Abstract
PURPOSE
Multiple myeloma (MM) is a prevalent hematologic malignancy in older adults, who often experience physical disability, increased health care usage, and reduced treatment tolerance. Home health (HH) services are frequently used by this group, but the relationship between disability, HH use, and MM treatment receipt is unclear. This study examines the connections between disability, treatment receipt, and survival outcomes in older adults with newly diagnosed MM using a nationwide data set.
METHODS
The SEER-Medicare data set was used to identify adults aged 66 years and older diagnosed with MM from 2010 to 2017, who used HH services the year before diagnosis. Disability was assessed with the Outcome and Assessment Information Set, using a composite score derived from items related to ability to complete activities of daily living. Mortality, therapy receipt, and health care utilization patterns were evaluated.
RESULTS
Of 37,280 older adults with MM, 6,850 (18.2%) used HH services before diagnosis. Moderate disability at HH assessment resulted in similar MM-directed therapy receipt as mild disability, with comparable health care usage after diagnosis to severe disability. HH users had a higher comorbidity burden and higher mortality (adjusted risk ratio for 3-year mortality: 1.59 [95% CI, 1.55 to 1.64]). Severe functional disability before diagnosis was strongly related to postdiagnosis mortality.
CONCLUSION
Among older adults with MM receiving HH services, disability is a predictor of early mortality. Moderately disabled individuals undergo similar therapy intensity as the mildly disabled but experience increased acute care utilization. Previous HH use could identify patients with MM requiring intensive support during therapy initiation.
Study links disability in older patients with myeloma to higher mortality and health care use. #MMsm.
INTRODUCTION
Multiple myeloma (MM) is a driver of significant morbidity and mortality, accounting for approximately 19% of new hematologic malignancy diagnoses and over 12,000 deaths per year in the United States.1 MM is disproportionately a disease of older adults, with a median age at diagnosis of 69 years and approximately 40% of new cases developing in those older than 75 years.2 The care of patients with MM is often challenging because of the higher prevalence of vulnerabilities observed with advancing age, a risk factor associated with MM incidence.
CONTEXT
Key Objective
To evaluate associations between prediagnosis disability, treatment receipt, survival, and acute care utilization among older adults with newly diagnosed multiple myeloma (MM) using a nationwide data set.
Knowledge Generated
In the population of older adults with MM using home health services, disability stands as a significant predictor for early mortality. Patients with moderate disability, although receiving therapy comparable with those with mild disability, tend to have a higher frequency of acute health care service usage.
Relevance (F.P.-Y. Lin)
-
This registry study highlights the importance of assessing disability levels in older adults with MM. Functional assessment in patients receiving HH care, in addition to prognostic evaluation, may guide proactive, tailored strategies to reduce reliance on acute care resources in this patient group.*
*Relevance section written by JCO CCI Associate Editor Frank Po-Yen Lin, MB ChB, PhD, FRACP, FAIDH.
Previous single-center studies and therapeutic trials have demonstrated that aging-related functional impairments in activities of daily living (ADLs—eg, bathing, toileting)3 and instrumental activities of daily living (IADLs—eg, meal preparation, transportation)4 are common among older adults with MM.5 Functional deficits in this population have been correlated with treatment intolerance,6-9 increased health care utilization,10 decreased autologous hematopoietic stem-cell transplantation candidacy,11,12 and increased mortality.6-9,13-15 These findings have led to the development of a number of MM-specific frailty scales incorporating impairments in ADLs/IADLs and comorbidities, with or without other clinical factors. These include the International Myeloma Working Group frailty score,6,13 the Mayo Clinical Frailty score,14 the revised Myeloma Comorbidity Index,15 and a simplified frailty scale proposed by Facon et al.9
However, previous research reliant upon single-center studies at academic institutions and therapeutic clinical trials largely reflects MM populations that are often disproportionately fit compared with the general MM population.16 In this study, we evaluated associations between functional disability and treatment receipt/outcomes among older adults with newly diagnosed MM using a large, diverse, and more broadly generalizable nationwide data source.
METHODS
Data Sources
The study cohort was identified from the SEER-Medicare linked database. This database contains patient-level demographic data and cancer-specific information derived from SEER-participating state and regional cancer registries linked to Medicare administrative data, which encompass detailed claims from inpatient and outpatient encounters and related physician services. Details of this data set have been previously published.17 The study design was evaluated by the institutional review board at the University of North Carolina and deemed exempt from review because of it being a secondary analysis of existing, anonymized data.
Data on functional capacity were obtained from the linked Outcome and Assessment Information Set (OASIS). The OASIS data set includes administrative and clinical assessment information submitted by all health agencies in the United States for all individuals age 18 years and older who received skilled home health (HH) services that were reimbursed by Medicaid or Medicare. These assessments are completed at initiation of care, upon transfer to an inpatient facility, at resumption of care, at the time of discharge from HH services, and at recertification intervals (every 60 days). Although OASIS assessments have been previously available to researchers, the National Cancer Institute (NCI) linked OASIS and SEER-Medicare data in 2019, with linked data now available for 1999 onward.18
Study Population
We identified a retrospective cohort of adults age 66 years or older diagnosed with MM between 2010 and 2017 in the SEER-Medicare database. MM was identified via International Classification of Diseases (ICD)-O-3 histology codes (9732/3), which notably include both individuals with MM and those with asymptomatic smoldering MM. Individuals were excluded if the month of diagnosis was missing. Date of diagnosis was set to the 15th day of the month of diagnosis (as SEER data do not report the detailed date of diagnosis). For analyses related to receipt of treatment and health care utilization, the cohort was further restricted to individuals who were enrolled in Medicare Parts A and B (traditional fee-for-service [FFS] coverage) during the 12 months before diagnosis and the 12 months after diagnosis or until death (whichever occurred first). Continuous Part D coverage was not required.
