Skip to main content
JAMA Network logoLink to JAMA Network
. 2019 Feb 1;2(2):e187633. doi: 10.1001/jamanetworkopen.2018.7633

Association of Care at Minority-Serving vs Non–Minority-Serving Hospitals With Use of Palliative Care Among Racial/Ethnic Minorities With Metastatic Cancer in the United States

Alexander P Cole 1,2, David-Dan Nguyen 1,3, Akezhan Meirkhanov 4, Mehra Golshan 5,6, Nelya Melnitchouk 1,6, Stuart R Lipsitz 1,7, Kerry L Kilbridge 8, Adam S Kibel 2, Zara Cooper 1,6, Joel Weissman 1, Quoc-Dien Trinh 1,2,
PMCID: PMC6484582  PMID: 30707230

This cohort study uses data from the National Cancer Database to assess the association between receipt of treatment at minority-serving vs non–minority-serving hospitals and use of palliative care among racial/ethnic minorities with cancer.

Key Points

Question

Is receipt of treatment at minority-serving hospitals associated with lower use of palliative care among racial/ethnic minorities with cancer compared with non–minority-serving hospitals?

Findings

In this cohort study of 601 680 individuals with metastatic prostate, lung, colon, and breast cancer in the United States, treatment at a minority-serving hospital had a statistically significant association with lower odds of receiving palliative care compared with treatment at a non–minority-serving hospital; patient race/ethnicity did not.

Meaning

Site of care may represent a factor associated with minority patients’ lower odds of receiving palliative care.

Abstract

Importance

It is not known whether racial/ethnic differences in receipt of palliative care are attributable to different treatment of minorities or lower utilization of palliative care at the relatively small number of hospitals that treat a large portion of minority patients.

Objective

To assess the association of receipt of palliative care among patients with metastatic cancer with receipt of treatment at minority-serving hospitals (MSHs) vs non-MSHs.

Design, Setting, and Participants

This retrospective cohort study used Participant Use Files of the National Cancer Database, a prospectively maintained, hospital-based cancer registry consisting of all patients treated at more than 1500 US hospitals, to collect data from individuals older than 40 years with metastatic prostate, lung, colon, and breast cancer, diagnosed from January 1, 2004, to December 31, 2015. Data were accessed in October 2017, and the analysis was performed in July 2018.

Exposures

Hospitals in the top decile in terms of the proportion of black and Hispanic patients for each cancer type were defined as MSHs.

Main Outcomes and Measures

A multilevel logistic regression model that estimated the odds of palliative care was fit, adjusting for year of diagnosis, sex, race/ethnicity, insurance, income, educational level, and cancer type, with an interaction term between cancer type and MSH status and a hospital-level random intercept to account for unmeasured hospital characteristics.

Results

A total of 601 680 individuals (mean [SD] age, 67.4 [11.4] years; 95% CI, 67.2-67.6 years; 314 279 [52.2%] male; 475 039 [78.9%] white) were studied. In total, 130 813 patients (21.7%) received palliative care, ranging from 102 019 (25.4%) with lung cancer to 9966 (11.1%) with colon cancer. In total, 16 435 black individuals (20.0%) and 3551 Hispanic individuals (15.9%) received palliative care vs 106 603 non-Hispanic white individuals (22.5%) (P < .001). The MSH patients were less likely than the non-MSH patients to receive palliative care, regardless of race/ethnicity (12 692 [18.0%] vs 118 121 [22.3%]; P = .002). In an adjusted analysis, treatment at an MSH had a statistically significant association with lower odds of receiving palliative care (odds ratio, 0.67; 95% CI, 0.53-0.84).

Conclusions and Relevance

Although the factors associated with minority patients’ receipt of palliative care are complex, in this study, treatment at MSHs was associated with significantly lower odds of receiving any palliative care in an adjusted analysis, but black and Hispanic race/ethnicity was not. These findings suggest that the site of care is associated with race/ethnicity-based differences in palliative care.

Introduction

Palliative care plays a central role in the management of advanced cancer. Despite advances in targeted chemotherapy and immunotherapy, cancer remains the second leading cause of death in the United States,1 and most patients with metastatic cancer will ultimately die of their disease. For these patients, receipt of palliative care is associated with improved quality of life and prolonged survival.2

The presence of race/ethnicity-based disparities in health care and health outcomes is well documented. Racial/ethnic minorities often receive worse care and have worse outcomes.3 In cancer specifically, there are disparities in screening,4 treatment,5,6 and survival.7 Race/ethnicity-based differences have also been found in receipt of end-of-life care.8,9

Although much research on racial/ethnic differences in care has focused on patient characteristics10 and physician bias,11,12 there is an increasing effort to also investigate the role of the site of care.13,14,15,16,17 Because hospital care for most minority patients is concentrated at a comparatively small number of facilities,18 differences in care at these minority-serving hospitals (MSHs) could explain worse population-level outcomes for minorities overall. If so, policies to improve care at these hospitals represent a potential strategy to address race/ethnicity-based disparities.

We assessed racial/ethnic differences in receipt of palliative care for individuals diagnosed with metastatic prostate, lung, colon, and breast cancer. We examined whether receipt of palliative care differed by site of care and whether racial/ethnic disparities in receipt of palliative care are associated with minority patients receiving treatment in a subset of hospitals where palliative care is less often provided.

