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Segmented polynomials for incidence rate estimation from prevalence data.

Severin Guy Mahiané1,2, Oliver Laeyendecker3,4

  • 1Avenir Health, 06033, Glastonbury, CT, U.S.A.

Statistics in Medicine
|September 28, 2016
PubMed
Summary

This study introduces a new method using segmented polynomial models to estimate infection incidence from prevalence surveys. The approach accurately estimates HIV incidence in Zimbabwean men, accounting for mortality.

Keywords:
incidence ratemaximum likelihood estimationmodel selectionmortalityprevalencesegmented polynomials

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Estimating incidence of non-remissible infections with differential mortality is challenging using cross-sectional data.
  • Accurate incidence estimation is crucial for understanding disease dynamics and implementing effective public health interventions.

Purpose of the Study:

  • To develop and validate a novel statistical method for estimating infection incidence from cross-sectional prevalence surveys.
  • To apply the method to estimate HIV incidence among men in Zimbabwe.

Main Methods:

  • Fitting segmented polynomial models to estimate incidence as a function of age.
  • Utilizing maximum likelihood estimation with automatic knot optimization.
  • Employing the Akaike information criterion for model selection.

Main Results:

  • The proposed method successfully estimates infection incidence using simulated data.
  • HIV incidence among men in Zimbabwe was estimated using data from Project Accept (HPTN 043) and Zimbabwe Demographic Health Surveys.

Conclusions:

  • Segmented polynomial modeling provides a robust approach for estimating incidence in the presence of differential mortality.
  • The method offers a valuable tool for epidemiological research and public health surveillance, particularly for diseases like HIV.