ALBERT

Description: Background: The increasing incidence in multiple myeloma (MM), coupled with improved efficacy of therapies, warrant population-based studies of diagnosis delay which could lead to disease-related complications. Methods: We analyzed tumor registry data and provider claims in the SEER-Medicare database, a geographically and racially diverse sample of Medicare enrollees with confirmed MM. Using extant selection criteria to study eligible MM patients, we identified claims with anemia (An) or back pain (BP) codes, conditions related to MM. We estimated the period of time between first claim for An or BP and confirmed MM diagnosis. We defined delay as ≥98 days (the median) between initial An or BP claim and MM diagnosis, and estimated logit models to predict sociodemographic and clinical factors associated with delay. We also estimated logit models to predict the likelihood of renal or skeletal complications following MM diagnosis. The study period was one year before, through six months following, diagnosis. Analyses were adjusted for a known 30-day lag time in MM diagnosis dates in SEER. Results: From the analytic sample of 5,185 patients, 3,600 had a diagnosis for An (2,576) and/or BP (2,012) prior to MM diagnosis. Male gender (OR 0.73, 95% CI 0.64–0.84) and northeast region (0.79, 0.65–0.96) decreased the likelihood of delay. Over 60% of patients had ≥ 1 Charlson comorbidity; higher Charlson scores and the presence of both An and BP increased likelihood of delay, but were strongly correlated, and an interaction term with these variables in the model was significant. Of the 5,112 patients with no history of renal or skeletal complications, 1,742 (34%) experienced a complication within six months after MM diagnosis. After adjustment for gender, age, race, and subsequent chemotherapy use, patients with: higher Charlson scores (1.17, 1.03–1.33); initial diagnosis during an inpatient stay (2.40, 1.92–3.00), and; no UPEP, SPEP, or bone marrow biopsy as part of the MM workup (1.68, 1.48–1.92) were more likely to experience a complication (29% of patients had neither PEP nor biopsies recorded). Delay between An or BP diagnosis and MM was not associated with likelihood of complication (0.96, 0.91–1.01). Conclusions: Despite the presence of two classic signs and symptoms of MM, gender and geography significantly influence diagnosis delays. The surprising finding that both anemia and back pain delay diagnosis is partly explained by increased comorbidity in these patients. These findings highlight the importance of considering MM as part of the routine workup for anemia in elderly patients. The existence of comorbidity in these patients may obscure the clinical presentation, resulting in delayed diagnosis. Underuse of common tests to diagnose myeloma may result in poor outcomes, and could be a focus of quality improvement efforts.

Description: Abstract 1369 Poster Board I-391 Background: Timeliness of diagnosis is a quality of care measure endorsed by the Institute of Medicine. Clinical outcomes for patients with chronic myelogenous leukemia (CML) are better when tyrosine kinase inhibitors, such as imatinib, are initiated in the early stages of disease. However, the patterns of care surrounding the CML diagnosis period, as well as the relationship between diagnosis delay and overall survival in the pre-imatinib era, are unknown. Methods: The Surveillance, Epidemiology and End Results (SEER)-Medicare linked database was used to identify traditional Medicare enrollees diagnosed with CML during 1991 through May 2001 (prior to the FDA approval of imatinib). Both inpatient and outpatient claims were analyzed from one year before, through six months following, the SEER diagnosis date. Signs, symptoms, and diagnostic studies commonly encountered in CML diagnoses were identified by CPT procedure and ICD-9 diagnosis and procedure codes. We calculated the time between the first visit for a sign or symptom and the SEER diagnosis date, and defined this time period as ‘diagnostic delay’ if it met or exceeded the median number of days for the sample. An accelerated failure time model examined variables associated with diagnostic delay. Overall survival was examined using a Cox proportional hazards model. Analyses were adjusted to account for a possible lag time in SEER cancer diagnosis dates, as well as year of diagnosis. Results: We studied 768 patients who met eligibility criteria. The most frequent signs and symptoms prior to CML diagnosis were infection (29.4%), anemia (22.4%), leukocytosis (13.4%) and fatigue (11.9%). The median time between any sign or symptom and CML diagnosis date in SEER was 90 days (interquartile range = 270). The median survival time was 3.5 years. The time between sign or symptom and CML diagnosis was increased for patients with at least one comorbidity (β=0.83, p 〈 .001), and for those diagnosed at age 75 or greater (β=0.30, p 〈 .05). Males had shortened times to diagnosis (β=-0.41, p 〈 .01). Diagnostic delay was not a significant predictor of overall survival (HR = 1.04, 95% CI = 0.88-1.23). Conclusions: The most common signs and symptoms older patients experience prior to CML diagnosis are nonspecific, which may impair diagnostic efforts. Prior to the approval and general availability of imatinib, differences in timeliness of diagnosis were observed by age, gender, and presence of comorbidities. Examination of patient-provider interactions stratified by these variables may aid efforts to standardize the diagnostic process, although diagnostic delay was not significantly associated with overall survival in the pre-imatinib era. Disclosures: No relevant conflicts of interest to declare.

