VA ASSIST Project

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Objectives: Using a national random sample of veterans diagnosed with colorectal and nonsmall cell lung cancer drawn from the VA national cancer registry, we will use the Cancer Quality-ASSIST indicator set to evaluate the overall quality of veterans' supportive cancer care, as well as: 1. Patient-level factors and facility factors that may be associated with overall quality, and 2. Whether palliative care services use is associated with higher quality supportive care. Research Plan: We will use the VA national cancer registry and link it to Austin data in order to identify approximately 750 veterans with advanced lung and colorectal cancer. We will conduct remote chart abstraction using the VA electronic medical record and an Access graphical user data entry tool. Methodology: A cohort of veterans with advanced cancer will be developed by obtaining data about incident cases in 2008 with metastatic disease. We will also obtain data about earlier stage incident cases for the previous 2 years. We will link these files with Austin data (patient treatment files) to determine preliminary eligibility (based on inpatient, outpatient usage and disease status). PTF files will provide patient data to supplement patient characteristics obtained from chart review. We will link patient data to KLFMenu files to characterize facilities. Following revision of the Cancer Quality ASSIST guidelines and quality indicator abstraction tool,oncology and other experienced nurses with a background in chart abstraction will train using the ASSIST guidelines and protocol to achieve an initial kappa for key variables of 0.8 or higher. Nurses will follow an abstraction protocol of approximately 2.75 hours / case to evaluate the quality of symptoms related to cancer and its complications, treatment-related toxicities, and information and care planning needs. Palliative care service use and descriptions will be obtained from chart review. Analyses will evaluate the overall quality of supportive cancer care, and control for age, gender, type of cancer, stage, co-morbidity, as well as the analytic variables of race / ethnicity, serious mental illnesses, and palliative care service use. Complete models will control for patient and facility factors. Given likely selection effects, we will explore the use of treatment selection models including propensity matching and instrumental variable analysis to evaluate the effect of service use. We will attempt multivariable linear regression and explore count data models for main models and consider evaluating facility effects with fixed effects and multi-level approaches. Results: This is a new project and results have not yet been obtained. Significance: Information on the quality of supportive care management, veterans who are particularly vulnerable and may receive lower quality supportive care, and the effectiveness of palliative care services in addressing these issues is urgently needed to information programmatic development, and other research and clinical priorities.