Analysis of Mammography Screening Schedules under Varying Resource Constraints for Planning Breast Cancer Control Programs in Low- and Middle-Income Countries: A Mathematical Study

Shifali Bansal, Vijeta Deshpande, Xinmeng Zhao, Jeremy A. Lauer, Filip Meheus, André Ilbawi and Chaitra Gopalappa
Additional contact information
Shifali Bansal: University of Massachusetts–Amherst, Amherst, MA, USA
Vijeta Deshpande: University of Massachusetts–Amherst, Amherst, MA, USA
Xinmeng Zhao: University of Massachusetts–Amherst, Amherst, MA, USA
Jeremy A. Lauer: World Health Organization, Geneva, Switzerland
Filip Meheus: International Agency for Research on Cancer, Lyon, Rhône-Alpes, France
André Ilbawi: World Health Organization, Geneva, Switzerland
Chaitra Gopalappa: University of Massachusetts–Amherst, Amherst, MA, USA

Medical Decision Making, 2020, vol. 40, issue 3, 364-378

Abstract: Background. Low-and-middle-income countries (LMICs) have higher mortality-to-incidence ratio for breast cancer compared to high-income countries (HICs) because of late-stage diagnosis. Mammography screening is recommended for early diagnosis, however, the infrastructure capacity in LMICs are far below that needed for adopting current screening guidelines. Current guidelines are extrapolations from HICs, as limited data had restricted model development specific to LMICs, and thus, economic analysis of screening schedules specific to infrastructure capacities are unavailable. Methods. We applied a new Markov process method for developing cancer progression models and a Markov decision process model to identify optimal screening schedules under a varying number of lifetime screenings per person, a proxy for infrastructure capacity. We modeled Peru, a middle-income country, as a case study and the United States, an HIC, for validation. Results. Implementing 2, 5, 10, and 15 lifetime screens would require about 55, 135, 280, and 405 mammography machines, respectively, and would save 31, 62, 95, and 112 life-years per 1000 women, respectively. Current guidelines recommend 15 lifetime screens, but Peru has only 55 mammography machines nationally. With this capacity, the best strategy is 2 lifetime screenings at age 50 and 56 years. As infrastructure is scaled up to accommodate 5 and 10 lifetime screens, screening between the ages of 44-61 and 41-64 years, respectively, would have the best impact. Our results for the United States are consistent with other models and current guidelines. Limitations. The scope of our model is limited to analysis of national-level guidelines. We did not model heterogeneity across the country. Conclusions. Country-specific optimal screening schedules under varying infrastructure capacities can systematically guide development of cancer control programs and planning of health investments.

Keywords: cancer screening guidelines; dynamic programming; low- and -middle income countries; mammography screening; Markov decision processes (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X20910724 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:3:p:364-378

DOI: 10.1177/0272989X20910724

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().