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Persons using assistive technology might not be able to fully access information in this file. For assistance, please send e-mail to: mmwrq@cdc.gov. Type 508 Accommodation and the title of the report in the subject line of e-mail. Effects of Sensitivity and Specificity on Signal-to-Noise Ratios for Detection of Influenza-Associated AberrationsWilliam W. Thompson
Corresponding author: William W. Thompson, CDC, 1600 Clifton Rd., MS E-61, Atlanta, GA 30333. Telephone: 404-639-8256; Fax: 404-639-8834; E-mail: wct2@cdc.gov. AbstractIntroduction: Influenza-associated outcomes have been used to test and validate alternative aberration-detection methods, yet a limited number of studies have examined the effects of using different outcomes with varying levels of sensitivity and specificity for influenza. Objectives: Influenza aberration-detection models developed by CDC were applied to daily death outcomes by using city-level mortality data. Methods: Influenza surveillance data were obtained from the World Health Organization, and city-level mortality data were obtained from CDC's National Center for Health Statistics. Deaths were categorized by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes. Age-specific log-linear regression models were used to identify influenza-associated aberrations in death outcomes. Results: For pneumonia and influenza deaths, the models accounted for 49% and 83% of the variance for persons aged <65 years and persons aged >65 years, respectively. Influenza accounted for 4.4% of the variance among persons aged <65 and 8.2% of the variance among persons aged >65. Seasonal variation accounted for the greatest percentage of explained variance for pneumonia and influenza deaths; day-of-week, holiday, and post-holiday variables accounted for <1% of the explained variance. For respiratory and circulatory deaths, the models accounted for 89% of the variance in outcome both for persons aged <65 and persons aged >65. Influenza accounted for 1.2% of the variance among persons aged < 65 and 6% of the variance among persons aged >65. Seasonal variation accounted for substantially less of the explained variance in death outcome for persons aged <65; time trends accounted for substantially more of the variation when compared with models applied to persons aged >65. Conclusions: Substantial differences were identified in the signal-to-noise ratios by influenza-associated death outcomes. Certain confounders (e.g., age, time, and season) are key factors when identifying influenza-associated aberrations.
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