Estimating population effects of vaccination using large, routinely collected data

被引：8

作者：

Halloran, M. Elizabeth ^{[1
,2
]}

Hudgens, Michael G. ^{[3
]}

机构：

[1] Fred Hutchinson Canc Res Ctr, Vaccine & Infect Dis Div, Ctr Inference & Dynam Infect Dis, 1124 Columbia St, Seattle, WA 98104 USA

[2] Univ Washington, Sch Publ Hlth, Dept Biostat, Seattle, WA 98195 USA

[3] Univ North Carolina Chapel Hill, Gillings Sch Global Publ Hlth, Dept Biostat, Chapel Hill, NC USA

来源：

STATISTICS IN MEDICINE | 2018年 / 37卷 / 02期

关键词：

causal inference; dependent happenings; herd immunity; indirect effects; potential outcome; surveillance; vaccination; EFFICACY;

D O I：

10.1002/sim.7392

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Vaccination in populations can have several kinds of effects. Establishing that vaccination produces population-level effects beyond the direct effects in the vaccinated individuals can have important consequences for public health policy. Formal methods have been developed for study designs and analysis that can estimate the different effects of vaccination. However, implementing field studies to evaluate the different effects of vaccination can be expensive, of limited generalizability, or unethical. It would be advantageous to use routinely collected data to estimate the different effects of vaccination. We consider how different types of data are needed to estimate different effects of vaccination. The examples include rotavirus vaccination of young children, influenza vaccination of elderly adults, and a targeted influenza vaccination campaign in schools. Directions for future research are discussed. Copyright (c) 2017 John Wiley & Sons, Ltd.

引用

页码：294 / 301

页数：8

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