Methods

Data Source

The data for this study were abstracted from the Participant Use Files of the National Cancer Database (NCDB), a US cancer registry combining data on patients seen at any 1 of 1500 Commission on Cancer–accredited institutions in the United States.19 The NCDB registry is a joint project of the American Cancer Society and the Commission on Cancer of the American College of Surgeons, comprising more than 29 million unique cases. Trained data abstractors use standardized methods to collect sociodemographic and clinical data, including tumor type, stage, grade, and treatments.20 The NCDB captures 50.8% of all prostate cancers, 82.1% of all lung cancers, 62.5% of all colon cancers, and 66.6% of all breast cancers diagnosed in the United States.21 Data were accessed in October 2017, and the analysis was performed in July 2018. The study was approved by the Brigham and Women’s Hospital Institutional Review Board under a general study protocol for analyses using NCDB data, which included a waiver of informed consent because the information in the Commission on Cancer’s NCDB is deidentified. This study conformed to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for reporting observational research.22

Study Cohort

We chose to focus on men and women 40 years and older with metastatic prostate, non–small cell lung, colon, and breast cancer. These 4 cancer types were chosen because they represented the most common and most lethal cancers for men and women during the study period.23 We chose individuals diagnosed with each cancer from January 1, 2004, to December 31, 2015, using the following International Classification of Diseases for Oncology, Third Edition topography codes: prostate C619, lung C340 to C349, colon C180 to C189 and C260, and breast C500 to C509. We selected men and women with confirmed distant metastases based on the American Joint Committee on Cancer staging system.24 We excluded individuals who had missing follow-up information as well as those diagnosed when younger than 40 years because facility information on these patients is censored by the NCDB for confidentiality purposes.

Receipt of Palliative Care

The main outcome measure was receipt of any palliative care services. Receipt of palliative care is a variable included with the Participant Use Files of the NCDB. Receipt of palliative care is determined by NCDB data abstractors based on patients’ clinical medical records at participating institutions. Treatments are coded as palliative only if it is explicitly mentioned that the goal of treatment is palliation and not cure (eg, pain control after a routine surgical procedure would not be coded as palliative care). Palliative care encompasses surgical treatment, radiation therapy, and systemic chemotherapy administered to alleviate symptoms but not to cure.25 For the purposes of this study, palliative care was treated as a dichotomous variable.

MSH Status

The site of care was the facility reporting the case to the NCDB. This facility is typically the site of diagnosis. For those who received care at multiple institutions, the site of care was the facility where they received definitive cancer care. The MSH status was calculated for each facility based on the proportion of minority patients as follows. First, hospitals were ranked in terms of the proportion of minority patients (black or Hispanic). Second, we identified hospitals in the top decile when ranked from least to greatest proportion of minority patients.26,27 Hospitals in the top decile were considered MSHs. We used the entire population with a diagnosis, not limiting to metastatic cancer only (eg, number of black and Hispanic men with prostate cancer [any stage] at an institution as a portion of the total number of men with prostate cancer [any stage] at that institution and so forth).

Covariates

Baseline sociodemographic covariates included age at diagnosis, sex, race/ethnicity (white non-Hispanic, black non-Hispanic [henceforth referred to as white and black], Hispanic, Asian, other, or unknown), and year of diagnosis. Sociodemographic variables include primary insurance carrier (private, Medicaid or other government payer, Medicare, uninsured, and unknown), educational level (estimated from the percentage of adults within the patient’s zip code without a high school diploma [<7%, 7%-12.9%, 13%-20.9%, or ≥21%]), and zip code–level median household income (<$38 000, $38 000-$47 999, $48 000-$62 999, or ≥$63 000). Clinical covariates included clinical comorbidity (based on the Charlson-Deyo Comorbidity Index, categorized into 0, 1, or ≥2) and cancer type. Because all patients in the cohort had distant metastases (stage IV), we did not adjust by clinical stage. Facility caseload was defined for each cancer as the mean of the total volume of patients with any stage disease treated at the facility for each cancer type in the year of the patient’s diagnosis. This calculation was performed using a previously defined method for NCDB data to account for some facilities leaving and entering the NCDB during the study.28

Statistical Analysis

For each of the 4 cancer types, clinical covariates were compared between patients treated at MSHs and non-MSHs. Clustering was performed at the level of the facility to account for correlation of patient characteristics within hospitals. Means (SDs) were calculated for all continuous variables and proportions for all categorical variables. Given less than 5% of missing data in variables, missing values for covariates were ignored because this has a low probability of skewing results.29 Missing outcome variables (unknown whether palliative care was performed) were assumed to be nonignorable, and a maximum likelihood technique for our multilevel model was used to address this.30 We used χ2 tests with a Rao-Scott adjustment to account for clustering to compare the distribution of covariates between patients treated at MSHs and non-MSHs.31,32 We then performed a univariate analysis, again clustering by facility, to compare the proportion of patients receiving palliative care based on race/ethnicity and other baseline characteristics (eg, site of care, cancer type).

To assess the association among site of care, patient characteristics, cancer type, and palliative care, a multilevel logistic regression model was fit using the entire study population. This model included fixed-effect terms for patient clinical and demographic covariates (including race/ethnicity and cancer type) and site of care (MSH vs non-MSH). We included an interaction term between cancer type and MSH status to assess whether the effect of MSHs differed in a statistically significant fashion among the 4 cancer types. A facility-level random intercept was included to account for unmeasured hospital-level characteristics that might cut across multiple cancers.33 For example, some hospitals may have palliative care departments, whereas others may not.

Finally, based on a significant interaction term (between MSH and cancer type), we performed subgroup analyses by cancer type. For each cancer type, we fit separate multilevel models that assessed the association of clinical and demographic variables as well as site of care on the odds of receiving palliative care.

All analyses were performed with Stata statistical software, version 14.0 (StataCorp). Statistical significance was defined as a 2-sided P < .05.

