Shareef, Zan

Background: HIT has the potential to improve the health of individuals and the performance of providers, yielding improved quality, cost savings, and greater engagement by patients. Minorities face a variety of financial and nonfinancial barriers to obtaining appropriate and timely health care. Studying the behavior of minority patients will help understand the specific challenges to HIT adoption and engagement in patients and their caregivers. We undertook a patient-reported survey to explore these factors and understand any underlying disparities. Methods: A 21-question paper-based anonymous questionnaire addressing the responder's attitudes, understanding and needs of the electronic medical record (EMR) was used. This was administered to adult patients with lymphoid malignancies and their caregivers at two campuses of Mayo Clinic in Florida (San Pablo and inner-city; Riverside). Categorical and continuous variables between white and minority responders as well as minority responders from the two campuses were compared using the Chi-square test and U Mann Whitney with a significance level of 0.05. Results: The survey was completed by 1004 patients and caregivers. Average time taken to complete the survey was approximately 11 minutes. Of the responders, 716 (71%) were whites and 271 (27%) were minorities, with race-ethnicity not reported by 17 (2%). Among minorities, 30% (n=80) were seen at San Pablo and 64% (n=173) at the Riverside campus, with 6% at a multiple myeloma patient-caregiver symposium. Whites were older (median age 66 vs. 62 years, p<0.001) and had higher education (undergraduate or higher 60% vs. 52%, p=0.023) as compared to minorities. Minority survey responders had lesser access to a computer (84% vs. 92%, p<0.001) or a smartphone (85% vs. 89%, p=0.08). (Figure 1a) Exploring HIT awareness showed that only 51% of minorities were registered to use the EMR as compared to 72% whites (p<0.001) and a lesser number of minorities even knew that they could access their medical records electronically (81% vs. 92%, p<0.001). even among those registered to use the EMR, 13% minorities reported never using the EMR as compared to 4% whites (p<0.001). Encouragingly, a higher willingness to learn about and use the EMR was noted among minorities who were more interested in knowing how to access the EMR (43% vs. 36%, p=0.04), would increase the use of EMR if someone explained how to use it (17% vs. 10%, p=0.002), and if someone explained about test results (14% vs. 6%, p<0.001). (Figure 1b) Comparing minorities among the two campuses, those from the San Pablo campus were older (67.5 yrs. vs. 57 yrs., P<0.001), had higher level of education (undergraduate or higher in 66% vs. 47%, p=0.005) and a higher number had access to a computer (94% vs. 78%, P=0.001) as compared to those from the inner-city Riverside. (Figure 2a) Minorities from the Riverside campus were less likely to be aware of the current HIT EMR (75% vs. 93%, p<0.001), were significantly less likely to have registered for the patient EMR portal (38% vs. 76%, p<0.001) and never used the EMR even if registered for it (22% vs. 7%, p=0.01) as against those from the San Pablo campus. Minorities at the Riverside campus had a higher interest in knowing how to access the EMR (47% vs. 33%, p=0.03). Linguistic barriers were more striking in the inner-city Riverside campus where a higher proportion of minority survey responders noted that it would help if the EMR was in their primary language (66% vs. 48%, p=0.006). (Figure 2b) Conclusions: Minorities continue to struggle with suboptimal utilization of healthcare system including that of HIT. There are barriers related to access, awareness, language and understanding of HIT that prevent the integration and participation of racial-ethnic minorities in the healthcare system to a full extent. It is important to qualify the minorities better, since there exist HIT utilization disparities even within the minorities from different areas representing socioeconomic and demographic differences. Efforts need to be made at several levels to help the minorities overcome educational, access and linguistic barriers so that they can benefit from advancements in the healthcare system. Disclosures Ailawadhi: Celgene: Consultancy; Amgen: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Pharmacyclics: Research Funding; Cellectar: Research Funding; Takeda: Consultancy. Manochakian:Novocure: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees; AstraZeneca: Membership on an entity's Board of Directors or advisory committees; Guardant Health: Membership on an entity's Board of Directors or advisory committees. Chanan-Khan:AbbVie: Research Funding; Xencor: Research Funding; Pharmacyclics: Research Funding; Merck: Research Funding; Jansen: Research Funding; Mayo Clinic: Employment; Ascentage: Research Funding; Millennium: Research Funding.

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.

