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Social, economic factors contribute to OS outcomes among younger patients with AML
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Certain social factors — such as insurance status, marital status and county-level income — may independently affect survival outcomes among younger patients with acute myeloid leukemia, according to the results of retrospective analysis.
“Near all we know about what treatments work best and what factors affect outcomes of serious blood cancers come from selected patients entering clinical trials or participating on specific treatment programs in large institutions,” Luciano J. Costa, MD, PhD, associate professor of medicine at University of Alabama at Birmingham, told HemOnc Today. “However, we have to take this information and use it to treat all the patients. As a consequence, results in real life are often worse than what we believe they are and other factors not identified in clinical trials — such as socioeconomic factors — may become relevant.”
Little research has been conducted regarding the impact of nonbiological factors on the long-term survival of patients with AML.
Costa and colleagues used the SEER database to evaluate biological and nonbiological factors for patients aged 19 to 64 years diagnosed with AML between 2007 and 2011. Researchers sought to determine the impact of nonbiological factors — including education level, income, relationship status and insurance status — alongside certain biological factors, such as disease subtype, sex, age and race.
The study included data from 5,541 patients with AML (median age, 53 years; 52.9% male). After a median follow-up for survivors of 19 months, the cohort had a median OS of 16 months (95% CI, 14.9-17).
Results of a multivariate analysis showed being a Medicaid beneficiary (HR = 1.24; 95% CI, 1.13-1.37) or uninsured (HR = 1.24; 95% CI, 1.07-1.44), being single (HR = 1.26; 95% CI, 1.15-1.38) or divorced (HR = 1.16; 95% CI, 1.04-1.3) and residing in a county within the lower three quintiles of median household income (quintile 3, HR = 1.25; 95% CI, 1.11-1.4) each were independently associated with increased mortality risk.
Patients with private insurance had a median survival of 17 months, whereas patients without insurance or those with Medicaid each had a median survival of 13 months. Median survival was 16 months for single and married patients, but 13 months for divorced patients and 10 months for widowed patients. Patients residing in the first quintile of median household income — or those with the lowest median income — had a median survival of 14 months, whereas those in the fifth quintile had a median survival of 22 months.
Researchers hypothesized that early death — defined as death within 2 months following diagnosis — may be a surrogate for access to care or late presentation. County-level income (quintile 1, OR = 1.6; 95% CI, 1.2-2.13) and insurance status (Medicaid, OR = 1.34; 95% CI, 1.07-1.66; uninsured, OR = 1.99; 95% CI, 1.46-2.73) — but not marital status — increased the risk for early mortality.
Researchers also hypothesized that late mortality may be a surrogate for access to post-remission therapy and stem cell transplantation. County-level income (P = .021), health insurance (P = .001) and marital status (P < .001) independently predicted survival in an analysis limited to patients who survived at least 2 months following diagnosis.
Thus, researchers surmised nonbiological factors affect early and late mortality.
To demonstrate that effects of nonbiological factors were not exclusively related to a lack of therapy, an analysis restricted to patients known to have received chemotherapy (n = 4,913) demonstrated these nonbiological factors remained predictive of survival.
The researchers performed an age-stratified analysis, dividing patients into cohorts aged 19 to 49 years (n = 2,276) and 50 to 64 years (n = 3,265). County-level income, marital status and insurance status continued to remain predictive in both age groups; however, being divorced appeared associated with worse outcomes among patients aged 50 to 64 years.
“We are also moving into an era where the performance of different care delivery systems is being carefully scrutinized and will affect reputation and even payments,” Costa said. “Performance is measured in terms of actual results vs. expected results based on the characteristics of the patients you treat. It is important therefore that we identify the effect of those not so obvious factors on outcomes and account for them when establishing performance benchmarks. Otherwise, systems that care for more disadvantaged patients may be punished for apparent worse performance.” – by Cameron Kelsall
For more information:
Luciano J. Costa, MD, PhD, can be reached at University of Alabama at Birmingham, 1720 2nd Avenue South, NP 2552, Birmingham, AL 35294-3300; email: ljcosta@uabmc.edu.
Disclosure: The researchers report no relevant financial disclosure.
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