Exposures
The primary exposure among the main analytic cohort was degree of functional disability could be determined only among individuals receiving HH services. Additional contextualizing analyses were conducted using a secondary exposure of any receipt of HH services during the year before MM diagnosis, versus nonrecipients of such services.
Individuals were deemed HH service recipients if they had at least one OASIS assessment in the 12 months before MM diagnosis (inclusive of the portion of month of diagnosis before the 15th day). Degree of functional disability was defined via a composite score derived from individual items related to ADLs and IADLs contained in the OASIS-C1 or OASIS-C2 assessment instruments (Appendix Table A1), as done previously.19 These OASIS items included M1800 (grooming), M1810 (upper body dressing), M1820 (lower body dressing), M1830 (bathing), M1840 (toileting), M1845 (toileting hygiene), M1850 (transferring), M1860 (ambulation), M1870 (feeding), and M1880 (meal preparation). The numeric responses to each item were summed, yielding a total score ranging from 0 (no functional disability) to 40 (most disabled). Individuals were categorized by cohort distributional breakpoints as having mild (score 0-9), moderate (10-25), or severe (26-40) disability on the basis of two patterns in the observed data. These included an apparent bimodal distribution of OASIS scores with a long right tail in the cohort (Appendix Fig A1) and an apparent inflection point in the frequency of receiving any MM therapy at an OASIS score of 26 (Appendix Fig A2). Individuals who had an OASIS assessment in the year before diagnosis in which functional assessment items were incomplete (n = 266) were excluded from analysis.
Outcomes
The primary outcome was overall mortality, which was assessed up to 5 years from MM diagnosis or December 31, 2019 (whichever occurred first). Secondary outcomes included receipt of any MM therapy in the year after diagnosis, treatment type (three-agent triplet therapy and autologous stem-cell transplantation), and health care utilization (emergency department [ED] visits and hospitalizations as identified in Medicare claims files). Treatment receipt was defined as any claim for an agent typically used for the treatment of MM in the year after diagnosis, with relevant agents identified via National Drug Codes or Healthcare Current Procedural Coding System codes (Appendix Table A2). Triplet therapy was defined via claims for two agents in different therapeutic classes within a 60-day interval, with the assumption that corticosteroids were concurrently administered as a third medication.20
Covariates
Patient-level covariates included individual demographic information (age, sex, race/ethnicity, SEER region, and marital status), year of diagnosis, and county-level socioeconomic indicators derived from the American Community Survey. Among Medicare enrollees with FFS coverage, comorbidity was quantified via the NCI comorbidity index (NCI CI).21 The NCI CI uses ICD-9 and ICD-10 diagnosis codes in the Medicare claims files to identify the 15 noncancer comorbidities from the Charlson comorbidity index. A weight is then assigned to each condition on the basis of its potential influence on 2-year mortality, and the weights are summed to obtain a comorbidity index for each patient.
Statistical Analyses
Descriptive statistics are reported as medians and IQRs or frequencies and percentages. Associations between functional disability and survival were assessed via cumulative risk ratios (RRs) at 12, 36, and 60 months from diagnosis, with bootstrapped 95% CIs. The association between receipt of any HH services and mortality was unadjusted, with the aim of contextualizing prognosis among the primary analytic cohort of individuals who received HH services in light of anticipated stark differences between this group and the broader population with MM. The association between degree of functional disability and mortality among those receiving HH services was adjusted for age, sex, race and ethnicity, and SEER region via inverse probability of treatment weighting (IPTW), with the aim of evaluating the independent association of degree of disability with mortality among this group.22
Associations between the degree of functional disability and MM therapy outcomes were assessed via hazard ratios (HRs) and associated 95% CIs, with death considered as a competing risk and adjustment for age, sex, race, NCI CI, and SEER region via IPTW. Sensitivity analyses related to Medicare Part D coverage were conducted. For utilization outcomes, the HH service recipients in the cohort were stratified by degree of functional disability and mean cumulative counts (MCCs) were calculated for each outcome within each stratum with adjustment for the same covariates via IPTW.23,24 These counts were then compared via calculation of MCC differences and associated 95% CIs.
RESULTS
Cohort Characteristics
Among 37,280 individuals, 6,850 (18.2%) received HH services in the year before diagnosis, and these individuals served as our primary analytic cohort used to explore associations between disability and mortality (Fig 1). When compared with nonrecipients, HH service recipients were older (38.6% aged ≥82 years v 21.8%), more likely to be non-Hispanic Black (19.9%% v 17.2%), and more likely to live in counties with higher levels of poverty (25.2% in highest quartile v 21.7%), and had lower educational attainment (ie, high school noncompletion of 27.4% in highest quartile v 24.5%; Table 1). Urban versus rural residence was similar between HH service recipients and nonrecipients, with the majority of both groups living in urban counties. Among recipients, 2,366 (34.5%) were categorized as having mild disability, 2,200 (32.1%) as moderate disability, and 2,284 (33.3%) as severe disability.
FIG 1.
Individuals identified in SEER-Medicare included in study cohorts. FFS, fee-for-service; HH, home health; MM, multiple myeloma; OASIS, Outcome and Assessment Information Set.
TABLE 1.
