Timeliness of Initial Therapy in Multiple Myeloma: Trends and Factors Affecting Patient Care

Abstract

Multiple myeloma (MM) treatment has advanced significantly over the last 2 decades. In most patients, the disease course has been altered from early fatality to chronic morbidity with multiple lines of treatment. The MM treatment paradigm has shifted toward treating patients before end-organ damage occurs. Thus, timeliness of treatment initiation in this era might improve patient outcomes. This is the first report to our knowledge analyzing disparities and trends in treatment timeliness of patients with MM using the National Cancer Database. Multiple factors affected the timing of treatment initiation in MM and disparities were found. We noted that initiation of treatment was delayed in women (odds ratio [OR], 1.15; 95% CI, 1.1 to 1.2) and blacks (OR, 1.21; 95% CI, 1.14 to 1.28; reference, whites) and in patients diagnosed in more recent years (2012-2015; OR, 1.15; 95% CI, 1.1 to 1.22; reference, 2004-2007). Patients were likely to start treatment earlier if they were age ≥ 80 years (OR, 0.83; 95% CI, 0.76 to 0.9; reference, age < 60 years), were uninsured (OR, 0.81; 95% CI, 0.72 to 0.91; reference, private insurance), had Medicaid (OR, 0.87; 95% CI, 0.79 to 0.95; reference, private insurance), were treated in a comprehensive community cancer program (OR, 0.7; 95% CI, 0.65 to 0.77; reference, community cancer program), lived in a location other than the US Northeast, or had a higher Charlson comorbidity score. Patient education and income levels did not affect time to treatment initiation. Particular aspects of these disparities could be explained by our current health care system and insurance rules, whereas others need to be investigated more deeply.

Influence of Social Determinants of Health on Timeliness to Treatment for Metastatic HCC and the Impact of Affordable Care Act

“…A retrospective analysis using de-identi ed data accessed from the NCDB was performed. 9 The study was exempt from Institutional Review Board (IRB) oversight and did not require ethics approval. The NCDB was queried for patients diagnosed between 2004-2015 with stage IV hepatocellular carcinoma HCC (Histology code 8170, AJCC pathologic stage IV).…”

“…We excluded patients with NCDB class of case categories other than 10-14 (patients who had a diagnosis at the reporting facility and all treatment or a decision not to treat was done elsewhere), as has been previously utilized and reported from the NCDB to minimize reporting errors. 9 Race was recategorized into four categories -non-Hispanic Whites (NHW), non-Hispanic Blacks (NHB), Hispanics (H) and others. Comorbidity was captured using the Charlson/Deyo comorbidity index.…”

“…The Charlson/Deyo value is a weighted score derived from the sum of the scores for each of the comorbid conditions listed in the Charlson Comorbidity Score. 9,10 Other sociodemographic variables studied included -gender (male and female), educational status represented in terms of quartiles of the percentage of persons with less than a high school education and median household income -both according to the residents' census tract. Locations were assigned based on data provided by the United States Department of Agriculture Economic Research Service and categorized as rural, urban or metropolitan.…”

Initial treatment of patients with thyroid cancer: Outcomes and factors associated with care at academic versus nonacademic cancer centers

“…Adult patients with a diagnosis of 1 of the 5 histopathologic subtypes of TC identified from the NCDB database from 2004 to 2013 were included. We excluded patients without a specified pathology subtype for the TC diagnosis, those without specification of whether they received treatment at an AC versus a NAC, those with stage 0 disease, and those not having a NCDB class of case 10 to 14 (patients who had a diagnosis at the reporting facility, and all treatment or a decision not to treat was done elsewhere), as has been previously used and reported from the NCDB 12,13 . We performed univariate and multivariate analyses to compare socioeconomic and demographic factors influencing patient distribution across a treatment facility category of an AC versus a NAC.…”

“…Described odds ratios (ORs) predicted the likelihood of patients receiving treatment at these facility types. Data collection methods for the NCDB have been described previously 12,13 …”