Regional Adoption of Commercial Gene Expression Testing for Prostate Cancer

Abstract

Although tissue-based genomic tests can aid in treatment decision-making for patients with prostate cancer, little is known about their clinical adoption. To evaluate regional adoption of genomic testing for prostate cancer and understand common trajectories of uptake shared by regions. This dynamic cohort study of patients diagnosed with prostate cancer used administrative claims from Blue Cross Blue Shield Axis, the largest source of commercial health insurance in the US, to characterize temporal trends in the use of commercial, tissue-based genomic testing and calculate the proportion of tested patients at the hospital referral region (HRR) level. Eligible patients from July 1, 2012, through June 30, 2018, were those aged 40 to 89 years with prostate cancer diagnosed from July 1, 2012, through June 30, 2018. Group-based trajectory modeling was used to classify regions according to discrete trajectories of adoption of commercial, tissue-based genomic testing for prostate cancer. Across regions with distinct trajectories, HRR-level sociodemographic and health care contextual characteristics were compared, using data previously calculated among Medicare beneficiaries. A total of 92 418 men with prostate cancer who met inclusion criteria were identified; the median (interquartile range) age at diagnosis was 60 (56-63) years. Overall, the proportion of patients who received genomic testing increased from 0.8% in July 2012 to June 2013 to 11.3% in July 2017 to June 2018. Trajectory modeling identified 5 distinct regional trajectories of genomic testing adoption. Although less than 1% of patients in each group were tested at baseline, group 1 (lowest adoption) increased to 4.0%. Groups 2 (7.8%), 3 (14.6%), and 4 (17.3%) experienced more modest growth, while in group 5 (highest adoption), use increased to 33.8% of patients tested from June 2017 to July 2018. Compared with regions that more slowly adopted testing, HRRs with the highest rate of adoption (group 5) had higher HRR-level education measures (percentage [SD] with college education: group 1, 25.6% [4.8%]; vs group 2, 27.5% [7.3%]; vs group 3, 30.3% [9.1%]; vs group 4, 29.8% [8.2%]; vs group 5, 30.4% [11.4%]; P for trend = .03), median (SD) household income (group 1, $50 412.8 [$6907.4]; vs group 2, $54 419.6 [$11 324.5]; vs group 3, $61 424.0 [$17 723.8]; vs group 4, $58 508.3 [$15 174.6]; vs group 5, $58 367.0 [$13 180.5]; P for trend = .005), and prostate cancer resources, including clinician density (No. [SD] of clinicians per 100 000: group 1, 2.5 [0.3]; vs group 2, 2.5 [0.5]; vs group 3, 2.6 [0.5]; vs group 4, 2.7 [0.7]; vs group 5, 2.6 [0.5]; P for trend = .04) and prostate cancer screening (percentage [SD] of prostate-specific antigen testing among patients aged 68-74 y: group 1, 29.4% [11.8%]; vs group 2, 32.4% [11.2%]; vs group 3, 33.1% [12.7%]; vs group 4, 36.1% [9.7%]; vs group 5, 28.8% [11.8%]; P for trend = .05). In this cohort study of patients with prostate cancer, the adoption of commercial tissue-based genomic testing for prostate cancer was highly variable in the US at the regional level and may be associated with contextual measures related to socioeconomic status and patterns of prostate cancer care. These findings highlight factors underlying differential adoption of prognostic technologies for patients with cancer.