Using an Agent-based Model to Examine Deimplementation of Breast Cancer Screening

Bibliographic Data

ID9096409
AuthorsSarah A Nowak (0000-0001-5965-9035, University of Vermont, corresponding author), Andrew M Parker (0000-0001-7511-0894, RAND Corporation, Pittsburgh, PA), Archana Radhakrishnan (0000-0001-7689-6390, University of Michigan), Nancy Schoenborn (School of Medicine, Johns Hopkins University), Nancy L Schoenborn (0000-0001-5053-5132, Johns Hopkins University), Craig Evan Pollack (0000-0003-2464-6415, Johns Hopkins University)
Year2021
Volume59
Issue1
Pagese1-e8
Publication date2021-01-01
Open AccessNo
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000001442
PMID33165149
OpenAlexW3105021465
LanguageEN
Citations received1
References cited38

OBJECTIVE: The objective of this study was to examine the potential impact of provider social networks and experiences with patients on deimplementation of breast cancer screening. RESEARCH DESIGN: We constructed the Breast Cancer-Social network Agent-based Model (BC-SAM), which depicts breast cancer screening decisions, incidence, and progression among 10,000 women ages 40 and over and the screening recommendations of their providers over a 30-year period. The model has patient and provider modules that each incorporate social network influences. Patients and providers were connected in a network, which represented patient-patient peer connections, provider-provider peer connections, connections between providers and patients they treat, and friend/family relationships between patients and providers. We calibrated provider decisions in the model using data from the CanSNET national survey of primary care physicians in the United States, which we fielded in 2016. RESULTS: First, assuming that providers' screening recommendations for women ages 50-74 remain unchanged but their recommendations for screening among younger (below 50 y old) and older (75+ y old) women decrease, we observed a decline in predicted screening rates for women ages 50-74 due to spillover effects. Second, screening rates for younger and older women were slow to respond to changes in provider recommendations; a 78% decline in provider recommendations to older women over 30 years resulted in an estimated 23% decline in patient screening in that group. Third, providers' experiences with unscreened patients, friends, and family members modestly increased screening recommendations over time (7 percentage points). Finally, we found that provider peer effects can have a substantial impact on population screening rates and can entrench existing practices. CONCLUSION: Modeling cancer screening as a complex social system demonstrates a range of potential effects and may help target future interventions designed to reduce overscreening

Breast cancer · Cancer · Family medicine · Incidence (geometry) · Primary care · Spillover effect · AI in cancer detection · Global Cancer Incidence and Screening · Healthcare Systems and Technology · Internal Medicine · Medicine · Gerontology

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