Cohort Characteristics Among Medicare Beneficiaries Diagnosed With Multiple Myeloma From 2010 to 2017, Full Cohort and Stratified by Receipt of Home Health Services
| Demographic Variable | Total (N = 37,280) | Home Health Recipients (n = 6,850) | Nonrecipients (n = 30,430) |
|---|---|---|---|
| Age at diagnosis, years, No. (%) | |||
| 66-70 | 9,722 (26.1) | 1,010 (14.7) | 8,712 (28.6) |
| 71-75 | 8,964 (24.0) | 1,360 (19.9) | 7,604 (25.0) |
| 76-81 | 9,324 (25.0) | 1,835 (26.8) | 7,489 (24.6) |
| ≥82 | 9,270 (24.9) | 2,645 (38.6) | 6,625 (21.8) |
| Sex, No. (%) | |||
| Female | 16,650 (44.7) | 3,557 (51.9) | 13,093 (43.0) |
| Male | 20,630 (55.3) | 3,293 (48.1) | 17,337 (57.0) |
| Race/ethnicity, No. (%) | |||
| Other than non-Hispanic Black | 30,695 (82.3) | 5,484 (80.1) | 25,211 (82.8) |
| Non-Hispanic Black | 6,585 (17.7) | 1,366 (19.9) | 5,219 (17.2) |
| Marital status, No. (%) | |||
| Married | 14,716 (39.5) | 2,249 (32.8) | 12,467 (41.0) |
| Not married | 20,445 (54.8) | 4,268 (62.3) | 16,177 (53.2) |
| Unknown | 2,119 (5.7) | 333 (4.9) | 1,786 (5.9) |
| Patient location, No. (%) | |||
| Urban | 33,271 (89.2) | 6,130 (89.5) | 27,141 (89.2) |
| Rural | 4,009 (10.8) | 720 (10.5) | 3,289 (10.8) |
| Percentage living in poverty (county),a No. (%) | |||
| Lowest quartile | 9,393 (25.2) | 1,550 (22.6) | 7,843 (25.8) |
| Low-mid quartile | 9,216 (24.7) | 1,698 (24.8) | 7,518 (24.7) |
| High-mid quartile | 10,321 (27.7) | 1,877 (27.4) | 8,444 (27.8) |
| Highest quartile | 8,336 (22.4) | 1,724 (25.2) | 6,612 (21.7) |
| Percentage with less than high-school education (county),a No. (%) | |||
| Lowest quartile | 9,281 (24.9) | 1,526 (22.3) | 7,755 (25.5) |
| Low-mid quartile | 9,307 (25.0) | 1,702 (24.9) | 7,605 (25.0) |
| High-mid quartile | 9,356 (25.1) | 1,746 (25.5) | 7,610 (25.0) |
| Highest quartile | 9,322 (25.0) | 1,875 (27.4) | 7,447 (24.5) |
| Percentage unemployed (county),a No. (%) | |||
| Lowest quartile | 9,294 (24.9) | 1,564 (22.8) | 7,730 (25.4) |
| Low-mid quartile | 9,201 (24.7) | 1,711 (25.0) | 7,490 (24.6) |
| High-mid quartile | 9,053 (24.3) | 1,708 (24.9) | 7,345 (24.1) |
| Highest quartile | 9,718 (26.1) | 1,866 (27.2) | 7,852 (25.8) |
| Year of diagnosis, No. (%) | |||
| 2010 | 4,040 (10.8) | 610 (8.9) | 3,430 (11.3) |
| 2011 | 4,368 (11.7) | 816 (11.9) | 3,552 (11.7) |
| 2012 | 4,412 (11.8) | 821 (12.0) | 3,591 (11.8) |
| 2013 | 4,686 (12.6) | 888 (13.0) | 3,798 (12.5) |
| 2014 | 4,825 (12.9) | 892 (13.0) | 3,933 (12.9) |
| 2015 | 4,901 (13.1) | 962 (14.0) | 3,939 (12.9) |
| 2016 | 5,127 (13.8) | 968 (14.1) | 4,159 (13.7) |
| 2017 | 4,921 (13.2) | 893 (13.0) | 4,028 (13.2) |
| SEER registry, No. (%) | |||
| California | 9,687 (26.0) | 1,588 (23.2) | 8,099 (26.6) |
| Connecticut | 1,284 (3.4) | 280 (4.1) | 1,004 (3.3) |
| Georgia | 3,453 (9.3) | 682 (10.0) | 2,771 (9.1) |
| Hawaii | 404 (1.1) | 25 (0.4) | 379 (1.2) |
| Idaho | 519 (1.4) | 81 (1.2) | 438 (1.4) |
| Iowa | 1,285 (3.4) | 180 (2.6) | 1,105 (3.6) |
| Kentucky | 1,527 (4.1) | 336 (4.9) | 1,191 (3.9) |
| Louisiana | 1,700 (4.6) | 431 (6.3) | 1,269 (4.2) |
| Massachusetts | 2,012 (5.4) | 479 (7.0) | 1,533 (5.0) |
| Detroit | 1,658 (4.4) | 402 (5.9) | 1,256 (4.1) |
| New Jersey | 3,121 (8.4) | 532 (7.8) | 2,589 (8.5) |
| New Mexico | 522 (1.4) | 87 (1.3) | 435 (1.4) |
| New York | 7,829 (21.0) | 1,444 (21.1) | 6,385 (21.0) |
| Utah | 642 (1.7) | 131 (1.9) | 511 (1.7) |
| Seattle | 1,637 (4.4) | 172 (2.5) | 1,465 (4.8) |
| Total FFS Medicare Enrollees (N = 20,328) | FFS Home Health Recipients (n = 4,108) | FFS Nonrecipients (n = 16,220) | |
|---|---|---|---|
| Comorbid conditions, median (IQR)b | 2 (0-4) | 3 (1-5) | 2 (0-3) |
| 0, No. (%) | 5,128 (25.2) | 513 (12.5) | 4,615 (28.5) |
| 1, No. (%) | 3,952 (19.4) | 516 (12.6) | 3,436 (21.2) |
| 2 or more, No. (%) | 11,248(55.3) | 3,079 (74.9) | 8,169 (50.4) |
Abbreviation: FFS, fee-for-service.
There are 14 observations with missing county-level data.
Comorbid conditions could only be assessed among traditional Medicare/FFS enrollees.
