The impact of data suppression on local mortality rates: The case of cdc wonder

C. Tiwari, K. Beyer and G. Rushton

American Journal of Public Health, 2014, vol. 104, issue 8, 1386-1388

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.

Keywords: algorithm; article; factual database; heart disease; human; mortality; public health service; statistical analysis; statistics; United States, Algorithms; Centers for Disease Control and Prevention (U.S.); Data Interpretation, Statistical; Databases, Factual; Heart Diseases; Humans; Mortality; United States (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:aph:ajpbhl:10.2105/ajph.2014.301900_9

DOI: 10.2105/AJPH.2014.301900

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