Naïve Bayesian network-based contribution analysis of tumor biology and healthcare factors to racial disparity in breast cancer stage-at-diagnosis

Abstract

Background: Variation in breast cancer stage at initial diagnosis (including racial disparities) is driven both by tumor biology and healthcare factors.

Methods: We studied women age 67-74 with initial diagnosis of breast cancer from 2006 through 2014 in the SEER-Medicare database. We extracted variables related to tumor biology (histologic grade and hormone receptor status) and healthcare factors (screening mammography [SM] utilization and time delay from mammography to diagnostic biopsy). We used naïve Bayesian networks (NBNs) to illustrate the relationships among patient-specific factors and stage-at-diagnosis for African American (AA) and white patients separately. After identifying and controlling confounders, we conducted counterfactual inference through the NBN, resulting in an unbiased evaluation of the causal effects of individual factors on the expected utility of stage-at-diagnosis. An NBN-based decomposition mechanism was developed to evaluate the contributions of each patient-specific factor to an actual racial disparity in stage-at-diagnosis. 2000 bootstrap samples from our training patients were used to compute the 95% confidence intervals (CIs) of these contributions.

Results: Using a causal-effect contribution analysis, the relative contributions of each patient-specific factor to the actual racial disparity in stage-at-diagnosis were as follows: tumor grade, 45.1% (95% CI: 44.5%, 45.8%); hormone receptor status, 5.0% (4.5%, 5.4%); mammography utilization, 23.1% (22.4%, 24.0%); and biopsy delay 26.8% (26.1%, 27.3%).

Conclusion: The modifiable mechanisms of mammography utilization and biopsy delay drive about 49.9% of racial difference in stage-at-diagnosis, potentially guiding more targeted interventions to eliminate cancer outcome disparities.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-021-00165-5.

Keywords: Breast cancer; Counterfactual inference; Naïve Bayesian network; Racial disparity; Stage-at-diagnosis.

Fig. 1

The distribution of target patients’ biopsy delay based on five delay categories

Fig. 2

NBNs for AA patients with all factors in factual (a) and reference statuses (b) and NBNs for white patients with all factors in factual (c) and reference statuses (d)

Fig. 3

NBNs for AA (a) or white (c) patients with SM utilization in reference status and all its confounders in factual status; NBNs for AA (b) or white (d) patients with SM utilization in factual status and all its confounders in reference status

Fig. 4

Conditional probability table associated with node “Stage” in an NBN for white patients

Fig. 5

ROC surfaces to describe the prediction performances of the NBN (a) and ORM (b) for AA patients; ROC surfaces to indicate the prediction performances of the NBN (c) and ORM (d) for white patients