A subset of 20,328 individuals were continuously enrolled in Medicare Parts A and B during the 12 months before diagnosis and the 12 months after diagnosis (or until death), allowing for assessment of comorbid conditions at diagnosis and claims-based outcomes (Table 1). Compared with the full cohort, these Medicare enrollees with FFS coverage were older (27.2% in highest age quartile v 24.9%) and less likely to be non-Hispanic Black (14.2% v 17.7% in full cohort; Appendix Table A3). Among this subset, 4,108 received HH services before their MM diagnosis and served as a secondary analytic cohort used to explore associations between disability and receipt of therapy/health care utilization. HH service recipients had a greater comorbidity burden than nonrecipients (median 3 v 2).
Functional Disability and Mortality
HH service recipients were more likely to die after their MM diagnosis compared with nonrecipients (Fig 2A). The 3-year overall mortality was 67.3% among HH service recipients and 43.4% among nonrecipients. Crude RRs for mortality among recipients versus nonrecipients were 1.88 at 1 year (95% CI, 1.82 to 1.94), 1.55 at 3 years (95% CI, 1.52 to 1.58), and 1.38 at 5 years (95% CI, 1.35 to 1.40). This higher mortality was observed among recipients for all disability severity groups. For instance, when compared with nonrecipients at 3 years from diagnosis, RRs for mortality were 1.43 (95% CI, 1.39 to 1.48) among HH service recipients with mild disability, 1.53 (95% CI, 1.48 to 1.58) among those with moderate disability, and 1.70 (95% CI, 1.65 to 1.75) among those with severe disability.
FIG 2.
(A) Crude cumulative mortality among HH recipients and non-HH service recipients with multiple myeloma. (B) Inverse probability of treatment-weighted cumulative mortality among HH service recipients, stratified by degree of disability. Models adjusted for age, sex, race, and SEER region. HH, home health.
Among HH service recipients, mortality was higher among those with more severe functional disabilities (Fig 2B). Adjusted RRs for mortality among the severe versus mild disability groups were 2.30 (95% CI, 2.05 to 2.58) at 6 months, 1.83 (95% CI, 1.67 to 2.00) at 1 year, 1.35 (95% CI, 1.27 to 1.44) at 3 years, and 1.21 (95% CI, 1.15 to 1.27) at 5 years (Appendix Table A4). Adjusted RRs for the moderate versus mild disability group were 1.32 (95% CI, 1.22 to 1.43) at 6 months, 1.23 (95% CI, 1.16 to 1.30) at 1 year, 1.10 (95% CI, 1.06 to 1.13) at 3 years, and 1.07 (95% CI, 1.04 to 1.10) at 5 years.
Receipt of Therapy
Among Medicare enrollees with FFS coverage diagnosed with MM and receiving HH services, 57.0% received MM-directed treatment in the year after diagnosis. The cumulative incidence of receiving MM therapy differed by disability (Fig 3). Those with severe disability were less likely to receive MM therapy in the year after diagnosis (adjusted HR, 0.70 v mild [95% CI, 0.56 to 0.88]; HR, 0.63 v moderate [95% CI, 0.51 to 0.79]). Individuals with moderate disability were more likely to receive treatment than those with mild disability (adjusted HR, 1.10 [95% CI, 1.02 to 1.20]).
FIG 3.
Incidence of therapy receipt in the year after diagnosis among home health service recipients diagnosed with multiple myeloma stratified by degree of disability. Shaded regions reflect 95% CIs. The figure reflects unadjusted cumulative proportions.
In a sensitivity analysis restricted to individuals with Medicare Part D coverage to ensure more complete capture of treatments (n = 1,979), the cumulative incidence of chemotherapy receipt was somewhat higher (approximately 61.4%), although the HRs for receipt of treatment between disability strata were similar to those in the primary analysis (HR for severe v mild, 0.63 [95% CI, 0.47 to 0.86]; moderate v mild, 1.15 [95% CI, 1.02 to 1.30]).
Individuals with severe disability were less likely to receive more intensive triplet therapy (adjusted HR, 0.57 v mild [95% CI, 0.35 to 0.93]). Rates of triplet therapy receipt were higher in the moderate versus mild disability group, although this difference was not statistically significant (adjusted HR, 1.11 [95% CI, 0.94 to 1.32]). Stem-cell transplants were rare in all groups (<5%).
Health Care Utilization
In the year after diagnosis, Medicare enrollees diagnosed with MM who had received previous HH services experienced an average of 2.5 ED visits and 2.1 inpatient admissions. Individuals with mild disability had fewer ED visits (adjusted MCC difference v moderate, –0.34 [95% CI, –0.50 to –0.18]; v severe, –0.57 [95% CI, –1.05 to –0.10]) and fewer hospitalizations (adjusted MCC difference v moderate, –0.42 [95% CI, –0.54 to –0.30]; v severe, –0.59 [95% CI, –0.93 to –0.26]) in the year after diagnosis (Fig 4). Those with moderate or severe degrees of disability had similar numbers of ED visits and hospitalizations.
FIG 4.
(A) Mean cumulative counts of ED visits among HH service recipients diagnosed with multiple myeloma, stratified by degree of disability. Shaded regions reflect 95% CIs. Models adjusted for age, sex, race, and SEER region. (B) Mean cumulative counts of hospital admissions among HH service recipients diagnosed with multiple myeloma, stratified by degree of disability. Shaded regions reflect 95% CIs. Models adjusted for age, sex, race, and SEER region. ED, emergency department; HH, home health.
DISCUSSION
In this large and diverse study of older adult HH recipients diagnosed with MM, we identified differences in treatment receipt and health care utilization by degree of prediagnosis functional disability. Individuals with moderate disability had comparable or even higher proportions of treatment receipt when compared with individuals with mild disability but experienced high acute care utilization similar to the severe disability group. These differences remained statistically significant even after adjustment for other factors likely to affect treatment decisions and health care utilization, such as chronologic age and comorbidity. Considering these results, individuals with moderate disability receiving HH services may represent a population judged fit enough for treatment but in particular need of supportive care resources when initiating MM therapy.