Description: Abstract 3830 Background: Although the primary care physician (PCP) is often the first point of contact for patients with suspected hematologic malignancy, little is known about hematologic referrals from primary care, including their frequency, the factors that affect choice of specialist, and the quality of information exchanged. Methods: In April 2010, we administered a 34-item questionnaire to a random sample of 190 physicians in the state of Massachusetts identified as PCPs (family practice, general practice, or internal medicine) in the American Medical Association's physician file. PCPs were given the opportunity to complete the survey via post or Internet. An additional mailing was sent to non-respondents, followed by at least two attempts at telephone contact. Physicians were asked for the approximate number of patients seen in the past year with suspected hematologic malignancy, the frequency of formal specialty referral, and informal “curbside” referral. PCPs were also queried about the factors that influence their choice of specialist, and about the information exchange with the specialist; these measures were then analyzed by self-reported PCP characteristics using chi-square statistics. Results: As of August, 2010, 118 physicians had responded (response rate = 62.1%). 67.8% identified themselves as internists, and 61.9% were male. The median reported patient panel size during the prior 12 months was 1800; median percentage of patients ≥ 65 years was 30.0%; median percentage of patients in managed care was 55.0%; and median year of graduation from residency, 1996. PCPs were evenly distributed with respect to academic affiliation (from no affiliation to full-time faculty). The median number (IQR) of patients in the prior 12 months who were suspected of having hematologic malignancy was 5 (3, 10). Among suspected hematologic malignancies, the median number formally referred to a specialist (hematologist or surgeon) was 5 (3, 10), and the median number who received informal “curbside” consult was 0 (0, 0.5). Respondents rated the importance of several factors in their choice of specialist (1 = not important at all to 5 = extremely important). Those factors rated ≥ 3 included reputation of specialist/facility (94.9%), patient's preference for site of care (92.4%), distance of site from patient's home (89.8%), specialist's affiliation with a cancer center (88.1%), practice's affiliation with specialist (82.2%), personal relationship with specialist (79.7%), patient's ability to pay (67.0%), and availability of clinical trials at the referral site (63.6%). The following table summarizes responses to questions about flow of referral information and follow-up: Conclusions: Consultation for suspected hematologic malignancy from PCPs is relatively infrequent, tends to manifest through formal referral as opposed to informal discussion, and is most often affected by specialist reputation and patient preference for site of care. Only about half of our respondents reported providing the specialist with a referral letter or email, which may result in poor quality of referral information. Alternately, a high number reported giving a copy of abnormal test results to their patients to bring to the specialist, which may ameliorate this issue and reflect an ongoing evolution in the patient/provider partnership. Moreover, fairly often, patients have not been to see the specialist upon follow-up with their PCP. This finding seems to reflect patient cancellations rather than a failure in physician systems, suggesting that increases in patient education and personalized follow-up may be the best approach to ensure completion of timely hematologic referrals. Disclosures: No relevant conflicts of interest to declare.

