Improvement of Influenza Incidence Estimation Using Auxiliary Information in Sentinel Surveillance in Japan
Miyuki Kawado1, Shuji Hashimoto1, *, Akiko Ohta2, Mari S. Oba3, Kiyosu Taniguchi4, Tomimasa Sunagawa5, Tamano Matsui5, Masaki Nagai6, Yoshitaka Murakami3
Identifiers and Pagination:Year: 2018
First Page: 29
Last Page: 36
Publisher Id: TOIDJ-10-29
Article History:Received Date: 6/12/2017
Revision Received Date: 06/4/2018
Acceptance Date: 13/4/2018
Electronic publication date: 14/05/2018
Collection year: 2018
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sentinel surveillance in Japan is used to estimate national influenza incidence under the assumption that Sentinel Medical Institutions (SMIs) are randomly selected. The current method might lead to overestimation when SMIs are recruited on a voluntary basis.
Aims & Objectives:
We aimed to improve influenza incidence estimation using auxiliary information without this assumption.
Materials and Method:
We used reports of influenza from SMIs in 2015, together with the number of all disease outpatients in September 2014 at all medical institutions from the Survey of Medical Institutions of Japan, as auxiliary information. The influenza incidence was estimated by the method using auxiliary information and the current method (without auxiliary information).
Result and Conclusion:
Influenza incidence rate per 1,000 population in 2015 estimated by using auxiliary information and by the current method was 63.7 (95% Confidence Interval (CI), 61.0-66.3) and 96.5 (95% CI, 93.0-100.0), respectively. The ratio of these estimates was 0.66. Our findings suggest that influenza incidence estimated by using the number of all disease outpatients as auxiliary information is more accurate than estimates by the current method.