Results

The study cohort consisted of 601 680 individuals (mean [SD] age, 67.4 [11.4] years; 95% CI, 67.2-67.6 years; 314 279 [52.2%] male; 475 039 [78.9%] white) with metastatic cancer diagnosed from January 1, 2004, to December 31, 2015. There were 44 521 men with metastatic prostate cancer, of whom 7096 (15.9%) were treated at MSHs. There were 402 912 men and women with metastatic non–small cell lung cancer, of whom 43 882 (9.4%) were treated at MSHs. There were 89 826 men and women with metastatic colon cancer, of whom 10 570 (11.8%) were treated at MSHs. Finally, of the 65 380 women and men with metastatic breast cancer, 9166 (14.0%) were treated at MSHs.

For all 4 cancer types, those treated at MSHs had lower educational levels, had lower income, and were less likely to have public insurance. The baseline characteristics of men and women treated for each of the 4 cancer types at MSHs and non-MSHs are summarized in Table 1.

Table 1. Baseline Characteristics of Patients With Metastatic Prostate, Lung, Colon, and Breast Cancer in the National Cancer Database.

Characteristic No. (%) of Patients
Prostate Cancer Non–Small Cell Lung Cancer Colon Cancer Breast Cancer
MSHs Non-MSHs P Valuea MSHs Non-MSHs P Valuea MSHs Non-MSHs P Valuea MSHs Non-MSHs P Valuea
Total patients 7095 (15.9) 37 426 (84.1) NA 43 882 (9.4) 359 030 (90.6) NA 10 570 (11.8) 79 256 (88.2) NA 9166 (14.0) 56 214 (86.0) NA
Palliative care
Yes 831 (11.7) 5962 (16.0) <.001 9452 (21.5) 92567 (25.8) .02 1036 (9.8) 8930 (11.3) .17 1373 (15.0) 10662 (19.0) .003
No 6263 (88.3) 31405 (84.0) 34420 (78.5) 265841 (74.2) 9532 (90.2) 70260 (88.7) 7792 (85.0) 45354 (81.0)
Sex
Male NA NA NA 25 320 (57.7) 198 359 (55.2) <.001 5323 (50.4) 39 810 (50.2) .84 150 (1.6) 796 (1.4) .11
Female NA NA 18 562 (42.3) 160 671 (44.8) 5247 (49.6) 39 446 (49.8) 9016 (98.4) 55 418 (98.6)
Age group, y
40-50 289 (4.1) 1203 (3.2) <.001 4080 (9.3) 24 620 (6.9) <.001 1432 (13.5) 8682 (11.0) <.001 1908 (20.8) 8648 (15.4) <.001
51-60 1572 (22.2) 5628 (15.0) 11 282 (25.7) 71 508 (19.9) 2802 (26.5) 16 202 (20.4) 2816 (30.7) 14 939 (26.6)
61-70 2227 (31.4) 9943 (26.6) 13 502 (30.8) 110 694 (30.8) 2818 (26.7) 19 763 (24.9) 2229 (24.3) 14 866 (26.4)
71-80 1780 (25.1) 10 590 (28.3) 10 549 (24.0) 104 514 (29.1) 2047 (19.4) 18 839 (23.8) 1361 (14.8) 10 465 (18.6)
≥81 1227 (17.3) 10 062 (26.9) 4469 (10.2) 47 694 (13.3) 1471 (13.9) 15 770 (19.9) 852 (9.4) 7296 (13.0)
Race/ethnicity
White 2137 (30.1) 29 250 (78.2) <.001 20 542 (46.8) 306 885 (85.5) <.001 3955 (37.4) 63 648 (80.3) <.001 3368 (36.8) 45 254 (80.5) <.001
Black 3463 (48.8) 5244 (14.0) 16 977 (38.7) 31 507 (8.8) 4425 (41.9) 9630 (12.2) 3916 (42.7) 7100 (12.6)
Hispanic 1196 (16.9) 1538 (4.1) 4255 (9.7) 7865 (2.2) 1727 (16.3) 2723 (3.4) 1407 (15.4) 1728 (3.1)
Asian 164 (2.3) 750 (2.0) 1408 (3.2) 8206 (2.3) 306 (2.9) 2017 (2.5) 280 (3.1) 1231 (2.2)
