Age-Adjustment in National Biosurveillance Systems

A Survey of Issues and Analytical Tools for Age-Adjustment in Biosurveillance
  • Steven A. Cohen
  • Elena N. Naumova
Chapter
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.

Keywords

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

References

  1. Bongaarts J. Fertility and reproductive preferences in post-transitional societies. Popul Dev Rev 1998;27:260–81.Google Scholar
  2. Cayuela A, Rodríguez-Domínguez S, Ruis-Borrego M, Gili M. Age-period-cohort analysis of breast cancer mortality rates in Andalucia (Spain). Ann Oncol 2004;15:686–8.PubMedCrossRefGoogle Scholar
  3. Centers for Medicare and Medicaid Services. Medicare eligibility tool. Website: http://www.medicare.gov/MedicareEligibility/Home.asp?dest=NAV|HomeGeneralEnrollment#TabTop. Accessed March 20, 2007.Google Scholar
  4. Cohen JE. Human population: the next half century. Science 2003;302:1172–5.PubMedCrossRefGoogle Scholar
  5. Cohen SA, Naumova EN. Population dynamics in the elderly: the need for age–adjustment in national biosurveillance systems. Lect Notes Comput Sci 2007;4506:47–58.CrossRefGoogle Scholar
  6. Cohen SA, Oomer IA, Naumova EN. Regional differences in the estimation of influenza burden in the elderly: does choice of population denominator matter? Presented at the Population Association of America Annual Meeting, New Orleans, LA, April 16–19, 2008.Google Scholar
  7. Collins JJ. The contribution of medical measures to the decline of mortality from respiratory tuberculosis: an age-period-cohort model. Demography 1982;19:409–27.PubMedCrossRefGoogle Scholar
  8. Derrick VPA. Observations of (1) errors of age in the population statistics of England and Wales and (2) the changes of mortality indicated by national records. J Inst Actuar 1927;58:117–59.Google Scholar
  9. Federal Interagency Forum on Aging-Related Statistics. Older Americans 2004. Key Indicators of Well-Being. Federal Interagency Forum on Aging-Related Statistics. Washington, DC: U.S. Government Printing Office.Google Scholar
  10. Fry AM, Shay DK, Holman RC, Curns AT, Anderson LJ. Trends in hospitalizations for pneumonia among persons aged 65 years or older in the United States, 1988–2002. JAMA 2005;294:2712–9.PubMedCrossRefGoogle Scholar
  11. MacNeill IB, Elwood JM, Miller D, Mao Y. Trends in mortality from melanoma in Canada and prediction of future rates. Stat Med 1995;14:821–39.PubMedCrossRefGoogle Scholar
  12. Olson DR, Heffernan RT, Paladini M, Konty K, Weiss D, Mostashari F. Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City. PLoS Med 2007;4:1349–61.fCrossRefGoogle Scholar
  13. Rizzo C, Viboud C, Montomoli E, Simonsen L, Miller MA. Influenza-related mortality in the Italian elderly: no decline associated with increasing vaccination coverage. Vaccine 2006;24:6468–75.PubMedCrossRefGoogle Scholar
  14. Sacher GA. On the statistical nature of mortality with special reference to chronic radiation mortality. Radiology 1956;67:250–7.PubMedGoogle Scholar
  15. Simonsen L, Fukuda K, Schonberger LB, Cox NJ. The impact of influenza epidemics on hospitalizations. J Infect Dis 2000;181:831–7.PubMedCrossRefGoogle Scholar
  16. Simonsen L, Reichert TA, Viboud C, Blackwelder WC, Taylor RJ, Miller MA. Impact of influenza vaccination on seasonal mortality in the US elderly population. Arch Intern Med 2005;165:265–72.PubMedCrossRefGoogle Scholar
  17. The Henry Kaiser Family Foundation: Medicare. Website: http://www.statehealthfacts.org/cgi-bin/healthfacts.cgi?action=compare&welcome=1&category=Medicare. Accessed March 20, 2007.
  18. Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, Fukuda K. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA 2004;289:179–86.CrossRefGoogle Scholar
  19. Tuljapurkar S, Li N, Boe C. A universal pattern of mortality decline in G7 countries. Nature 2000;405:789–92.PubMedCrossRefGoogle Scholar
  20. United States Bureau of the Census. Projections of the US population: 1999–2100. Available at: http://www.census.gov/population/www/projections/natproj.htm. Accessed February 8, 2007.
  21. S Census Bureau. Population Estimates. Website: http://www.census.gov/popest/estimates.php, Accessed February 22, 2008.
  22. Vandeschrick C, The Lexis diagram, a misnomer. Demogr Res 2001;4:97–124.CrossRefGoogle Scholar
  23. Wilmoth J, Vallin J, Caselli G. When does a cohort’s mortality differ from what we might expect? Popul English Selection 1990;2:93–126.Google Scholar

Suggested Reading

  1. Rowland DT. Demographic Methods and Concepts. Oxford: Oxford University Press. This is an essential overview of a variety of basic demographic concepts and procedures and serves as an excellent resource for public health researchers and practitioners into this relevant field.Google Scholar
  2. Preston SH, Heuveline P, Guillot M. Demography: Measuring and Modeling Population Processes. Oxford: Blackwell Publishing.Google Scholar
  3. This book provides an overview of some of the demographic theory and intermediate methods described in this chapter, as well as other, related demographic tools available for public health research. It is more mathematical and statistical than Rowland’s text.Google Scholar
  4. Du P, Coles FB, O’Campo P, McNutt LA. Changes in population characteristics and their implication on public health research. Epidemiol Perspect Innov. 2007;4:6.PubMedCrossRefGoogle Scholar
  5. This is an excellent article outlining some of the major challenges facing public health research in regard to changing population distributions and socioeconomic factors.Google Scholar
  6. Tapia Granados JA. Economics, demography, and epidemiology: an interdisciplinary glossary. J Epidemiol Community Health. 2003;57:929–35.PubMedCrossRefGoogle Scholar
  7. This article contains a glossary of terms and concepts essential to the fields of demography, epidemiology, public health, and economics.Google Scholar

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.• http://www.census.gov/popest/datasets.html
  3. The archived population estimates, dating back in some cases to 1900, can be found at:• http://www.census.gov/popest/archives/

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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