Description: Background: Hematologic malignancies have been associated with poor performance on standard measures of quality of end-of-life (EOL) care in oncology (eg, Hui, Cancer, 2014); however, these measures were originally developed primarily for solid tumors, and they may not appropriately address EOL quality issues for patients with blood cancers. We sought to explore hematologic oncologists' perspectives regarding the acceptability of current oncology EOL quality measures, hypothesizing that they would report them to be largely unacceptable. Methods: In 2014, we mailed a 30-item survey to a national sample of hematologic oncologists randomly selected from the American Society of Hematology clinical directory. The survey was developed through focus groups (n=20) and cognitive debriefing (n=5) with hematologists whose practices focus on patients with blood cancers. In the resulting survey, we provided a list of standard EOL quality measures (Earle, JCO, 2003; Keating, Cancer, 2010; Phelps, JAMA, 2009; see table) and two novel hematology-based measures (no red cell transfusions ≤ 7 days before death, and no platelet transfusions ≤ 7 days before death) and asked "Please indicate whether or not you feel each is an acceptable indicator of good quality EOL care for patients with hematologic malignancies." We decided a priori that we would consider a measure to be "highly acceptable" if there were at least 75% agreement among hematologic oncologists on its acceptability. Worrying that they might reject them all, we also asked them to identify three measures they would choose in a scenario where three had to be adopted. Results: We received 349 surveys from 48 states (response rate: 57.3%). Non-responders were not significantly different across known variables (gender and region of practice). Among respondents, median age was 52 years, median time in practice was 25 years, and 43% practiced primarily in tertiary centers. Eighty-seven percent were board-certified in oncology, 81% in hematology, and 71% in both specialties. The table below shows acceptability of the quality measures as rated by respondents. In the exercise where three measures had to be chosen, the one chosen most often was no CPR within 30 days of death (54%), followed by enrollment in hospice 〉7 days before death (46%). Conclusions: In contrast to our hypothesis, all of the measures we presented were considered acceptable by a substantial proportion of the hematologic oncologists in our national cohort. Moreover, while four of the measures reached our a priori designation of being highly acceptable, the two hematology-focused measures did not meet this same threshold. These data suggest that in hematologic oncology, resources should be directed towards addressing barriers to performance on established EOL quality measures in addition to creating new ones. Figure 1. Figure 1. Disclosures No relevant conflicts of interest to declare.

Description: Introduction The use of surveillance CT imaging in patients with DLBCL in remission is neither effective to detect recurrence nor cost-effective. The ASH Choosing Wisely (CW) campaign, in particular, emphasizes the lack of benefit in imaging beyond 2-years of completion of therapy. We sought to describe the practice of surveillance imaging and predictors of this practice. Methods We used population-based health system administrative databases from Ontario, Canada. We studied a cohort of all adult patients ≥18 with diffuse large B-cell lymphoma who received R-CHOP therapy for curative intent between January 1, 2004 to June 30, 2011. Based on the CW campaign, we defined an index date of 2-years after the last dose of R-CHOP as the time-frame beyond which surveillance CT imaging would be inappropriate. The cumulative incidence of receiving CT scans within 3 years after the index date (i.e. from 2- to 5-years beyond the end of treatment) represented the primary outcome of interest (established within the Ontario Health Insurance Program database). To ensure that only surveillance scans in asymptomatic patients were captured, patients were censored 6 months prior to development of recurrent disease or a new cancer diagnosis, further chemotherapy/radiation, or if they died (censored at time of death). Predictor characteristics included baseline comorbidities (John Hopkins weighted Aggregated Diagnosis Groups (ADG) comorbidity score) and income quintile (linkage of the patient postal code to Statistics Canada Census data on average household income by postal code). Results The cohort consisted of 2,838 patients treated with R-CHOP during the study period. Median age at time of first R-CHOP dosing was 63 years (IQR 52-72) and the median number of cycles received was 6 (IQR 6-8). The cumulative incidence of receiving CT imaging from the index date (2-years from end of treatment) to 3-years beyond the end of treatment was 40.1% (95% CI 38.3%-41.9%). The cumulative incidence of imaging from the index date to 5-years beyond the end of treatment was 55.6 % (95% CI 53.7%-57.5%). During the follow-up period, patients ≥65 were more likely to receive imaging than those aged

