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A Two-Step Markov Processes Approach for Parameterization of Cancer State-Transition Models for Low- and Middle-Income Countries

Author

Listed:
  • Chaitra Gopalappa

    (University of Massachusetts Amherst, Amherst, MA, USA)

  • Jiachen Guo

    (University of Massachusetts Amherst, Amherst, MA, USA)

  • Prashant Meckoni

    (University of Massachusetts Amherst, Amherst, MA, USA)

  • Buyannemekh Munkhbat

    (University of Massachusetts Amherst, Amherst, MA, USA)

  • Carel Pretorius

    (Avenir Health, Glastonbury, CT, USA)

  • Jeremy Lauer

    (World Health Organization, Geneva, GE, Switzerland)

  • André Ilbawi

    (World Health Organization, Geneva, GE, Switzerland)

  • Melanie Bertram

    (World Health Organization, Geneva, GE, Switzerland)

Abstract

Implementation of organized cancer screening and prevention programs in high-income countries (HICs) has considerably decreased cancer-related incidence and mortality. In low- and middle-income countries (LMICs), screening and early diagnosis programs are generally unavailable, and most cancers are diagnosed in late stages when survival is very low. Analyzing the cost-effectiveness of alternative cancer control programs and estimating resource needs will help prioritize interventions in LMICs. However, mathematical models of natural cancer onset and progression needed to conduct the economic analyses are predominantly based on populations in HICs because the longitudinal data on screening and diagnoses required for parameterization are unavailable in LMICs. Models currently used for LMICs mostly concentrate on directly calculating the shift in distribution of cancer diagnosis as an evaluative measure of screening. We present a mathematical methodology for the parameterization of natural cancer onset and progression, specifically for LMICs that do not have longitudinal data. This full onset and progression model can help conduct comprehensive analyses of cancer control programs, including cancer screening, by considering both the positive impact of screening as well as any adverse consequences, such as over-diagnosis and false-positive results. The methodology has been applied to breast, cervical, and colorectal cancers for 2 regions, under the World Health Organization categorization: Eastern Sub-Saharan Africa (AFRE) and Southeast Asia (SEARB). The cancer models have been incorporated into the Spectrum software and interfaced with country-specific demographic data through the demographic projections (DemProj) module and costing data through the OneHealth tool. These software are open-access and can be used by stakeholders to analyze screening strategies specific to their country of interest.

Suggested Citation

  • Chaitra Gopalappa & Jiachen Guo & Prashant Meckoni & Buyannemekh Munkhbat & Carel Pretorius & Jeremy Lauer & André Ilbawi & Melanie Bertram, 2018. "A Two-Step Markov Processes Approach for Parameterization of Cancer State-Transition Models for Low- and Middle-Income Countries," Medical Decision Making, , vol. 38(4), pages 520-530, May.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:4:p:520-530
    DOI: 10.1177/0272989X18759482
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    Cited by:

    1. Robert Kraig Helmeczi & Can Kavaklioglu & Mucahit Cevik & Davood Pirayesh Neghab, 2023. "A multi-objective constrained partially observable Markov decision process model for breast cancer screening," Operational Research, Springer, vol. 23(2), pages 1-42, June.

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