Degree of physical disability before MM diagnosis was also a substantial predictor of early postdiagnosis mortality, even within a population that uniformly required HH services before their MM diagnoses. In this cohort, we observed an apparent dose-response relationship, in which worse disability severity was associated with an increased risk of mortality. Mortality in the moderate disability group differed only modestly from that of the mild disability group.
These results validate findings from previous studies regarding the association of disability and health outcomes among older adults with MM. Functional deficits in this population have been correlated with MM treatment intolerance,6-9 increased health care utilization,10 reduced transplant eligibility,11,12 and worse survival.6-9,13-15 However, previous studies have generally been conducted at academic centers or as part of therapeutic clinical trials. Our study is novel for its use of a large and diverse data source that is more generalizable to routine clinical care.
One previous study has investigated the association of frailty with survival among patients with MM using SEER-linked data.25 This study differed in scope from the current work, as it focused on the development of a generalized frailty model, rather than centering primarily on the relationship between functional impairments/disability and treatment outcomes.26 This previous study used linked data from SEER and the Medicare Health Outcomes Survey (MHOS). Consequently, claims-based outcomes such as therapy receipt and health care utilization could not be concurrently assessed. An additional limitation of the MHOS is that this survey is conducted among patients with Medicare Advantage plans, a group that may be disproportionately functional compared with the general population of patients with MM.27
Among the Medicare enrollees with FFS coverage diagnosed with MM and receiving HH services evaluated in this study, 43.0% did not have any form of systemic MM therapy identified in Medicare claims in the year after their MM diagnosis. Our observed frequency of nontreatment is broadly similar to that noted in a previous SEER-Medicare analysis, in which 38% of Medicare enrollees diagnosed with MM from 2007 to 2011 received no therapy in the 6 months after diagnosis.28 Although we are not able to assess reasons for nontreatment in the current study, these results suggest that older adults may be at risk of undertreatment after a MM diagnosis.
The current study has some limitations. As most analyses were limited to HH service recipients, findings regarding the association between disability and treatment receipt or mortality may not be generalizable to all older adults with MM. The quality or thoroughness with which OASIS assessments are completed may also be variable,29 although the positive association between degree of disability and mortality observed in this study suggests a degree of face validity for this disability measure. Additionally, we did not distinguish individuals with clinically overt MM from presymptomatic smoldering MM, for whom MM-directed therapy would not be considered standard of care. These presymptomatic individuals may be disproportionately more functional, as they would (by definition) not have clinical manifestations of MM, and this relationship could confound the association between disability status and therapy receipt. Notably, previous analyses that have attempted to distinguish smoldering MM from overt MM on the basis of concurrent claims suggest that smoldering MM represents <10% of MM diagnoses in SEER-Medicare.25,30 Finally, our study is limited to an assessment of newly diagnosed individuals potentially receiving first-line therapy for a disease entity for which multiple sequential lines of therapy are available31; future work should evaluate associations between disability, receipt, and tolerability of later lines of therapy for relapsed/refractory disease.
Even among a vulnerable population of older adults requiring HH services, most individuals with mild or moderate disability went on to receive some form of MM-directed therapy in the year after their diagnosis. These individuals have greater early mortality than older adults with MM who did not previously receive HH services, potentially highlighting the utility of HH service receipt as a marker of individuals who will need more intensive support when starting therapy. HH service receipt may also identify individuals who would most benefit from more thorough clinical assessments, such as geriatric assessment,11,32-34 to identify other areas of vulnerability and opportunities for management and supportive care.
ACKNOWLEDGMENT
The authors appreciate the assistance provided by Dr Charles Gaber, who provided code related to calculation of IPTW-adjusted MCCs.
APPENDIX
FIG A1.
Histogram of OASIS composite score among home health recipients diagnosed with MM. Analytic groups of 0-9 (mild disability), 10-25 (moderate disability), and 26-40 (severe disability) were used, because of apparent local peaks in the 0-9 and 10-25 ranges. MM, multiple myeloma; OASIS, Outcome and Assessment Information Set.
FIG A2.
MM-directed therapy receipt versus OASIS composite score among Medicare enrollees with fee-for-service coverage diagnosed with MM and receiving home health services in the year before diagnosis. Analytic groups of 0-9 (mild disability), 10-25 (moderate disability), and 26-40 (severe disability) were used, because of apparent inflection in treatment patterns noted near the OASIS composite score of 26. MM, multiple myeloma; OASIS, Outcome and Assessment Information Set.
TABLE A1.
Response Items From OASIS-C Instruments Used in Composite Functional Disability Variable
| Response Item | Description | Score Range |
|---|---|---|
| ADL/IADLsa | ||
| M1800 | Grooming | 0-3 |
| M1810 | Ability to dress the upper body | 0-3 |
| M1820 | Ability to dress the lower body | 0-3 |
| M1830 | Bathing | 0-6 |
| M1840 | Toilet transferring | 0-4 |
| M1845 | Toilet hygiene | 0-3 |
| M1850 | Transferring | 0-5 |
| Functional abilitiesa | ||
| M1860 | Ambulation/locomotion | 0-6 |
| M1870 | Feeding or eating | 0-5 |
| M1880 | Ability to plan and prepare light meals | 0-2 |
Abbreviations: ADL, activity of daily living; IADL, instrumental activity of daily living; OASIS, Outcome and Assessment Information Set.
Heading names derived from OASIS-C1 and -C2 item sets.
TABLE A2.
