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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shifali Bansal & Vijeta Deshpande & Xinmeng Zhao & Jeremy A. Lauer & Filip Meheus & André Ilbawi & Chaitra Gopalappa, 2020. "Analysis of Mammography Screening Schedules under Varying Resource Constraints for Planning Breast Cancer Control Programs in Low- and Middle-Income Countries: A Mathematical Study," Medical Decision Making, , vol. 40(3), pages 364-378, April.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:3:p:364-378
    DOI: 10.1177/0272989X20910724
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    References listed on IDEAS

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    1. Lisa M. Maillart & Julie Simmons Ivy & Scott Ransom & Kathleen Diehl, 2008. "Assessing Dynamic Breast Cancer Screening Policies," Operations Research, INFORMS, vol. 56(6), pages 1411-1427, December.
    2. David E. Bloom & Dan Chisholm & Eva Jane-Llopis & Klaus Prettner & Adam Stein & Andrea Feigl, 2011. "From Burden to "Best Buys": Reducing the Economic Impact of Non-Communicable Disease in Low- and Middle-Income Countries," PGDA Working Papers 7511, Program on the Global Demography of Aging.
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    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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