Model-based imputation of missing data from the 122 Cities Mortality Reporting System (122 CMRS)

Paula Moraga, Al Ozonoff

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


National estimates of the all-cause and pneumonia and influenza (P&I) mortality burden derived from U.S. influenza surveillance data treat all missing or unreported values as zero counts. The effect of this methodological decision is to undercount influenza deaths, thus biasing estimates downward and producing underestimates of the true mortality burden. In this paper, a regression-based procedure is proposed to impute missing values and thus produce a more accurate estimate of mortality. Several model specifications are considered and evaluated to predict weekly death counts by city, calendar week, calendar year and age group. Revised all-cause, P&I and excess mortality estimates are calculated by imputing the missing data. The impact of the treatment of unreported mortality data on national estimates is evaluated by comparing the estimates obtained using data with and without imputation. This comparison reflects some differences in mortality burden, excess deaths, and trends over time. The model presented is a useful approach to impute missing counts and improve inference in situations with modest occurrence of missing data.
Original languageEnglish (US)
Pages (from-to)1499-1507
Number of pages9
JournalStochastic Environmental Research and Risk Assessment
Issue number5
StatePublished - Oct 18 2015
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2021-03-16

ASJC Scopus subject areas

  • Environmental Science(all)
  • Water Science and Technology
  • Environmental Chemistry
  • Environmental Engineering
  • Safety, Risk, Reliability and Quality


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