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631 The Impact of Insurance Status at Diagnosis on Overall Survival in Chronic Myeloid Leukemia: A Population-Based Analysis

Health Services and Outcomes Research – Malignant Diseases
Program: Oral and Poster Abstracts
Type: Oral
Session: 902. Health Services and Outcomes Research – Malignant Diseases: Clinical Trials and Health Outcomes
Monday, December 7, 2015: 10:30 AM
Chapin Theater (W320), Level 3 (Orange County Convention Center)

Ashley M. Perry, BS1*, Tao Zou, MD, PhD1*, Andrew M. Brunner, MD1,2, Donna S Neuberg, ScD3 and Amir T. Fathi, MD1

1Massachusetts General Hospital, Boston, MA
2Dana-Farber Cancer Institute, Boston, MA
3Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA

Introduction:

Survival among patients diagnosed with chronic myeloid leukemia (CML) has markedly improved with the advent of tyrosine kinase inhibitors. Nonetheless, access to care, including medication cost and adherence, may be barriers to therapeutic effectiveness.  We performed a population-based analysis to determine if insurance status at the time of CML diagnosis influenced patient outcomes.

Methods:

We used the Surveillance, Epidemiology, and End Results Program (SEER) database (November 2014 submission) to identify patients age 15 or older, diagnosed with CML between 2007 and 2012 (SEER ICD-O3 recodes 9863 and 9875). We included patients with documented insurance status at diagnosis and categorized them as either private insurance, Medicaid coverage, or uninsured. We excluded patients with unknown insurance status at diagnosis. The primary outcome was overall survival according to insurance status. We performed a stratified analysis looking at patients age 15-64 and patients 65 or older; we did not include uninsured patients over age 65 in the analysis (n=16) due to Medicare eligibility.  Covariates of interest in multivariable analysis included age at diagnosis, race, ethnicity, sex, and marital status at diagnosis. Overall survival was compared by log-rank test and estimated by the method of Kaplan and Meier. P-values were significant to the 2-sided 0.05 level.

Results:

5784 patients were diagnosed with CML between 2007 and 2012 and had insurance status documented at diagnosis. Of patients age 15-64, uninsured and Medicaid patients were younger, more often non-white race and Hispanic ethnicity, and less often married (Table 1). Over age 65, Medicaid patients were more often female, non-white race and Hispanic ethnicity, and less often married.

Median follow up was 32 months. Among patients age 15 to 64, being uninsured or having Medicaid was associated with worse survival compared to insured patients (5-year OS uninsured 72.7%, Medicaid 73.1%, insured 86.6%, p<0.0001) (Figure 1A). For patients over age 65, there was no difference in 5-year OS between patients with Medicaid and those with other insurance (40.2% vs. 43.4%, p=0.0802).

In multivariable analysis of patients age 15-64, compared to insured patients, there was increased mortality among patients who were uninsured (HR 2.156, p<0.0001) or on Medicaid (HR 1.972, p<0.0001). There was worse survival with increased age (HR 1.046 per year, p<0.0001), male sex (HR 1.282, p=0.0279) and, compared to married persons, being single (HR 1.883, p<0.0001). For patients over age 65 at diagnosis, only age was associated with increased mortality (HR 1.078 per year, p<0.0001).

 

Conclusions:

CML patients under age 65 without insurance or with Medicaid had significantly worse survival compared to patients with insurance. This difference was not noted with patients over age 65; whose survival was relatively poorer regardless of insurance status, as previously described (Cancer 2013;119:2620). Marital status and race/ethnicity also impacted survival. Despite highly effective therapies currently available for CML, these findings suggest that many patients may not have access to or receive appropriate care, in part related to insurance coverage.

Figure 1. Survival of patients (A) age 15-64 and (B) age 65+ by insurance status at diagnosis.

Figure1

Table 1. Patient Demographics Age 15-64 p-value Age 65+ p-value
(3626 patients) (2142 patients)
Uninsured Medicaid Insured Medicaid Insured
Total, n (%) 321 (8.9%) 595 (16.4%) 2710 (74.7%)   190 (8.9%) 1952 (91.1%)  
Age, median (range) 44 (18-64) 45 (15-64) 50 (15-64) <0.0001 75 (65-97) 76 (65-102) 0.5388
Gender, n (%)       0.0482     0.0074
Male 203 (63%) 328 (55%) 1603 (59%)   86 (45%) 1087 (56%)  
Female 118 (37%) 267 (45%) 1107 (41%)   105 (55%) 865 (44%)  
Race, n (%)       <0.0001     <0.0001
White 231 (72%) 402 (68%) 2112 (78%)   131 (69%) 1724 (89%)  
Black 68 (21%) 114 (19%) 313 (12%)   26 (14%) 144 (7%)  
American Indian 2 (1%) 25 (4%) 13 (0.5%)   2 (1%) 7 (0.4%)  
Asian, Pacific Islander 15 (5%) 48 (8%) 215 (8%)   30 (16%) 65 (3%)  
Unknown 5 (2%) 6 (1%) 57 (2%)   1 (0.5%) 12 (0.6%)  
Hispanic Ethnicity, n (%)       <0.0001     <0.0001
Non- 234 (73%) 420 (71%) 2341 (86%)   151 (79%) 1813 (93%)  
Hispanic
Hispanic 87 (27%) 175 (29%) 369 (14%)   39 (21%) 139 (7%)  
Marital Status, n (%)       <0.0001     <0.0001
Single 141 (46%) 281 (50%) 588 (23%)   41 (23%) 153 (9%)  
Married/partner 123 (40%) 194 (34%) 1652 (65%)   67 (37%) 1069 (59%)  
Divorced/separated/widowed 45 (15%) 90 (16%) 288 (11%)   72 (40%) 578 (32%)  

Disclosures: Fathi: Seattle Genetics: Membership on an entity’s Board of Directors or advisory committees , Research Funding ; Takeda Pharmaceuticals International Co.: Research Funding ; Ariad: Consultancy ; Exelexis: Research Funding ; Agios: Membership on an entity’s Board of Directors or advisory committees ; Merck: Membership on an entity’s Board of Directors or advisory committees .

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*signifies non-member of ASH