Other 61 (0.9) 281 (0.7) 287 (0.7) 1929 (0.5) 83 (0.8) 549 (0.7) 76 (0.8) 377 (0.7)
Unknown 74 (1.0) 363 (1.0) 413 (0.9) 2638 (0.7) 74 (0.7) 689 (0.9) 119 (1.3) 524 (0.9)
Year of diagnosis
2004-2006 1717 (24.2) 8350 (22.3) .007 10 716 (24.4) 85 646 (23.9) .30 2081 (19.7) 14 583 (18.4) .19 2027 (22.1) 11 422 (20.3) .02
2007-2009 2048 (28.9) 10 389 (27.8) 12 390 (28.2) 102 357 (28.5) 3101 (29.3) 23 001 (29.0) 2724 (29.7) 16 393 (29.2)
2010-2012 2376 (33.5) 13 172 (35.2) 15 596 (35.5) 126 400 (35.2) 3985 (37.7) 30 695 (38.7) 3262 (35.6) 20 910 (37.2)
2013-2015 954 (13.4) 5515 (14.7) 5180 (11.9) 44 627 (12.4) 1403 (28.7) 10 977 (13.9) 1153 (12.6) 7489 (13.3)
Charlson-Deyo Comorbidity Index
0 5448 (76.8) 28 556 (76.3) .81 28 493 (64.9) 224 356 (62.5) .10 7780 (73.6) 56 907 (71.8) .20 7408 (80.8) 44 836 (79.7) .25
1 1129 (15.9) 6075 (16.2) 10 524 (24.0) 92 953 (25.9) 2054 (19.4) 16 161 (20.4) 1320 (14.4) 8280 (14.8)
≥2 518 (7.3) 2795 (7.5) 4865 (11.1) 41 721 (11.6) 736 (7.0) 6188 (7.8) 438 (4.8) 3098 (5.5)
Insurance
Private 1381 (19.5) 9298 (24.8) <.001 10 721 (24.4) 105 008 (29.3) <.001 2883 (27.3) 26 823 (33.8) <.001 2752 (30.0) 22 142 (39.4) <.001
Medicare 3335 (47.0) 23 539 (62.9) 20 846 (47.5) 205 995 (57.4) 4423 (41.8) 42 910 (54.1) 3061 (33.4) 24 636 (43.8)
Medicaid 1011 (14.2) 1920 (5.1) 5928 (13.5) 22 778 (6.3) 1423 (13.5) 4624 (5.8) 1763 (19.2) 5175 (9.2)
Other governmental 57 (0.8) 423 (1.1) 507 (1.2) 4774 (1.3) 68 (0.6) 654 (0.8) 59 (0.7) 419 (0.7)
None 1011 (14.2) 1518 (4.1) 4328 (9.9) 14 125 (3.9) 1372 (13.0) 3057 (3.9) 1108 (12.1) 2702 (4.8)
Unknown 300 (4.2) 728 (2.0) 1552 (3.5) 6350 (1.8) 401 (3.8) 1188 (1.5) 423 (4.6) 1140 (2.0)
Family income, $b
>63 000 1203 (17.0) 11 159 (29.8) <.001 7252 (16.5) 98 561 (27.4) <.001 1861 (17.6) 24 521 (30.9) <.001 1662 (18.1) 18 222 (32.4) <.001
49 000-63 000 1471 (20.7) 10 069 (26.9) 9047 (20.6) 95 824 (26.7) 2152 (20.4) 20 713 (26.1) 2003 (21.9) 14 918 (26.5)
38 000-48 999 1498 (21.1) 8887 (23.7) 9492 (21.7) 90 470 (25.2) 2381 (22.5) 18 684 (23.6) 1996 (21.8) 12 729 (22.7)
<38 000 2830 (39.9) 6679 (17.9) 17160 (39.1) 65337 (18.2) 3976 (37.6) 13 643 (17.2) 3357 (36.6) 9340 (16.6)
Unknown 93 (1.3) 632 (1.7) 931 (2.1) 8838 (2.5) 200 (1.9) 1695 (2.2) 148 (1.6) 1005 (1.8)
Educational level, % without high school diplomab
Unknown 89 (1.3) 601 (1.6) <.001 910 (2.1) 8645 (2.4) <.001 198 (1.9) 1654 (2.1) <.001 144 (1.6) 973 (1.7) <.001
<7 595 (8.4) 9018 (24.1) 4024 (9.2) 73 948 (20.6) 1032 (9.8) 17 938 (22.6) 934 (10.2) 13 331 (23.7)
7-12.9 1211 (17.0) 12 328 (32.9) 8787 (20.0) 119 752 (33.4) 2068 (19.6) 25 854 (32.6) 1756 (19.2) 18 982 (33.8)
13-20.9 2108 (29.7) 9515 (25.5) 13 629 (31.0) 98 698 (27.5) 3241 (30.6) 21 094 (26.6) 2865 (31.3) 14 218 (25.3)
>30 3092 (43.6) 5964 (15.9) 16 532 (37.7) 57 987 (16.2) 4031 (38.1) 12 716 (16.1) 3467 (37.8) 8710 (15.5)

