Cancer
Measuring the uptake of screening mammography in populations using insurance claims: a problem of imperfect sensitivity Neal D. Goldstein* Neal Goldstein Goldstein Goldstein Goldstein Goldstein Goldstein Goldstein University of Delaware
Background: Quantifying the uptake of screening mammography at a population level is challenged by suboptimal surveillance data. Insurance claims represent a potentially useful data source, however, claims data can be limited by underreporting. Using a quantitative bias analysis framework, we demonstrate an approach for dealing with imperfect sensitivity in an insurance claims database.
Methods: Insurance claims were obtained at the census tract level for screening mammography for the State of Delaware for 2019 and 2022. A measurement model related the observed uptake of mammography to the truth via two approaches: 1) the total number of medical claims, and 2) proportion of government funded health insurance. We then modeled how uptake of mammography predicts invasive breast cancer incidence and stage at diagnosis in Delaware.
Results: During the year 2022 and based on the observed measure, the statewide averaged uptake was 176 mammograms per 1,000 women aged 40 and above. This figure increased to a median of 234 mammograms per 1,000 women (95% simulation interval: 230, 239) following adjustment. Each additional 1,000 mammograms predicted an increase of 126 breast cancer cases using the observed data (95% confidence interval: 112, 140), whereas following adjustment, this number was attenuated to 77 cases (95% simulation interval: 63, 90). Mammography uptake was less predictive of more advanced stage disease following adjustment, in line with our hypothesis.
Conclusions: When working with administrative datasets for measuring the uptake of screening mammography in a population, researchers and healthcare administrators should account for potential underreporting.