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

Moraga, Paula & Ozonoff, Al (2015) Model-based imputation of missing data from the 122 Cities Mortality Reporting System (122 CMRS). Stochastic Environmental Research and Risk Assessment, 29(5), pp. 1499-1507.

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Abstract

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.

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ID Code: 95685
Item Type: Journal Article
Refereed: Yes
Keywords: Influenza surveillance, Excess mortality, Missing data, Serfling method
DOI: 10.1007/s00477-014-0974-4
ISSN: 1436-3259
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer-Verlag Berlin Heidelberg
Deposited On: 22 May 2016 22:27
Last Modified: 23 May 2016 21:23

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