Abbreviations: MSH, minority-serving hospital; NA, not applicable.

a

Hospital-level clustering with Taylor series linearization; the Pearson χ2 test was used to test significance.

b

Both estimated using patients’ county of residence.

In the combined cohort, 130 813 patients (21.7%) received any palliative care and 470 867 (78.1%) did not. The number of patients receiving palliative care differed based on cancer type. The number of patients receiving palliative care was 6793 (15.3%) of those with metastatic prostate cancer, 102 019 (25.4%) of those with metastatic lung cancer, 9966 (11.1%) of those with metastatic colon cancer, and 120 035 (18.5%) of those with metastatic breast cancer (P < .001). In terms of race/ethnicity, whereas 106 603 white patients (22.5%) received palliative care, only 16 435 black patients (20.0%) and 3551 Hispanic patients (15.9%) received palliative care (P < .001 for all). Patients treated at an MSH were less likely than patients treated at a non-MSH to receive palliative care regardless of race/ethnicity (12 692 [18.0%] vs 118 121 [22.3%], P = .002). Receipt of palliative care based on other baseline characteristics is summarized in Table 2.

Table 2. Unadjusted Proportions of Patients With Metastatic Cancer Receiving Palliative Care in Overall Cohort by Baseline Characteristics.

Characteristic No. (%) of Patients P Valuea
No Palliative Care Any Palliative Care
Total patients 470 867 (78.1) 130 813 (21.7) NA
Hospital type
MSH 58 007 (82.1) 12 692 (18.0) .002
Non-MSH 412 860 (77.8) 118 121 (22.3)
Cancer type
Prostate 37 668 (84.7) 6793 (15.3) <.001
Non–small cell lung 300 261 (74.6) 102 019 (25.4)
Colon 79 792 (88.9) 9966 (11.1)
Breast 53 146 (81.5) 12 035 (18.5)
Sex
Male 244 246 (77.8) 69 571 (22.2) <.001
Female 226 621 (78.7) 61 242 (21.3)
Age group, y
≤50 39 413 (77.7) 11 305 (22.3) <.001
51-60 97 147 (76.8) 29 331 (23.2)
61-70 136 217 (77.5) 39 545 (22.5)
71-80 125 971 (78.8) 33 977 (21.2)
≥81 72 119 (81.2) 16 655 (18.8)
Race/ethnicity
White 367 695 (77.5) 106 603 (22.5) <.001
Black 65 716 (80.0) 16 435 (20.0)
Hispanic 18 814 (84.1) 3551 (15.9)
Asian 11 782 (82.1) 2572 (17.9)
Other 2879 (79.5) 741 (20.5)
Unknown 3981 (81.4) 911 (18.6)
Year of diagnosis
2004-2006 108 557 (79.7) 27 602 (20.3) <.001
2007-2009 135 234 (78.6) 36 847 (21.4)
2010-2012 168 279 (77.8) 47 907 (22.2)
2013-2015 58 797 (76.1) 18 457 (23.9)
Charlson-Deyo Comorbidity Index
0 318 898 (79.1) 84 038 (20.9) <.001
1 105 984 (76.6) 32 434 (23.4)
≥2 45 985 (76.2) 14 341 (23.8)
Insurance
Private 141 937 (78.5) 38 986 (21.6) <.001
Medicare 257 850 (78.5) 70 705 (21.5)
Medicaid 33 980 (76.2) 10 624 (23.8)
Other governmental 5143 (73.9) 1816 (26.1)
None 22 424 (76.8) 6784 (23.2)
Unknown 9533 (83.4) 1898 (23.4)
Family income, $a
>63 000 130 096 (79.2) 34 101 (20.8) .02
49 000-63 000 122 089 (78.3) 33 879 (21.7)
38 000-48 999 113 059 (77.5) 32 849 (22.5)
<38 000 95 269 (78.0) 26 824 (22.0)
Unknown 10 354 (76.6) 3160 (16.6)
Educational level, % without high school diplomaa
<7 94 022 (77.9) 26 614 (22.1) <.001
7-12.9 147 688 (77.5) 42 865 (22.5)
13-20.9 129 068 (78.2) 36 018 (21.8)
>30 89 998 (80.2) 22 220 (19.8)
Unknown 10 091 (76.5) 3096 (23.5)

Abbreviations: MSH, minority-serving hospital; NA, not applicable.

a

Estimated from patients’ county of residence.

In our adjusted multilevel logistic regression model adjusting for age, race/ethnicity, comorbidity, cancer type, and patient demographics and including an interaction term between MSH status and cancer type, patients who received care at an MSH had two-thirds the odds of receiving palliative care compared with those who received care at a non-MSH (odds ratio [OR], 0.67; 95% CI, 0.53-0.84). Later study year was also associated with increased odds of receiving palliative care (first vs last period: OR, 1.30; 95% CI, 1.27-1.33). Patients with Medicaid and uninsured patients were more likely to receive palliative care compared with those with private insurance (Medicaid vs private: OR, 1.16 [95% CI, 1.13-1.19]; uninsured vs private: OR, 1.17 [95% CI, 1.13-1.21]).

After adjusting for MSH status and other covariates, the difference in receipt of palliative care between white and black individuals was no longer statistically significant (OR, 1.02; 95% CI, 0.99-1.04). Hispanic patients had higher odds of palliative care compared with white patients (OR, 1.06; 95% CI, 1.01-1.10). Compared with non-Hispanic white patients, a lower proportion of Asian patients received palliative care (2572 [17.9%] vs 106 603 [22.5%], P < .001). This finding was also true on adjusted analyses (OR, 0.93; 95% CI, 0.88-0.98). Table 3 provides a summary of the adjusted analyses.

Table 3. Factors Associated With Palliative Care in an Adjusted Multilevel Model Including a Hospital-Level Random Intercept .

Indicator Odds Ratio (95% CI) P Valuea
Hospital type
Non-MSH 1 [Reference] NA
MSH 0.67 (0.53-0.84) .001
Metastatic cancer type
Prostate 1 [Reference] NA
Non–small cell lung 1.69 (1.51-1.88) <.001
Colon 0.56 (0.50-0.63) <.001
Breast 1.10 (0.98-1.23) .08
Sex
Male 1 [Reference] NA
Female 0.95 (0.94-0.97) <.001
Age group, y
≤50 1 [Reference] NA
51-60 1.00 (0.97-1.03) .89
61-70 0.93 (0.90-0.95) <.001
71-80 0.86 (0.83-0.88) <.001
≥81 0.79 (0.77-0.82) <.001
Race/ethnicity
White 1 [Reference] NA
Black non-Hispanic 1.02 (0.99-1.04) .19
Hispanic 1.06 (1.01-1.10) .01
Asian 0.93 (0.88-0.98) .008
Other 0.92 (0.85-1.01) .08
Unknown 0.78 (0.72-0.84) <.001
Year of diagnosis
2004-2006 1 [Reference] NA
2007-2009 1.10 (1.08-1.12) <.001
2010-2012 1.16 (1.14-1.18) <.001
2013-2015 1.30 (1.27-1.33) <.001
Charlson-Deyo Comorbidity Index
0 1 [Reference] NA
1 1.01 (0.99-1.03) .18
≥2 1.00 (0.98-1.03) .70
Insurance
Private 1 [Reference] NA
Medicare 1.01 (0.99-1.03) .14
Medicaid 1.16 (1.13-1.19) <.001
Other governmental 1.20 (1.13-1.27) <.001
None 1.17 (1.13-1.21) <.001
Unknown 0.87 (0.82-0.92) <.001
Family income, $a
>63 000 1 [Reference] NA
49 000-63 000 0.99 (0.97-1.01) .39
38 000-48 999 0.97 (0.95-1.00) .06
<38 000 0.99 (0.96-1.02) .46
Unknown 0.87 (0.65-1.16) .34
Educational level, % without high school diplomaa
>30 1 [Reference] NA
13-20.9 1.00 (0.98-1.02) .94
7-12.9 1.00 (0.97-1.03) .99
<7 1.00 (0.97-1.03) .98
Unknown 1.24 (0.92-1.66) .16

Abbreviations: MSH, minority-serving hospital; NA, not applicable.

a

Estimated from county of residence.

