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.
|Number of pages||9|
|Journal||Stochastic Environmental Research and Risk Assessment|
|Publication status||E-pub ahead of print - 18 Oct 2014|
Moraga, P., & Ozonoff, A. (2014). Model-based imputation of missing data from the 122 Cities Mortality Reporting System (122 CMRS). Stochastic Environmental Research and Risk Assessment, 29(5), 1499–1507. https://doi.org/10.1007/s00477-014-0974-4