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Related Experiment Videos

A method for assessing age-time disease incidence using serial prevalence data

I C Marschner1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.

Biometrics
|January 10, 1998
PubMed
Summary

This study estimates disease incidence trends using population prevalence data, accounting for selection bias. Disease incidence trends are robust to differential selection, even with complex models.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical modeling

Background:

  • Estimating disease incidence trends from serial prevalence data is challenging.
  • Existing methods often fail to account for differential selection of individuals.
  • Accurate incidence estimation is crucial for public health interventions.

Purpose of the Study:

  • To develop a nonparametric method for estimating age- and time-specific disease incidence trends.
  • To account for differential selection of diseased and undiseased individuals in prevalence data.
  • To apply the method to serial toxoplasmosis prevalence data.

Main Methods:

  • Utilized serial cross-sectional prevalence data from multiple population samples.
  • Employed an expectation-maximization (EM) algorithm for maximum likelihood estimation under a log-linear incidence odds model.

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  • Extended the methodology using generalized additive models for smooth nonparametric estimation with penalized likelihood.

    • Main Results:

    • The EM algorithm facilitates maximum likelihood estimation using existing generalized linear models software.
    • The nonparametric approach allows fitting a range of age-time incidence models.
    • Qualitative incidence trends were found to be robust to differential selection patterns.

    Conclusions:

    • The proposed methodology provides a flexible framework for nonparametric incidence estimation.
    • Nonmultiplicative models are generally unnecessary for analyzing this type of data.
    • The findings suggest that disease incidence trends are relatively stable despite potential selection biases.