Screening and Selection: The Case of Mammograms
Author(s) -
Liran Einav,
Amy Finkelstein,
Tamar Oostrom,
Abigail Ostriker,
Heidi Williams
Publication year - 2019
Publication title -
health economics ejournal
Language(s) - English
Resource type - Reports
DOI - 10.3386/w26162
Subject(s) - selection (genetic algorithm) , case selection , computer science , artificial intelligence , medicine , surgery
Debates over whether and when to recommend screening for a potential disease focus on the causal impact of screening for a typical individual covered by the recommendation, who may differ from the typical individual who responds to the recommendation. We explore this distinction in the context of recommendations that breast cancer screening start at age 40. The raw data suggest that responders to the age 40 recommendation have less cancer than do women who self-select into screening at earlier ages. Combining these patterns with a clinical oncology model allows us to infer that responders to the age 40 recommendation also have less cancer than women who never screen, suggesting that the benefits of recommending early screening are smaller than if responders were representative of covered individuals. For example, we estimate that shifting the recommendation from age 40 to age 45 results in over three times as many deaths if responders were randomly drawn from the population than under the estimated patterns of selection. These results highlight the importance of considering the characteristics of responders when making and designing recommendations.
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