Description: Background: Azacitidine (AZA) use in higher-risk MDS has been adopted because it improves survival. Despite this, "real-world" data on the economic impact and resource utilization remains unknown. We used the Ontario provincial AZA MDS registry, which captures all AZA-treated patients in the province, to analyze "real-world" data on healthcare use, associated costs and their predictors in AZA treated higher-risk patients. Methods: We linked the provincial MDS AZA registry (single-payer/universal access), which captures baseline characteristics and treatment response for all AZA-treated patients in Ontario, to population-based health system administrative databases. Only higher-risk MDS patients (IPSS intermediate-2, high) and low blast count AML (21-30% blasts) treated from May 30, 2010 to March 16, 2015 were included. Patients were followed for 24 months following first AZA treatment and censored at the earliest of 90 days after last AZA treatment, date of death, time of acute leukemia induction/allogeneic stem cell transplant or March 31, 2016. We estimated healthcare resource utilization and the mean (and overall) standardized 28-day healthcare cost in Canadian dollars ($1 CDN = 0.76 USD$). Quantile regression was used to explore predictors of cost. Negative binomial regression models were used to explore predictors for higher rate of emergency department (ED) visits, and for longer length of stay, with the natural logarithm of length of follow-up as an offset variable in each model. Results: The registry had 652 higher-risk MDS and 225 low blast count AML patients (n = 877) with median follow up of 8 months (IQR 4-13). Median age was 73 years (IQR 66-79), 66.0% were male, 17.8% were secondary MDS and IPSS scores of those calculable were intermediate-2 (64.9%) and high-risk (35.1%). At the time of AZA initiation, 587 patients (66.9%) were transfusion dependent. The median number of cycles received was 6 (range 3 to 11) and median overall survival was 16.1 months (95% CI 13.9 to 18.3). Overall, 705 patients (80.4%) had at least 1 ED visit and 290 (33.1%) had an ED visit during their first cycle of AZA. In addition, 680 patients (77.5%) had at least 1 hospital admission with a mean hospital stay of 17.7 days (95% CI 16.3 to 19.1) over the entire study period. 141 patients (16.1%) required admission to an intensive care unit. Older age (Rate ratio [RR] = 1.33, 95% CI 1.09-1.62), rurality (RR=1.75, 95% CI 1.42-2.15), high IPSS score (RR=1.31, 95% CI 1.06-1.62), and increased comorbidity level were each independent predictors of increased ED visits; while higher comorbidity level (RR=1.51, 95% CI 1.08-2.11), high IPSS score (RR=1.39, 95% CI 1.01-1.92), and transfusion dependence (RR=1.51, 95% CI 1.13-2.01) were associated with longer hospital stays. The overall mean cost was $146,675 per patient (95% CI $139,537 to $153,812) including AZA and $103,580 (95% CI 98,675 to 108,486) excluding AZA drug costs. The mean standardized cost per 28-day period per patient was $17,638 (95% CI $16, 870 to $18,407) with AZA and $13,450 (95% CI $12,730 to $14,170) without AZA drug costs. Inpatient admissions ($4,631, 95% CI $4,010 to $5,251) and non-physician outpatient cancer clinic costs ($6,092, 95% CI $5,851 to $6,333) were the major cost drivers. Excluding AZA costs, the mean standardized 28-day costs were higher in those receiving less than 4 cycles of AZA (n= 295) at $19,408 (95% CI $17,568 to $21,248), compared with those receiving 4 or more cycles (n= 582) at $10,430 (95% CI $10,069 to $10,790) with inpatient admissions as the major driver (mean $10,192, 95% CI $8,594 to $ 10,192 vs. $1,812, 95% CI $1,558 to $2,065). On multivariable analysis, only greater comorbid disease burden (β = $2,074, 95% CI $665 to $3,483) and transfusion dependence (β = $2,402, 95% CI $1,190 to $3,613) were associated with higher median standardized 28-day cost. Conclusions: In our analysis of "real-world" patients with uniformly higher-risk MDS treated with AZA we demonstrate a significant economic impact above and beyond the cost of AZA alone. The costs are higher in patients who are transfusion dependent and have greater comorbidity and appear to be driven by inpatient care and outpatient non-physician ambulatory care. This group of patients are high users of healthcare resources with the majority having ED visits and inpatient admissions. These results will inform patients and providers about the "real-world" anticipated toxicities of AZA. Disclosures Buckstein: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding.

