton; Patricia Matthews-Juarez; Wansoo Im; Myriam Patricia Cifuentes; Charles A Phillips; Maureen Y Lichtveld; Michael A Langston
Journal:  Environ Dis       Date:  2017-06-30
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