Abstract Background: Therapeutic advancements have led to significant improvement in outcomes for MM patients. Revised diagnostic criteria from International Myeloma Working Group focus on initiating treatment before the onset of end-organ damage. Thus, timeliness of initial therapy can significantly improve patient morbidity and outcomes. Such an analysis has not been reported for MM. Methods: We analyzed patients diagnosed with MM between 2004-2015 from the National Cancer Database (NCDB). Patients who received systemic treatment any time within the first year of diagnosis were included; those treated later than one year were considered possibly initially diagnosed with smoldering myeloma and hence, excluded. Time to initial treatment was divided into centiles with the 4 centiles analyzed being 0-7 days, 8-18 days, 19-37 days and >37 days. We performed univariate and multivariate analyses to compare sociodemographic and clinical factors influencing patient distribution across these timeliness categories and trends over time. Results: A total of 53,665 patients were included with 15,282 in 0-7 days, 12,462 in 8-18 days, 12,723 in 19-37 days and 13,198 in the >37 days treatment categories. Distribution across these centiles over time is shown in Figure. Univariate analyses showed a significant difference in distribution across the timeliness categories by year of diagnosis, patient age, gender, race/ethnicity, education level, insurance type, comorbidity score, treatment facility type, geographical location and distance to treating facility (all p<0.001) but no effect of income level. Multivariate analysis showed that there was a significantly higher likelihood of being in 4th centile (>37 days to treatment) for women (p<0.001), African Americans (p<0.001) or Hispanics (p=0.03; reference: Whites) or diagnosed in more recent years (p<0.001). Patients were less likely to be in the 4th centile if they were ≥80 years (HR 0.89, 95% CI 0.81-0.98, p=0.02), had >0 Charlson comorbidity score (p<0.001), were uninsured (p=0.02) or had Medicaid (p=0.03) as compared to having private insurance, were in non-community treatment facilities or geographical location other than Northeast (p<0.001). Patient education, income level or distance from treating facility was no longer significant. Conclusions: Several factors were found to affect timeliness of treatment in MM. We noted relatively faster time to initial treatment in elderly, uninsured or those who were treated in academic/comprehensive cancer programs but a delay in treatment for women and racial/ethnic minorities among other factors. These factors need to be addressed for equitable access and treatment practices and to realize improved outcomes for all. Figure. Figure. Disclosures Ailawadhi: Amgen: Consultancy; Celgene: Consultancy; Janssen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: Racial disparities in outcomes of cancer patients have been reported. Access to comprehensive cancer centers is associated with improved overall survival (OS) but racial/ethnic minorities may have a disparate access to such care. While the impact of treatment facility volume on outcomes has been evaluated, outcomes of centers with minority-predominant patient population have not been studied. We compared demographic profiles, facility level data and OS of patients treated at minority-predominant facilities to facilities that treated predominantly non-Hispanic Whites (NHW) with non-DLBCL. Methods: The National Cancer Database (NCDB) was used to identify all non-DLBCL patients diagnosed between 2004 and 2015. "Minority-treating facilities" were defined as facilities in the top decile by proportion for initial treatment of non-Hispanic African-Americans (NHAA), Hispanics and other races. We performed univariate and multivariate analyses to compare sociodemographic and clinical factors influencing outcomes between minority treating and non-minority treating facilities. A subgroup analysis stratified by race/ethnicity was also conducted to study the effect of treating facilities on the outcome of NHWs and minorities separately. Results: Of 1339 total facilities, 123 (9.1%) qualified as minority treating. Of 207,239 eligible patients in NCDB, 18,719 (9.03%) received treatment at the minority-treating facilities and of these, 11,190 (~60%) belonged to the minority races. Overall, 4.5% (6,988/156,664) NHWs and 30% (11,190/37,639) minorities received treatment at the minority-treating facilities. Several demographic and facility level characteristics were significantly different among the patients treated at minority-treating facilities as compared to non-minority treating facilities. Overall, significantly higher number of patients in minority-treating hospitals had lower income and education, had Medicaid coverage or lack of insurance. The OS of patients in minority treating facilities was significantly worse as compared to non-minority facilities (Figures). On multivariate analysis, patients who received treatment at minority-treating facilities were at 10% (HR=1.10, 95% CI: 1.06-1.14 p<0.001) higher risk of mortality as compared to those treated at the non-minority treating facilities. On multivariate analysis, NHAA (30% increased risk) and 'other races' (9% increased risk) were at significantly higher risk of mortality as compared to the NHW (Table 2). To study the effect of treatment at minority-treating facilities on OS among the patients of same race/ethnicity group, a multivariate analysis was also run separately for NHW and racial minorities. The NHWs who received treatment at minority-treating facilities were at 13% higher risk of death (HR=1.13, 95% CI: 1.08-1.19 p<0.001) as compared to NHWs who were treated at non-minority treating facilities. Similarly, the racial minorities who received treatment at minority treating facilities were at 8% higher risk of death (HR=1.08, 95% CI: 1.03-1.19 p=0.003) as compared to those who received treatment at the nonminority treating facilities. Conclusions:Outcomes of patients who received treatment at minority treating facilities was significantly worse than those at non-minority treating facilities. This was true for NHWs and racial minorities separately as well. Several demographic and facility