Description: Background: Acute myeloid leukemia (AML) is the most common type of leukemia among adults in the U.S. However, data on the effects of treatment on survival for large numbers of AML patients in clinical practice are limited. Methods: Using the linked SEER-Medicare database, we evaluated mortality for AML patients 65+ years of age diagnosed between 1999 and 2002 who received chemotherapy versus no chemotherapy. Patients were followed from diagnosis until death or the database cut-off (December 31, 2003). The presence of chemotherapy treatment was identified based on procedure codes and diagnosis-related groups. The two study cohorts were matched on age, sex, comorbidity (Deyo adaptation of the Charlson Comorbidity Index), geographical region, teaching hospital status, and myelodysplastic syndrome (MDS) in the prior 12 months. Mortality rates were estimated via the Kaplan-Meier technique. Subgroup analyses were performed by level of comorbidity, age group, and teaching hospital status. Results: A total of 3,317 elderly patients with AML who were eligible for fee-for-service Medicare coverage were identified. Of these, 1,193 (36%) received chemotherapy and 2,124 did not receive treatment. Treated patients were found to be younger and had fewer comorbidities compared to untreated patients. After matching, there were 888 patients in both cohorts. Mean age was 75 years and 59% were male. Fifty-three percent of patients received care at teaching hospitals. The median Charlson score was 1.0 and 24% were diagnosed with prior MDS. Median survival was 4.4 months longer for the treated cohort than for the untreated cohort (6.1 versus 1.7 months; P

Description: Background: Myeloproliferative neoplasms (MPNs) are chronic myeloid malignancies with markedly heterogeneous disease course, and are associated with underlying inflammatory states that promote development of thrombotic events and acquisition of comorbidities. There is poor understanding of health care resource utilization (HRU) and cost of treatment in patients with MPNs. Objectives:To estimate and compare the HRU and cost of treatment for MPN patients (Essential Thrombocythemia, ET; Polycythemia Vera, PV; and Myelofibrosis, MF) with matched controls, and investigate the impact of patient characteristics and health service factors on the cost of treatment. Study design: Retrospective, population-based, matched-cohort study, using provincial health databases of Ontario's single payer universal health system. Study population: Cases were individuals in the Ontario Cancer Registry, diagnosed with MPN (Total n= 7130; ET, n=3481; PV, n=2618; MF, n=1031), from 2004 to 2016. Controls were individuals in the general population of Ontario, without a diagnosis of MPN. Each case was matched with four controls on age, sex, geographical location, and neighborhood income quintile. Baseline parameters including thrombosis and other comorbidities were collected during two-years prior to the date of MPN diagnosis. The baseline comorbid disease burden was measured using the Aggregated Diagnostic Group (ADG) score with a larger number of ADGs representing a greater comorbid disease burden (https://www.johnshopkinssolutions.com/wp-content/uploads/2014/04/ACG-White-Paper-Applications-Dec-2012.pdf). Main outcome measures:For each case and its controls, direct medical costs were obtained by costing all health care-related resources and expressed as mean per person year costs ((2018 Canadian Dollars, $1 CDN = $0.76 USD) to adjust for variable length of follow-up. Linear regression analysis was performed to assess the impact of baseline factors on the cost of treatment for MPN and represented as rate ratios (95% CI). Results:The mean duration of follow-up in years (cases vs controls) was 3.9 vs 4.3 for ET; 3.9 vs 4.2 for PV and 3.2 vs 4.9 for MF. The total follow-up duration was 27449 person years for all MPN cases, and 124963 person years for all controls. Comorbidities (congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, stroke, chronic renal failure, chronic liver disease, and pre-diagnosis arterial and venous thromboses were significantly higher in cases as compared to controls (p6 months after the diagnosis incurred significantly lesser cost of treatment due to less comorbidity burden as noted by the lower ADG score for patients with 〉6 months vs

Description: Abstract 2084 BACKGROUND: Although the primary care physician (PCP) is often the first provider to diagnose anemia, little is known about the anemia workup in current clinical practice. Knowledge of current practices can inform efforts to improve anemia management in older adults, which has been recognized as a public health crisis by a combined ASH/National Institute of Aging blue ribbon panel. METHODS: From April to August 2010, we administered a 34-item questionnaire to a random sample of 190 Massachusetts physicians identified as PCPs (family practice, general practice, or internal medicine) in the American Medical Association's physician file. PCPs were given a vignette about a hypothetical patient asking “If you were to see a previously healthy patient during a routine physical with mild anemia (Hg 80% of normal) and no other symptoms, which of the following would you do?” PCPs were given 13 choices, but could also write in answers. In the next section, they were told “The patient with mild anemia presents two weeks later. The anemia is unchanged, but the patient has one new sign/symptom in the following list. For each of these as an isolated new finding, what would you do next?” and asked which of 11 signs/symptoms would prompt (1) imaging (2) referral to a hematologist (3) further follow-up. More than one choice was allowed. Results were analyzed descriptively, and significant differences in the second workup stage were identified using Wald chi-square statistics obtained from logistic regression models controlling for correlations of individual PCP responses. RESULTS: 134 PCPs responded (70.5%). 62.4% identified as internists; 58.7% were male. PCPs were evenly distributed with respect to level of academic affiliation. The median reported patient panel size during the prior 12 months was 1800; median percentage of patients ≥ 65 years was 30.0%; median percentage of patients in managed care 55.0%; and median year of graduation from residency, 1996. For the first stage of the workup, most PCPs reported they would send iron studies (93.2%), a differential (85.7%), and B12/folate (85.0%). Fewer would obtain a stool guaiac (69.2%), reticulocyte count (66.2%), or a serum protein electrophoresis (SPEP; 17.3%). At this first stage, 30.8% reported they would require a 2-week follow-up visit, 26.3% a colonoscopy, and 8.3% an EGD. Almost none would refer to a hematologist (3.8%) or obtain imaging (1.5%), and 12.0% wrote in “work-up depends on patient's age.” Reported subsequent actions with persistent anemia and one new sign or symptom were as follows: Among those patients mostly likely to be referred to a hematologist (those with pancytopenia, thrombocytopenia and leukopenia), PCPs reported recommending low levels of 2-week follow-up in addition to the referral (10.6%, 16.7% and 15.6% respectively). CONCLUSIONS: Use of the reticulocyte count, stool guaiac and SPEP were less frequent than might be expected in the first steps of the anemia work-up; in contrast, more than one-quarter of PCPs reported they would obtain a relatively expensive procedure (colonoscopy) as a first step. Signs and symptoms suggesting bone marrow failure most often prompted referral to a hematologist, while those suggesting lymphoma were generally followed by imaging. Interestingly, an insistent family member could influence hematology referral in the setting of persistent anemia, even more so than night sweats, leukocytosis, or weight loss. These data suggest that several lower-cost diagnostic tools may be underutilized in the PCP's anemia workup, that the workup varies with associated clinical factors, and that patients and families influence the ultimate decision to refer to a hematologist. Disclosures: No relevant conflicts of interest to declare.

