EGFR testing and erlotinib use in non-small cell lung cancer patients in Kentucky.
Kara L LarsonBin HuangQuan ChenThomas TuckerMarissa SchuhSusanne M ArnoldJill M KolesarPublished in: PloS one (2020)
This study determined the frequency and factors associated with EGFR testing rates and erlotinib treatment as well as associated survival outcomes in patients with non small cell lung cancer in Kentucky. Data from the Kentucky Cancer Registry (KCR) linked with health claims from Medicaid, Medicare and private insurance groups were evaluated. EGFR testing and erlotinib prescribing were identified using ICD-9 procedure codes and national drug codes in claims, respectively. Logistic regression analysis was performed to determine factors associated with EGFR testing and erlotinib prescribing. Cox-regression analysis was performed to determine factors associated with survival. EGFR mutation testing rates rose from 0.1% to 10.6% over the evaluated period while erlotinib use ranged from 3.4% to 5.4%. Factors associated with no EGFR testing were older age, male gender, enrollment in Medicaid or Medicare, smoking, and geographic region. Factors associated with not receiving erlotinib included older age, male gender, enrollment in Medicare or Medicaid, and living in moderate to high poverty. Survival analysis demonstrated EGFR testing or erlotinib use was associated with a higher likelihood of survival. EGFR testing and erlotinib prescribing were slow to be implemented in our predominantly rural state. While population-level factors likely contributed, patient factors, including geographic location (areas with high poverty rates and rural regions) and insurance type, were associated with lack of use, highlighting rural disparities in the implementation of cancer precision medicine.
Keyphrases
- epidermal growth factor receptor
- health insurance
- advanced non small cell lung cancer
- tyrosine kinase
- affordable care act
- small cell lung cancer
- primary care
- healthcare
- south africa
- mental health
- stem cells
- single cell
- cell therapy
- electronic health record
- machine learning
- quality improvement
- high intensity
- lymph node metastasis
- social media
- deep learning
- long term care