level characteristics were significantly different in the two groups however OS remained worse after adjusting for them. Causes of poor outcomes at minority-treating facilities must be analyzed to mitigate them and improve outcomes for all. Figure. Figure. Disclosures Ailawadhi: Takeda: Consultancy; Celgene: Consultancy; Amgen: Consultancy; Janssen: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: The treatment of NHL has witnessed a paradigm shift over time, with targeted immunotherapy, stem cell transplant (SCT) among others. Historically, XRT was used quite frequently for the management of NHL but with advent of better systemic therapy, its utilization has changed. Trends and patterns of care for XRT use have never been formally reported. Methods: We identified patients with NHL diagnosis in the National Cancer Database (NCDB) between years 2004 and 2015. Demographic, clinical, facility level, initial treatment and outcome data were collected. The utilization of XRT in diffuse large B-cell (DLBCL) and non-DLBCL NHL were analyzed separately by univariate and multivariate analyses. To analyze the trends in the rates of XRT, we applied segmented linear regression to calculate the average annual percent change (AAPC) and 95% confidence Interval (CI) with a 'p' value. AAPC and CIs were calculated using the segmented package in R studio v1.1.49. Rest of the analyses was conducted using StataCorp version 15.1. Results: A total of 133,182 DLBCL and 204,933 non-DLBCL patients were identified. Among patients with DLBCL, 27,895 (20.9%) patients received RT. The rate of XRT declined from 25% in 2004 to 18.4% in 2015 with estimated AAPC of -0.59% (95%CI: -0.70- -0.49), p= 0.03 (Figure 1). In a subgroup analysis, a similar decline in the rate of XRT was evident across all the age groups, combined stages I and II vs stages III and IV and nodal vs extra nodal DLBCL (Table 1). Among non DLBCL, 33,369 (16.3%) patients received XRT. There was a statistically significant decline in the rate of XRT from 18.03% in 2004 to 16.3% in 2014 with an AAPC of -0.26 (95%CI: -0.38- -0.14) p <0.001 (Figure 1). On the subgroup analysis, the utilization of XRT declined across all the studied subgroups except among patients aged 80 years and above (Table 1). On multivariate analysis, several demographic, clinical and facility level factors were found to be significantly associated with XRT utilization in NHL (Table 2). In particular, older age groups, racial/ethnic minorities, advanced stages, higher Charlson comorbidity scores and diagnosis in the more recent calendar years were associated with lesser chances of receiving RT. Extranodal DLBCL had lower utilization while extranodal non-DLBCL had increased XRT utilization. Conclusion: There has been a significant decline in the utilization of XRT among patients with NHL (DLBCL and non-DLBCL) in the US since 2004, likely due to introduction of practice changing systemic therapeutics. There is still significant heterogeneity noted in practice patterns regarding utilization of XRT across the US. Having more standardized guidelines will help streamline delivery of evidence-based patient care. Disclosures Ailawadhi: Celgene: Consultancy; Janssen: Consultancy; Amgen: Consultancy; Takeda: Consultancy; Pharmacyclics: Research Funding.

Abstract Background: Clinical trials are fundamental to advance therapeutics systematically and improve patient outcomes. Despite this, enrollment on clinical trials remains dismal in the United States (US) and is a constant focus of healthcare policy. We studied distribution of clinical trials for B-cell malignancies over time across the US and unique clinical trial opportunities i.e. individual clinical trials for the given diagnosis at a site that patients may have access to participate. Methods: We abstracted data from clinicaltrials.gov for all trials that had non-Hodgkin lymphoma (NHL) or multiple myeloma (MM) as an inclusion indication between 1999-2018. Clinical trial characteristics and distribution over US geographical divisions (West, Midwest, Northeast, and South) were studied, and differences were assessed by Chi-square test. Results: A total of 1930 trials were identified (NHL: 982, MM: 948), of which 483 were recruiting at the time of data abstraction (NHL: 250, MM: 233). Over the past 2 decades, 182691 patients were enrolled on the various trials (NHL: 81592, MM: 101099). Trials by phase of study included phase 1: 629, phase 1/2: 316, phase 2: 813, phase 2/3: 11 and phase 3: 161. Number of trials by phase separated by NHL and MM are shown in Figure 1. Of these, 197 trials were randomized (NHL: 67, MM: 130). Geographical distribution of trials by diagnosis type is shown in Figure 2. A total of 31806 unique trial opportunities were noted for MM and NHL, of which 9,513 were international and 22,293 were in the US, with a geographical distribution of 5080 in West, 8198 in Midwest, 3944 in Northeast, and 5071 in South. 4,883 of the unique trial opportunities were available at NCI/NCCN accredited sites and 17,410 were at non-NCI/NCCN sites in the US. Treatment characteristics of the trials included monoclonal antibodies in 1218, other targeted agents in 2641, stem cell transplant in 526, and other agents in 517 trials with several trials utilizing more than one of these therapeutic options. There was no statistically significant difference in the distribution of clinical trials by phase of study across various US geographical regions for MM (p=0.71), NHL (p=0.98) or combined MM+NHL (p=0.16). On the other hand, unique trial opportunities were significantly different by study phase and geographical distribution for MM, NHL or MM+NHL (all p<0.001) (Figure 3). Conclusions: Widespread access to clinical trials within a cancer diagnosis is imperative for generalizability of trial results. In a comprehensive, national analysis we noted that while it may appear that clinical trials are available across the US, sites where they are open are distributed unevenly, giving rise to a disparity in access to evidence-based therapeutic advancements for patients. Disclosures Ailawadhi: Janssen: Consultancy; Amgen: Consultancy; Pharmacyclics: Research Funding; Takeda: Consultancy; Celgene: Consultancy. Sher:Affimed: Research Funding.