Medications Used for the Treatment of Multiple Myeloma
| Medication | Codes |
|---|---|
| Arsenic trioxide | HCPCS: C9012, J9017 NDC: 14789060010, 49315000510, 49315000710, 50742043810, 50742052507, 54879002711, 63323063710, 63459060010, 63459060106, 68382099710, 69918072002, 69918072010, 70121148307, 70710161006, 70860021710 |
| Belantamab mafodotin | HCPCS: J9037, C9069 NDC: 00173089601 |
| Bendamustine | HCPCS: C9243, J9033, J9034, J9036 NDC: 63459034804, 63459039008, 63459039120, 63459039502, 63459039602, 42367052025, 42367052125, 67457032512, 67457032605 |
| Bortezomib | HCPCS: J9041, S0115 NDC: 63020004901, 43598086560 |
| Carfilzomib | HCPCS: J9047, C9295 NDC: 76075010101, 76075010201, 76075010301 |
| Carmustine | HCPCS: C9437, J9050 NDC: 00015301260, 23155026141, 24338005008, 62856017708, 54879003664, 68475050301, 70121148202, 70710152509, 23155064941, 00781347432, 23155079041 |
| Cisplatin | HCPCS: J9060, J9062, C9418 NDC: 00069008101, 00069008407, 00703574711, 00703574811, 16729028811, 16729028838, 44567050901, 44567051001, 44567051101, 47781060925, 47781061023, 61126000310, 61126000401, 61126000402, 63323010351, 63323010364, 63323010365, 68001028327, 68001028332, 68083016201, 68083016301, 44567053001, 00015307097, 00015307220, 00015307297, 67457042410, 67457042551, 70860020650, 70860020651 |
| Cyclophosphamide | HCPCS: J9091, J9070, J9092, J9080, J9090, C9420, C9421, J9093, J9094, J9095, J9096, J9097, J8530 NDC: 00054038225, 00054038325, 00054412925, 00054413025, 00781323394, 00781324494, 00781325594, 10019093501, 10019093601, 10019093701, 10019093801, 10019093901, 10019094201, 10019094301, 10019094401, 10019094501, 10019095501, 10019095601, 10019095701, 10019098801, 10019098901, 10019099001, 54868500500, 54868521800, 54868521801, 69189038201, 69189038301, 70121124001, 70121123901, 70121123801, 54879002201, 54879002101, 43975030810, 43975030710, 00015050241, 16714085701, 16714085801, 16714085901, 00015050541, 00015050301, 00015050401, 00015050641, 68001037027, 68001037132, 68001037232, 69097051607, 69097051707, 10019098201, 10019098401, 50742051902, 50742052005, 62559093001, 62559093101, 68001044226, 68001044327, 68001044432, 72603010401, 72603041101, 72603032601, 70860021803, 70860021805 |
| Cytarabine | HCPCS: J9100, C9422, J9110, J9098 NDC: 00069015201, 00069015202, 00069015301, 00069015302, 00069015401, 00069015501, 55390013110, 55390013210, 55390013301, 55390013401, 55390080610, 55390080710, 55390080801, 55390080901, 57665033101, 61703030346, 61703030436, 61703030538, 61703031922, 63323012020, 67457045220, 67457045450, 67457045500, 67457045552, 67457061520, 71288010920, 68083033701, 68083034305, 71288010806 |
| Daratumumab | HCPCS: J9415, C9476, J9144, C9062 NDC: 57894050205, 57894050220, 57894050301 |
| Doxorubicin | HCPCS: J9000, C9415, J9002, Q2048, Q2049, Q2050, J9001 NDC: 00069303120, 00069303220, 00069303320, 00069400405, 00069401510, 00069402625, 00069403001, 00069403101, 00069403201, 00069403301, 00069403401, 00069403701, 00143954601, 00143954701, 00143954801, 00143954810, 00143954901, 00143954910, 00409012401, 00703504001, 00703504301, 00703504303, 00703504601, 16714074201, 16714085601, 25021020705, 25021020725, 25021020751, 43598028335, 43598054125, 45963073355, 45963073357, 45963073360, 45963073368, 47335004940, 47335005040, 47335008250, 47335008350, 53150031401, 53150031410, 53150031501, 53150031701, 53150032001, 53150032010, 59676096001, 59676096002, 59676096601, 59676096602, 62756082640, 62756082740, 63323010161, 63323088305, 63323088310, 63323088330, 67457039300, 67457039354, 67457039525, 67457039610, 67457043650, 67457047810, 68083024801, 68083024901, 68083025001, 70121121901, 00143927501, 00143927701, 43598068235, 43598068325, 47781025618, 47781025617, 47781025619, 68001034536, 68001034526, 55390023701, 55390023801, 00013111683, 00013113691, 00013114691, 00013115679, 00013117687, 00013126683, 00013128683, 00015335222, 00015335322, 00069017001, 00069017101, 00069303020, 00069303420, 67457039400, 67457039410, 70121121801, 70121121807, 00338006701, 00338006301, 00338008001, 00338008601, 16714000101, 49315000803, 49315000907, 00143909201, 00143909301, 72603010301, 72603020001, 70710153001, 70710153101, 68001049236, 68001049326 |
| Elotuzumab | HCPCS: C9477, J9176 NDC: 00003229111, 00003452211 |
| Etoposide | HCPCS: J9181, J9182, C9425, J8560, C9414 NDC: 63323010425, 63323010450, 68001026522, 68001026523, 68001026524, 68001026525, 68001026526, 68001026527, 00015340420, 00378326694, 00703565301, 00703565601, 00703565691, 16729011408, 16729011411, 16729011431, 16729011432, 16729026231, 16729026232, 55390029101, 55390029201, 55390029301, 55390049101, 55390049201, 55390049301, 63323010405, 00703565701, 00703565791 |
| Idecabtagene vicleucel | HCPCS: C9081 NDC: 59572051501, 59572051502, 59572051503 |
| Isatuximab | HCPCS: J9227 NDC: 00024065401, 00024065601 |
| Ixazomib | NDC: 63020007801, 63020007802, 63020007901, 63020007902, 63020008001, 63020008002 |
| Lenalidomide | NDC: 59572040200, 59572040228, 59572040500, 59572040528, 59572041000, 59572041028, 59572041500, 59572041521, 59572042000, 59572042021, 59572042500, 59572042521 |
