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ORCID
https://orcid.org/0000-0002-5226-2868
Access Type
Open Access Thesis
Document Type
thesis
Degree Program
Industrial Engineering & Operations Research
Degree Type
Master of Science (M.S.)
Year Degree Awarded
2020
Month Degree Awarded
September
Abstract
Low-and-middle-income countries (LMICs) have a 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, current screening guidelines are only generalized by economic disparities, and are based on extrapolation of data from randomized controlled trials in HICs, which have different disease burdens and all-cause mortality compared to LMICs. Moreover, the infrastructure capacity in LMICs is far below that needed for adopting current screening guidelines. This study analyzes the impact of disease burden, infrastructure availability, and other cause mortality on optimal mammography screening schedules for LMICs. Further, these key features are analyzed under the context of overdiagnosis, epidemiologic/clinical uncertainty in pathways of the initial stage of cancer, and variability in technological availability for diagnosis and treatment. It uses a Markov decision process (MDP) model to estimate optimal schedules under varying assumptions of resource availability, applying it to six LMICs. Results suggest that screening schedules should change with disease burden and life-expectancy. For countries with similar life-expectancy but different disease burden, the model suggests to screen age groups with higher incidence rates. For countries with similar incidence rate and different life expectancy, the model suggests to screen younger age groups for countries with lower life-expectancy. Overdiagnosis and differences in screening technology had minimal impact on optimal schedules. Optimality of screening schedules were sensitive to epidemiologic/clinical uncertainty. Results from this study suggest that, instead of generalized screening schedules, those tailored to disease burden and infrastructure capacity could help optimize resources. Results from this study can help inform current screening guidelines and future health investment plans.
DOI
https://doi.org/10.7275/18558455
First Advisor
Chaitra Gopalappa
Second Advisor
Ana Muriel
Third Advisor
Hari Balasubramanian
Recommended Citation
Shifali, Shifali, "Optimal Mammography Schedule Estimates Under Varying Disease Burden, Infrastructure Availability, and Other Cause Mortality: A Comparative Analyses of Six Low- and Middle- Income Countries" (2020). Masters Theses. 992.
https://doi.org/10.7275/18558455
https://scholarworks.umass.edu/masters_theses_2/992