Description: Background: Little is known about the patterns of care related to the diagnosis of chronic lymphocytic leukemia (CLL), including the use of modern diagnostic techniques such as flow cytometry. A population-based analysis of the diagnostic process for CLL patients, including predictors and consequences of diagnostic delay, could fuel quality improvement efforts. Methods: The SEER-Medicare linked database for the years 1991–2003 was used to identify traditional Medicare enrollees diagnosed with CLL. Both inpatient and outpatient claims were analyzed from one year before, through six months following, the SEER diagnosis date. Signs, symptoms, and diagnostic studies commonly encountered in CLL diagnoses were identified by ICD-9 diagnosis and CPT procedure codes. Using the dates on claims, we calculated the time between the first visit for a sign or symptom and the SEER diagnosis date. Diagnostic delay was considered present if this time period met or exceeded the median number of days for the sample. Logistic regression models were used to estimate the likelihood of receipt of flow cytometry and of diagnostic delay, using clinical and sociodemographic predictor variables. Overall survival was examined using a Cox proportional hazards model. Analyses were adjusted to account for a known lag time in SEER cancer diagnosis dates. Results: We studied 5,086 patients who met eligibility criteria. Of those, 2,282 (48.9%) had a claim for flow cytometry during the study period, and 1,965 (38.6%) were performed within 30 days of the SEER diagnosis date. The most frequent signs and symptoms prior to diagnosis were infection (32.2%), lymphocytosis, (28.7%), and anemia (23.9%). The median survival time was 9.9 years. The median time between sign or symptom and CLL diagnosis date in SEER (defined as diagnostic delay) was 63 days (interquartile range = 251). Significant predictors of diagnostic delay included age of 75 or higher (OR=1.45, 95% CI = 1.27 to 1.65), female gender (OR = 1.22, 95% CI = 1.07 to 1.39), urban resident (OR = 1.46, 95% CI = 1.19 to 1.79), one or more comorbidities, as measured by the Charlson Comorbidity Index (OR = 2.83, 95% CI = 2.45 to 3.28), and care in a teaching hospital in the year preceding diagnosis (OR = 1.20, 95% CI = 1.05 to 1.38). Significant predictors of receipt of flow cytometry were age below 75 (OR = 1.46, 95% CI = 1.30 to 1.66), urban residence (OR = 1.27, 95% CI = 1.05 to 1.53), northeast residence (OR = 2.01, 95% CI = 1.69 to 2.39), southern residence (OR = 1.51, 95% CI = 1.22 to 1.89) and increasing number of pre-diagnosis signs or symptoms (OR = 1.15, 95% CI = 1.08 to 1.22). In multivariate models, diagnostic delay was not a significant predictor of overall survival (HR = 1.10, 95% CI = 0.98–1.25). Conclusions: In this large national cohort of older adults, age and gender both significantly impact diagnostic delay for CLL, raising a concern for sociodemographic differences in clinicians’ responses to signs and symptoms of hematologic malignancy. In addition, our analysis suggests that the presence of comorbidities may lead clinicians to overlook malignancy as an explanation for hematologic anomalies. Finally, initial use of flow cytometry varies significantly by geography and population density, which may reflect knowledge gaps in recommended diagnostic studies or lack of access to hematopathology services.