The interaction term between cancer type and MSH status was associated with receipt of palliative care. Thus, we performed a subgroup analysis stratifying by cancer type. In the metastatic prostate cancer subgroup, the odds of receiving palliative care at MSHs were approximately 33% lower (OR, 0.67; 95% CI, 0.55-0.82); in the lung cancer subgroup, the odds of palliative care were 27% lower at MSHs (OR, 0.73; 95% CI, 0.57-0.93); in colon cancer, the odds of palliative care at MSHs were not significantly lower (OR, 0.86; 95% CI, 0.67-1.09); and in breast cancer, the odds of palliative care were 27% lower (OR, 0.73; 95% CI, 0.59-0.89). As in the combined cohort, adjustment for MSH status in all cancers attenuated the association between race/ethnicity and odds of receiving palliative care toward the null.

Discussion

In this retrospective, registry-based study of adults diagnosed with metastatic prostate, lung, breast, and colon cancer, there were significantly lower odds of receiving palliative care among patients treated at MSHs compared with non-MSHs. Although it has been previously reported that minority patients are less likely to receive palliative care services at the end of life,8,9 the present findings suggest that site of care may be a significant factor associated with race/ethnicity-based differences in palliative care.

The policy implications of this finding are significant. Given that care for minority patients is concentrated at a comparatively small number of hospitals in the United States, it is likely that one important strategy to address racial/ethnic disparities in palliative care is to focus on improving access to palliative care at the small number of hospitals that care for most minority patients. If initiatives to target palliative care use at MSHs are successful, national disparities in palliative care may be reduced.

Overall, this fits with an increasing understanding that the site of care is a determinant of health outcomes for minority patients. Although there are data that physicians may systematically treat black and white patients differently,11,12 that minority patients tend to receive care at different facilities is also important. More than being a function of individual behavior, there is increasing recognition that disparities in outcomes depend on different treatment of white and minority patients within the same hospital and systemic differences in where minority patients receive care.14,15

A previous study18 found that MSHs have higher readmission rates and worse performance in many clinical scenarios, for example, when treating acute myocardial infarctions and pneumonia. A study34 of emergency general surgery at MSHs found that hospital-level factors accounted for approximately 40% of increased odds for readmission, and inpatient mortality was significantly greater. Hospital leadership can also play an important role. A survey of chairmen at black-serving hospitals found that, when compared with non–black-serving hospital boards, these chairpersons report less expertise with quality-of-care issues and are less likely to give high priority to quality of care.35 An analysis36 of racial disparity in surgical mortality found that although gaps between black and white patients have narrowed overall, improvements were less likely among hospitals that served the highest proportion of minority patients. Overall, our findings suggest that similar systemic differences between MSHs and non-MSHs may be associated with the differences in receipt of palliative care among underserved minority patients.

Although Asian patients composed a small proportion of our population, they were less likely to receive palliative care after adjusting for MSH status. Asian individuals are a heterogeneous group and may in some cases have better access to health care compared with Hispanic patients and black patients; Asian individuals have population-level health outcomes that exceed most of the other racial/ethnic groups.37 Thus, as has been done in a prior study,27 we did not include Asian patients in our definition of MSHs. The lower odds of palliative care among Asian patients could reflect cultural differences, differences in familial characteristics among this population, and other economic or health systems factors.

The finding that palliative care is more common in Medicaid patients and uninsured patients was similarly surprising given that these patients seem to receive worse care based on many other health metrics.38 Perhaps these patients were presenting at a more advanced stage of disease, when palliative care is the only good option. Alternatively, perhaps the absence of a strong fee-for-service incentive toward doing more reduced the barrier for palliative care for the Medicaid and uninsured patients.

Strengths and Limitations

Strengths of our study include our use of a large, accurate national registry, which captures most US patients diagnosed with 4 highly prevalent types of cancers. Another strength is that our study included patients from all payers. Our work therefore improves on earlier definitions of minority serving, which often used Medicare claims and therefore involved only the proportion of Medicare beneficiaries who were racial/ethnic minorities not the proportion of patients with a given condition.26

Despite these strengths, this work has limitations. Data on palliative care services are of uncertain accuracy. The data on receipt of palliative care in the NCDB were collected from medical records by trained data abstractors at each institution. Intent must be inferred from clinical records. Although we believe that record review may be more accurate than insurance claims, which have been reported to often have only moderate accuracy for ascertaining the intensity of end-of-life care,39 the accuracy may be lower than some prospective trials that have specifically assigned patients to palliative care interventions.2 Additional studies that specifically address interrater variability and validate this variable against other end points (eg, inappropriately aggressive end-of-life care, such as chemotherapy in the last 14 days of life, death in hospital, or death in the intensive care unit) would be useful. Another limitation is the possibility of unmeasured patient confounders, which are always a factor in retrospective research. Our use of a multilevel model with a hospital-level random intercept should account for unmeasured hospital characteristics at the level of the hospital (eg, some hospitals may have palliative care departments, whereas others may not).

