Carlos Grijalva
Last active: 7/27/2018

Use of Multiple Imputation to Estimate the Proportion of Respiratory Virus Detections Among Patients Hospitalized With Community-Acquired Pneumonia.

Bozio CH, Flanders WD, Finelli L, Bramley AM, Reed C, Gandhi NR, Vidal JE, Erdman D, Levine MZ, Lindstrom S, Ampofo K, Arnold SR, Self WH, Williams DJ, Grijalva CG, Anderson EJ, McCullers JA, Edwards KM, Pavia AT, Wunderink RG, Jain S
Open Forum Infect Dis. 2018 5 (4): ofy061

PMID: 29946553 · PMCID: PMC5890478 · DOI:10.1093/ofid/ofy061

Background - Real-time polymerase chain reaction (PCR) on respiratory specimens and serology on paired blood specimens are used to determine the etiology of respiratory illnesses for research studies. However, convalescent serology is often not collected. We used multiple imputation to assign values for missing serology results to estimate virus-specific prevalence among pediatric and adult community-acquired pneumonia hospitalizations using data from an active population-based surveillance study.

Methods - Presence of adenoviruses, human metapneumovirus, influenza viruses, parainfluenza virus types 1-3, and respiratory syncytial virus was defined by positive PCR on nasopharyngeal/oropharyngeal specimens or a 4-fold rise in paired serology. We performed multiple imputation by developing a multivariable regression model for each virus using data from patients with available serology results. We calculated absolute and relative differences in the proportion of each virus detected comparing the imputed to observed (nonimputed) results.

Results - Among 2222 children and 2259 adults, 98.8% and 99.5% had nasopharyngeal/oropharyngeal specimens and 43.2% and 37.5% had paired serum specimens, respectively. Imputed results increased viral etiology assignments by an absolute difference of 1.6%-4.4% and 0.8%-2.8% in children and adults, respectively; relative differences were 1.1-3.0 times higher.

Conclusions - Multiple imputation can be used when serology results are missing, to refine virus-specific prevalence estimates, and these will likely increase estimates.

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