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A hidden Markov model for population-level cervical cancer screening data.

Braden C Soper1, Mari Nygård2, Ghaleb Abdulla1

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Summary

This study introduces a new hidden Markov model to analyze cervical cancer screening data. The model helps personalize screening intervals, aiming to improve cancer prevention strategies and reduce overtreatment.

Keywords:
cancer screeningcervical cancerhidden Markov modelpersonalized screeningpopulation-level dataprecision medicinereal-world evidence

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Norway's national cervical cancer screening program, established in 1992, utilizes triennial cytology exams for women aged 25-69.
  • While mass screening prevents up to 80% of cancers, it leads to significant screening activity and potential overtreatment of asymptomatic precancers.

Purpose of the Study:

  • To develop and present a continuous-time, time-inhomogeneous hidden Markov model.
  • To gain a detailed understanding of the cervical cancer screening process and carcinogenesis.
  • To explore the potential for personalizing screening intervals.

Main Methods:

  • Utilized a dataset of 1.7 million individuals' multivariate time-series medical exam data over 25 years.
  • Developed a continuous-time, time-inhomogeneous hidden Markov model.
  • Simultaneously estimated all model parameters and compared model-simulated survival curves with empirical data.

Main Results:

  • An age-dependent model was shown to accurately reflect the Norwegian screening program.
  • The model successfully compared empirical survival curves with data simulated from the proposed model.
  • The model demonstrated potential for generalization to include individual-level covariates and new screening exam types.

Conclusions:

  • The proposed hidden Markov model provides a robust framework for analyzing complex screening data.
  • Individualized screening histories and covariate data can significantly improve cancer screening program strategies.
  • The model offers a pathway towards personalized recommended screening intervals, optimizing cancer prevention and minimizing overtreatment.