Although the NCDB captures most patients with each of these 4 cancer types in the United States, data are not population based. Thus, certain patients who did not receive care at Commission on Cancer–accredited US hospitals may have been underrepresented. For example, if the database underrepresents poor-performing, rural non-MSHs, the disparities among MSHs could be inflated.

Conclusions

These findings suggest that there are significant racial/ethnic disparities in receipt of palliative care for metastatic cancer within a large cohort of US patients with cancer. After controlling for race/ethnicity and MSH status, we found that treatment at MSHs was associated with significantly lower odds of receiving palliative care, but black and Hispanic race/ethnicity was not. Strategies that focus on improving palliative care use at MSHs may be an effective strategy to increase the receipt of palliative care for this population.

References

  • 1.Centers for Disease Control and Prevention Deaths, percent of total deaths, and death rates for the 15 leading causes of death: United States and each state, 1999-2015. National Vital Statistics System. https://www.cdc.gov/nchs/nvss/mortality/lcwk9.htm. Accessed December 19, 2018.
  • 2.Temel JS, Greer JA, Muzikansky A, et al. . Early palliative care for patients with metastatic non-small-cell lung cancer. N Engl J Med. 2010;363(8):-. doi: 10.1056/NEJMoa1000678 [DOI] [PubMed] [Google Scholar]
  • 3.Nelson AR, Stith AY, Smedley BD. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care (Full Printed Version). Washington, DC: National Academies Press; 2002. [PubMed] [Google Scholar]
  • 4.Schenck AP, Peacock SC, Klabunde CN, Lapin P, Coan JF, Brown ML. Trends in colorectal cancer test use in the medicare population, 1998-2005. Am J Prev Med. 2009;37(1):1-7. doi: 10.1016/j.amepre.2009.03.009 [DOI] [PubMed] [Google Scholar]
  • 5.Friedlander DF, Trinh QD, Krasnova A, et al. . Racial disparity in delivering definitive therapy for intermediate/high-risk localized prostate cancer: the impact of facility features and socioeconomic characteristics [published online April 1, 2017]. Eur Urol. doi: 10.1016/j.eururo.2017.07.023 [DOI] [PubMed] [Google Scholar]
  • 6.Trinh QD, Sun M, Sammon J, et al. . Disparities in access to care at high-volume institutions for uro-oncologic procedures. Cancer. 2012;118(18):4421-4426. doi: 10.1002/cncr.27440 [DOI] [PubMed] [Google Scholar]
  • 7.Ward E, Jemal A, Cokkinides V, et al. . Cancer disparities by race/ethnicity and socioeconomic status. CA Cancer J Clin. 2004;54(2):78-93. doi: 10.3322/canjclin.54.2.78 [DOI] [PubMed] [Google Scholar]
  • 8.Hernandez RA, Hevelone ND, Lopez L, Finlayson SR, Chittenden E, Cooper Z. Racial variation in the use of life-sustaining treatments among patients who die after major elective surgery. Am J Surg. 2015;210(1):52-58. doi: 10.1016/j.amjsurg.2014.08.025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Smith AK, Earle CC, McCarthy EP. Racial and ethnic differences in end-of-life care in fee-for-service Medicare beneficiaries with advanced cancer. J Am Geriatr Soc. 2009;57(1):153-158. doi: 10.1111/j.1532-5415.2008.02081.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Torain MJ, Maragh-Bass AC, Dankwa-Mullen I, et al. . Surgical disparities: a comprehensive review and new conceptual framework. J Am Coll Surg. 2016;223(2):408-418. doi: 10.1016/j.jamcollsurg.2016.04.047 [DOI] [PubMed] [Google Scholar]
  • 11.Haider AH, Schneider EB, Sriram N, et al. . Unconscious race and social class bias among acute care surgical clinicians and clinical treatment decisions. JAMA Surg. 2015;150(5):457-464. doi: 10.1001/jamasurg.2014.4038 [DOI] [PubMed] [Google Scholar]
  • 12.Schulman KA, Berlin JA, Harless W, et al. . The effect of race and sex on physicians’ recommendations for cardiac catheterization. N Engl J Med. 1999;340(8):618-626. doi: 10.1056/NEJM199902253400806 [DOI] [PubMed] [Google Scholar]
  • 13.Thomas SB, Quinn SC, Butler J, Fryer CS, Garza MA. Toward a fourth generation of disparities research to achieve health equity. Annu Rev Public Health. 2011;32:399-416. doi: 10.1146/annurev-publhealth-031210-101136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Hasnain-Wynia R, Kang R, Landrum MB, Vogeli C, Baker DW, Weissman JS. Racial and ethnic disparities within and between hospitals for inpatient quality of care: an examination of patient-level Hospital Quality Alliance measures. J Health Care Poor Underserved. 2010;21(2):629-648. doi: 10.1353/hpu.0.0281 [DOI] [PubMed] [Google Scholar]
  • 15.Hasnain-Wynia R, Baker DW, Nerenz D, et al. . Disparities in health care are driven by where minority patients seek care: examination of the hospital quality alliance measures. Arch Intern Med. 2007;167(12):1233-1239. doi: 10.1001/archinte.167.12.1233 [DOI] [PubMed] [Google Scholar]
  • 16.Weissman JS, Hasnain-Wynia R, Weinick RM, et al. . Pay-for-performance programs to reduce racial/ethnic disparities: what might different designs achieve? J Health Care Poor Underserved. 2012;23(1):144-160. doi: 10.1353/hpu.2012.0030 [DOI] [PubMed] [Google Scholar]
