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Estimating disease prevalence using census data.

M Choy1, P Switzer, C De Martel

  • 1Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA 94305-5107, USA.

Epidemiology and Infection
|December 1, 2007
PubMed
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This study estimates disease prevalence in small areas using Helicobacter pylori infection data. Publicly available data and logistic regression accurately predicted infection rates and correlated with gastric cancer incidence.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating disease prevalence in small geographic areas is challenging.
  • Helicobacter pylori infection is a suitable model for developing new estimation methods.
  • Utilizing publicly available data can overcome resource limitations.

Purpose of the Study:

  • To develop and validate a method for estimating disease prevalence in small geographic areas using publicly available data.
  • To apply this method to estimate Helicobacter pylori infection prevalence in the San Francisco Bay Area.
  • To explore the correlation between estimated H. pylori prevalence and gastric cancer incidence.

Main Methods:

  • Logistic regression analysis of data from the Third National Health and Nutrition Examination Survey to determine H. pylori risk parameters.

Related Experiment Videos

  • Validation of risk parameters by predicting infections in an independent cohort.
  • Application of a predictive logistic model using 1990 U.S. Census data to estimate H. pylori prevalence in the San Francisco Bay Area.

    • Main Results:

    • Risk parameters for H. pylori infection were successfully obtained and validated.
    • The predictive logistic model accurately estimated H. pylori prevalence in the San Francisco Bay Area.
    • A strong positive correlation was observed between predicted H. pylori prevalence and gastric cancer incidence (P<0.001, R2=0.87).
    • No significant association was found between H. pylori prevalence and other cancer types.

    Conclusions:

    • A novel method using publicly available data and logistic regression can effectively estimate disease prevalence in small geographic areas.
    • The developed method shows promise for public health surveillance and epidemiological research.
    • The strong correlation with gastric cancer incidence highlights the public health significance of H. pylori infection.