Date of Graduation


Document Type


Degree Type



School of Pharmacy


Pharmaceutical Systems and Policy

Committee Chair

Nilanjana Dwibedi

Committee Co-Chair

Usha Sambamoorthi

Committee Member

Traci LeMasters

Committee Member

Chan Shen

Committee Member

Amit Ladani


In the United States (US), 25% of healthcare spending is considered wasteful because it is spent reimbursing low-value care. Low-value care is the utilization of healthcare services, medical tests, and procedures that have unclear or no clinical benefit to patients but still exposes them to risk. World-wide, low-value care imposes a significant economic burden on patients, payers, governments, and society. Cancer care among older adults > 65 years is one of the biggest drivers of healthcare expenditure in the US and accounts for nearly 40% of all spending, and low-value care among cancer patients is prevalent and contributes to the financial toxicity of cancer treatment. To date, no study has examined the risk of low-value non-cancer care among patients with cancer. There is a critical need to assess the prevalence of low-value non-cancer care in patients diagnosed with cancer and the risk factors associated with the receipt of low-value care. To fill the knowledge gap, the three related aims of this dissertation were (1) To assess the association of incident cancer (breast, prostate, colorectal, and Non-Hodgkin’s lymphoma) to low-value non-cancer care among older Medicare beneficiaries using machine learning methods. (2) To assess the association of incident cancer (breast, colorectal, prostate, ovarian, uterine, and Non-Hodgkin’s lymphoma) to annual wellness visit utilization among older adults using machine learning methods. (3) To examine the association of low-value care to economic burden (out-of-pocket expenditure) of older cancer survivors (breast, prostate, colorectal, and Non-Hodgkin’s lymphoma) using machine-learning methods. The study used a retrospective cohort study design, leveraging multiple years (2005-2015) of the cancer registry data from the Surveillance, Epidemiology and End Results (SEER) program linked with the Medicare claims data, the American community survey census tract files, and the Area Health Resource Files. In the first aim, among elderly Medicare beneficiaries with incident breast, colorectal, prostate cancers, and non-Hodgkin’s lymphoma (N = 329,267) the rates of low-value care differed significantly by cancer type; the highest rates were observed in Non-Hodgkin’s lymphoma (34%) followed by colorectal cancer (29% ) while the lowest rates were among patients diagnosed with prostate cancer (22%). The most used low-value care services were population-based screening for detection of carotid artery disease (10%), low-value MRI for low back pain (9.8%), traction for low back pain (5%); the association of cancer to low-value care varied by cancer type; both colorectal cancer and NHL were positively associated with low-value care, but breast and prostate cancers were negatively associated with low-value care. In the Second aim, among elderly Medicare beneficiaries diagnosed with incident breast, colorectal, prostate, ovarian, uterine cancer, or Non-Hodgkin’s lymphoma in 2014 (N = 36,447), only one in five eligible adults received an AWV in 2015. Overall, 16.5% of the cohort had an AWV in 2015. The AWV rate in the non-cancer cohort was 16.7%. AWV rates were high among individuals with breast (20%) and prostate (19%) cancer, followed by uterine cancer (16%) and NHL (13%). The lowest rates were among individuals with ovarian cancer (7.5%). In the third aim, among elderly Medicare beneficiaries diagnosed with incident breast, colorectal, prostate and non-Hodgkin’s lymphoma (N = 27,067), Individuals who received one or more low-value procedure had significantly higher mean out-of-pocket expenditure ($8,726±$7,214) vs. ($6,802±$6,102) compared to those who did not have low-value care in the follow-up period. On average, patients who received a low-value procedure experienced between $1,000 and $2,000 higher out-of-pocket expenditure attributable to low-value care. The machine learning models identified low-value care, fragmentation of care, and a higher number of pre-existing chronic conditions to be the most important factors driving excess out-of-pocket expenditure.

Embargo Reason

Publication Pending