A two-step Markov processes approach for parameterization of cancer state-transition models for low- and middle-income countries

Chaitra Gopalappa; Jiachen Guo; Prashant Meckoni; Buyannemekh Munkhbat; Carel Pretorius; Jeremy Lauer; André Ilbawi; Melanie Bertram

doi:10.1177/0272989X18759482

A two-step Markov processes approach for parameterization of cancer state-transition models for low- and middle-income countries

Chaitra Gopalappa^*, Jiachen Guo, Prashant Meckoni, Buyannemekh Munkhbat, Carel Pretorius, Jeremy Lauer, André Ilbawi, Melanie Bertram

^*Corresponding author for this work

Management Science

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Pages (from-to)	520-530
Number of pages	11
Journal	Medical Decision Making
Volume	38
Issue number	4
Early online date	24 Mar 2018
DOIs	https://doi.org/10.1177/0272989X18759482
Publication status	Published - 1 May 2018

Funding

University of Massachusetts Amherst, Amherst, MA, USA (CG, JG, PM, BM); Avenir Health, Glastonbury, CT, USA (CP); and World Health Organization, Geneva, GE, Switzerland (JL, AI, MB). Financial support for this study was provided by a grant from the World Health Organization. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The following authors are employed by the sponsor: Jeremy Lauer, André Ilbawi, and Melanie Bertram. Work presented at the INFORMS Annual Meeting, November 2014, San Francisco, CA; INFORMS Annual Meeting, November 2017, Nashville, TN. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Keywords

breast cancer modeling
cancer progression modeling
cervical cancer modeling
colorectal cancer modeling
low income countries
Markov processes
middle income countries

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10.1177/0272989X18759482

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title = "A two-step Markov processes approach for parameterization of cancer state-transition models for low- and middle-income countries",

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.",

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AU - Munkhbat, Buyannemekh

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A two-step Markov processes approach for parameterization of cancer state-transition models for low- and middle-income countries

Abstract

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