| Melphalan | HCPCS: J9245, J8600, J9246 NDC: 47781020050, 52609000105, 54868433900, 54868433901, 54868433903, 54868433904, 68152010900, 00173013093, 10139032101, 25021022160, 42023014901, 45963068602, 52609300100, 59572030101, 67457019501, 67457021501, 67457057901, 43598039248, 63323076020, 50742047701, 54288010603, 68083025901, 43598002748, 71288011290, 72266012801, 70860021461, 72611077902, 70700027897, 71288013290 |
| Melphalan flufenamide | HCPCS: C9080, J9247 NDC: 73657002001 |
| Pomalidomide | NDC: 59572050100, 59572050121, 59572050200, 59572050221, 59572050300, 59572050321, 59572050400, 59572050421 |
| Selinexor | NDC: 72237010101, 72237010102, 72237010103, 72237010104, 72237010105, 72237010106, 72237010107, 72237010202, 72237010206, 72237010207, 72237010305, 72237010401 |
| Thalidomide | NDC: 59572020514, 59572020517, 59572020594, 59572020597, 59572021015, 59572021095, 59572021513, 59572021593, 59572022016, 59572022096 |
| Venetoclax | NDC: 00074056111, 00074056114, 00074056607, 00074056611, 00074057928, 00074057611, 00074057622, 00074057634 |
| Vincristine | HCPCS: J9370, J9375, J9380, J9371 NDC: 00703440211, 00703441211, 20536032201, 61703030906, 61703030916, 61703030925, 61703030926 |
| Interferon | HCPCS: J9213, J9214, J9216 NDC: 00085113301, 00085116801, 00085123501, 00085124201, 00085125401, 00085435001, 00085435101, 00085435201, 64116001101, 64116001112, 75987011111, 00085053901, 00085057102, 00085111001, 42238011101, 42238011112 |
Abbreviations: HCPCS, Healthcare Common Procedure Coding System; NDC, US Food and Drug Administration National Drug Code.
TABLE A3.
Cohort Characteristics Among Fee-For-Service Medicare Beneficiaries Diagnosed With Multiple Myeloma From 2010 to 2017, Stratified by Receipt of Home Health Services
| Demographic Variable | Total (N = 20,328) | Home Health Recipients (n = 4,108) | Nonrecipients (n = 16,220) |
|---|---|---|---|
| Age at diagnosis, years, No. (%) | |||
| 66-70 | 4,613 (22.7) | 563 (13.7) | 4,050 (25.0) |
| 71-75 | 4,836 (23.8) | 792 (19.3) | 4,044 (24.9) |
| 76-81 | 5,342 (26.3) | 1,101 (26.8) | 4,241 (26.1) |
| ≥82 | 5,537 (27.2) | 1,652 (40.2) | 3,885 (24.0) |
| Sex, No. (%) | |||
| Female | 9,189 (45.2) | 2,135 (52.0) | 7,054 (43.5) |
| Male | 11,139 (54.8) | 1,973 (48.0) | 9,166 (56.5) |
| Race/ethnicity, No. (%) | |||
| Other than non-Hispanic Black | 17,448 (85.8) | 3,412 (83.1) | 14,036 (86.5) |
| Non-Hispanic Black | 2,880 (14.2) | 696 (16.9) | 2,184 (13.5) |
| Marital status, No. (%) | |||
| Married | 8,111 (39.9) | 1,386 (33.7) | 6,725 (41.5) |
| Not married | 11,069 (54.5) | 2,517 (61.3) | 8,552 (52.7) |
| Unknown | 1,148 (5.6) | 205 (5.0) | 943 (5.8) |
| Patient location, No. (%) | |||
| Urban | 17,566 (86.4) | 3,603 (87.7) | 13,963 (86.1) |
| Rural | 2,762 (13.6) | 505 (12.3) | 2,257 (13.9) |
| Percentage living in poverty (county),a No. (%) | |||
| Lowest quartile | 5,773 (28.4) | 1,063 (25.9) | 4,710 (29.1) |
| Low-mid quartile | 4,989 (24.6) | 1,000 (24.3) | 3,989 (24.6) |
| High-mid quartile | 4,984 (24.5) | 1,038 (25.3) | 3,946 (24.3) |
| Highest quartile | 4,571 (22.5) | 1,006 (24.5) | 3,565 (22.0) |
| Percentage with less than high-school education (county),a No. (%) | |||
| Lowest quartile | 5,644 (27.8) | 1,003 (24.4) | 4,641 (28.6) |
| Low-mid quartile | 5,227 (25.7) | 1,027 (25.0) | 4,200 (25.9) |
| High-mid quartile | 4,888 (24.1) | 1,017 (24.8) | 3,871 (23.9) |
| Highest quartile | 4,558 (22.4) | 1,060 (25.8) | 3,498 (21.6) |
| Percentage unemployed (county),a No. (%) | |||
| Lowest quartile | 5,481 (27.0) | 990 (24.1) | 4,491 (27.7) |
| Low-mid quartile | 4,965 (24.4) | 1,022 (24.9) | 3,943 (24.3) |
| High-mid quartile | 5,028 (24.7) | 1,069 (26.0) | 3,959 (24.4) |
| Highest quartile | 4,843 (23.8) | 1,026 (25.0) | 3,817 (23.5) |
| Year of diagnosis, No. (%) | |||
| 2010 | 2,397 (11.8) | 418 (10.2) | 1,979 (12.2) |
| 2011 | 2,531 (12.5) | 538 (13.1) | 1,993 (12.3) |
| 2012 | 2,487 (12.2) | 505 (12.3) | 1,982 (12.2) |
| 2013 | 2,606 (12.8) | 544 (13.2) | 2,062 (12.7) |
| 2014 | 2,573 (12.7) | 536 (13.0) | 2,037 (12.6) |
| 2015 | 2,553 (12.6) | 529 (12.9) | 2,024 (12.5) |
| 2016 | 2,676 (13.2) | 563 (13.7) | 2,113 (13.0) |
| 2017 | 2,505 (12.3) | 475 (11.6) | 2,030 (12.5) |
| SEER registry, No. (%) | |||
| California | 4,442 (21.9) | 873 (21.3) | 3,569 (22.0) |
| Connecticut | 775 (3.8) | 192 (4.7) | 583 (3.6) |
| Georgia | 1,916 (9.4) | 382 (9.3) | 1,534 (9.5) |
| Hawaii | 142 (0.7) | 5 (0.1) | 137 (0.8) |
| Idaho | 292 (1.4) | 45 (1.1) | 247 (1.5) |
| Iowa | 955 (4.7) | 127 (3.1) | 828 (5.1) |
| Kentucky | 964 (4.7) | 233 (5.7) | 731 (4.5) |
| Louisiana | 992 (4.9) | 284 (6.9) | 708 (4.4) |
| Massachusetts | 1,268 (6.2) | 318 (7.7) | 950 (5.9) |
| Detroit | 950 (4.7) | 256 (6.2) | 694 (4.3) |
| New Jersey | 2,173 (10.7) | 392 (9.5) | 1,781 (11.0) |
| New Mexico | 266 (1.3) | 49 (1.2) | 217 (1.3) |
| New York | 3,899 (19.2) | 778 (18.9) | 3,121 (19.2) |
| Utah | 343 (1.7) | 75 (1.8) | 268 (1.7) |
| Seattle | 951 (4.7) | 99 (2.4) | 852 (5.3) |
There are 11 observations with missing county-level data.
