Age-Adjustment in National Biosurveillance Systems

A Survey of Issues and Analytical Tools for Age-Adjustment in Biosurveillance
  • Steven A. Cohen
  • Elena N. Naumova
Part of the Integrated Series in Information Systems book series (ISIS, volume 27)

Chapter Overview

The practice of biosurveillance primarily involves measuring disease cases accurately and precisely in a given population. However, measuring the size and composition of the actual population at risk for the diseases under surveillance is just as important, particularly when the objective of surveillance is to measure rates of disease. The purpose of this chapter is to explore the issues pertaining to selection of the population denominator in population surveillance, with a particular focus on age and age-adjustment. This chapter presents an overview of several data sources commonly available to surveillance and epidemiological professionals, along with a synopsis of graphical and statistical tools to help assess and adjust for age effects in disease patterns.


Population dynamics Surveillance Age-adjustment Age-period-cohort analysis Standardization 


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Online Resources

  1. The US Census Bureau maintains an excellent and user-friendly database of data from the most recent decennial censuses. Most decennial Census data are available for many levels of geography, including states, countries, cities and towns, ZIP codes, census tracts and blocks, and much more. These data can be found on the Census Bureau’s website, or on a data clearinghouse webpage known as Data Ferret.Google Scholar
  2. The US Census Bureau’s website also maintains a database containing intercensal population estimates from the Population Estimates Program. This one page contains links to all the publicly available datasets and is organized by geographic level and contains data dating back to 1990.•
  3. The archived population estimates, dating back in some cases to 1900, can be found at:•

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Steven A. Cohen
    • 1
    • 2
  • Elena N. Naumova
    • 1
    • 2
  1. 1.Tufts University School of MedicineBostonUSA
  2. 2.Tufts University Initiative for the Forecasting and Modeling of Infectious Diseases (InForMID)Tufts UniversityBostonUSA

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