  • 17.Barnato AE, Lucas FL, Staiger D, Wennberg DE, Chandra A. Hospital-level racial disparities in acute myocardial infarction treatment and outcomes. Med Care. 2005;43(4):308-319. doi: 10.1097/01.mlr.0000156848.62086.06 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Jha AK, Orav EJ, Li Z, Epstein AM. Concentration and quality of hospitals that care for elderly black patients. Arch Intern Med. 2007;167(11):1177-1182. doi: 10.1001/archinte.167.11.1177 [DOI] [PubMed] [Google Scholar]
  • 19.Cole AP, Friedlander DF, Trinh QD. Secondary data sources for health services research in urologic oncology. Urol Oncol. 2018;36(4):165-173. doi: 10.1016/j.urolonc.2017.08.008 [DOI] [PubMed] [Google Scholar]
  • 20.Winchester DP, Stewart AK, Bura C, Jones RS. The National Cancer Data Base: a clinical surveillance and quality improvement tool. J Surg Oncol. 2004;85(1):1-3. doi: 10.1002/jso.10320 [DOI] [PubMed] [Google Scholar]
  • 21.Bilimoria KY, Stewart AK, Winchester DP, Ko CY. The National Cancer Data Base: a powerful initiative to improve cancer care in the United States. Ann Surg Oncol. 2008;15(3):683-690. doi: 10.1245/s10434-007-9747-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative . Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ. 2007;335(7624):806-808. doi: 10.1136/bmj.39335.541782.AD [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Jemal A, Siegel R, Xu J, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010;60(5):277-300. doi: 10.3322/caac.20073 [DOI] [PubMed] [Google Scholar]
  • 24.Edge SB, Compton CC The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 2010;17(6):1471-1474. [DOI] [PubMed] [Google Scholar]
  • 25.NCDB Data Dictionary Palliative Care. American College of Surgeons. http://ncdbpuf.facs.org/content/palliative-care. Accessed August 26, 2018.
  • 26.Joynt KE, Orav EJ, Jha AK. Thirty-day readmission rates for Medicare beneficiaries by race and site of care. JAMA. 2011;305(7):675-681. doi: 10.1001/jama.2011.123 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Fletcher SA, Gild P, Cole AP, et al. . The effect of treatment at minority-serving hospitals on outcomes for bladder cancer. Urol Oncol. 2018;36(5):238.e7-238.e17. doi: 10.1016/j.urolonc.2018.01.010 [DOI] [PubMed] [Google Scholar]
  • 28.Cole AP, Sun M, Lipsitz SR, Sood A, Kibel AS, Trinh QD. Reassessing the value of high-volume cancer care in the era of precision medicine. Cancer. 2018;124(7):1319-1321. doi: 10.1002/cncr.31254 [DOI] [PubMed] [Google Scholar]
  • 29.Graham JW. Missing data analysis: making it work in the real world. Annu Rev Psychol. 2009;60:549-576. doi: 10.1146/annurev.psych.58.110405.085530 [DOI] [PubMed] [Google Scholar]
  • 30.Ibrahim JG, Chen MH, Lipsitz SR. Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. Biometrika. 2001;88(2):551-564. doi: 10.1093/biomet/88.2.551 [DOI] [Google Scholar]
  • 31.Rao J, Scott A. On simple adjustments to chi-square tests with sample survey data. Ann Stat. 1987:385-397. doi: 10.1214/aos/1176350273 [DOI] [Google Scholar]
  • 32.Lipsitz SR, Fitzmaurice GM, Sinha D, Hevelone N, Giovannucci E, Hu JC. Testing for independence in J×K contingency tables with complex sample survey data. Biometrics. 2015;71(3):832-840. doi: 10.1111/biom.12297 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Cole AP, Trinh QD. Secondary data analysis: techniques for comparing interventions and their limitations. Curr Opin Urol. 2017;27(4):354-359. doi: 10.1097/MOU.0000000000000407 [DOI] [PubMed] [Google Scholar]
  • 34.Zogg CK, Jiang W, Chaudhary MA, et al. . Racial disparities in emergency general surgery: do differences in outcomes persist among universally insured military patients? J Trauma Acute Care Surg. 2016;80(5):764-775. doi: 10.1097/TA.0000000000001004 [DOI] [PubMed] [Google Scholar]
  • 35.Jha AK, Epstein AM. Governance around quality of care at hospitals that disproportionately care for black patients. J Gen Intern Med. 2012;27(3):297-303. doi: 10.1007/s11606-011-1880-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Mehtsun WT, Figueroa JF, Zheng J, Orav EJ, Jha AK. Racial disparities in surgical mortality: the gap appears to have narrowed. Health Aff (Millwood). 2017;36(6):1057-1064. doi: 10.1377/hlthaff.2017.0061 [DOI] [PubMed] [Google Scholar]
  • 37.Trinh QD, Nguyen PL, Leow JJ, et al. . Cancer-specific mortality of Asian Americans diagnosed with cancer: a nationwide population-based assessment. J Natl Cancer Inst. 2015;107(6):djv054. doi: 10.1093/jnci/djv054 [DOI] [PubMed] [Google Scholar]
  • 38.Walker GV, Grant SR, Guadagnolo BA, et al. . Disparities in stage at diagnosis, treatment, and survival in nonelderly adult patients with cancer according to insurance status. J Clin Oncol. 2014;32(28):3118-3125. doi: 10.1200/JCO.2014.55.6258 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Earle CC, Neville BA. Under use of necessary care among cancer survivors. Cancer. 2004;101(8):1712-1719. doi: 10.1002/cncr.20560 [DOI] [PubMed] [Google Scholar]

Articles from JAMA Network Open are provided here courtesy of American Medical Association

RESOURCES