TABLE A4.
Adjusted and Unadjusted Outcome Measures Among Cohort
| Outcome | Severe v Mild Disability | Moderate v Mild Disability | ||
|---|---|---|---|---|
| Adjustedb | Unadjusted | Adjustedb | Unadjusted | |
| Mortality, months, risk ratios (95% CI) | ||||
| 6 | 2.30 (2.05 to 2.58) | 2.26 (2.01 to 2.54) | 1.32 (1.22 to 1.43) | 1.33 (1.22 to 1.44) |
| 12 | 1.83 (1.67 to 2.00) | 1.82 (1.67 to 1.99) | 1.23 (1.16 to 1.30) | 1.23 (1.17 to 1.31) |
| 24 | 1.52 (1.41 to 1.63) | 1.54 (1.45 to 1.64) | 1.15 (1.10 to 1.20) | 1.16 (1.11 to 1.21) |
| 36 | 1.35 (1.27 to 1.44) | 1.37 (1.30 to 1.45) | 1.10 (1.06 to 1.13) | 1.10 (1.06 to 1.14) |
| 48 | 1.27 (1.20 to 1.34) | 1.30 (1.24 to 1.36) | 1.09 (1.06 to 1.13) | 1.10 (1.07 to 1.14) |
| 60 | 1.21 (1.15 to 1.27) | 1.23 (1.18 to 1.28) | 1.07 (1.04 to 1.10) | 1.07 (1.05 to 1.10) |
| Therapy receipt, hazard ratios (95% CI)a | ||||
| Chemotherapy receipt | 0.70 (0.56 to 0.88) | 1.10 (1.01 to 1.20) | 1.11 (1.02 to 1.20) | 1.10 (1.01 to 1.20) |
| Triplet therapy receipt | 0.57 (0.35 to 0.93) | 1.10 (1.01 to 1.20) | 1.11 (0.94 to 1.32) | 1.10 (1.01 to 1.20) |
| Health care utilization, MCC differences (95% CI)a | ||||
| ED visits | 0.57 (0.10 to 1.05) | 0.71 (0.29 to 1.13) | 0.34 (0.18 to 0.50) | 0.33 (0.18 to 0.48) |
| Hospitalizations | 0.59 (0.26 to 0.93) | 0.60 (0.31 to 0.89) | 0.42 (0.30 to 0.54) | 0.40 (0.28 to 0.52) |
Abbreviations: ED, emergency department; MCC, mean cumulative count; NCI, National Cancer Institute.
Over year after diagnosis.
All adjusted for age, sex, race and ethnicity, and SEER region. Therapy receipt and health care utilization outcomes also adjusted for NCI Comorbidity Index.
DISCLAIMER
The funders had no role in the study design or analysis.
PRIOR PRESENTATION
Presented in part at the American Society of Hematology Annual Meeting, New Orleans, LA, December 10-13, 2022, the ASCO Annual Meeting, Chicago, IL, June 2-6, 2023, and the AcademyHealth Annual Research Meeting, Seattle, WA, June 24-27, 2023.
DATA SHARING STATEMENT
Data used for the current analysis can be obtained via request to SEER-Medicare.
AUTHOR CONTRIBUTIONS
Conception and design: Christopher Edward Jensen, Matthew R. LeBlanc, Christopher D. Baggett, Katherine E. Reeder-Hayes, Jennifer L. Lund
Administrative support: Jennifer L. Lund
Provision of study materials or patients: Jennifer L. Lund
Collection and assembly of data: Christopher D. Baggett, Katherine E. Reeder-Hayes, Jennifer L. Lund
Data analysis and interpretation: All authors
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/cci/author-center.
Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).
Christopher Edward Jensen
Research Funding: Abbvie (Inst)
Open Payments Link: https://openpaymentsdata.cms.gov/physician/1636335
Matthew R. LeBlanc
Consulting or Advisory Role: GlaxoSmithKline
Christopher D. Baggett
Employment: Rho
Emilie D. Duchesneau
Employment: Wake Forest School of Medicine
Katherine E. Reeder-Hayes
Research Funding: Pfizer (Inst), Pfizer (Inst), Pfizer Global Medical Foundation (Inst)
Jennifer L. Lund
Consulting or Advisory Role: COTA Healthcare
No other potential conflicts of interest were reported.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data used for the current analysis can be obtained via request to SEER-Medicare.