Visual Abstract

Comprehensive cancer treatment approach incorporating Social Determinants of Health. Created with BioRender.com.

Comprehensive cancer treatment approach incorporating Social Determinants of Health. Created with BioRender.com.

Social determinants of health (SDHs) have been reported as relevant factors responsible for health inequity. We sought to assess clinical data from observational studies conducted in the United States evaluating the impact of SDHs on the outcomes of patients with hematologic malignancies. Thus, we performed a systematic review in 6 databases on 1 September 2021, in which paired reviewers independently screened studies and included data from 41 studies. We assessed the risk of bias using the Joanna Briggs Institute appraisal tools and analyzed the data using a descriptive synthesis. The most common SDH domains explored were health care access and quality (54.3%) and economic stability (25.6%); others investigated were education (19%) and social and community context (7.8%). We identified strong evidence of 5 variables significantly affecting survival: lack of health insurance coverage or having Medicare or Medicaid insurance, receiving cancer treatment at a nonacademic facility, low household income, low education level, and being unmarried. In contrast, the reports on the effect of distance traveled to the treatment center are contradictory. Other SDHs examined were facility volume, provider expertise, poverty, and employment rates. We identified a lack of data in the literature in terms of transportation, debt, higher education, diet, social integration, environmental factors, or stress. Our results underscore the complex nature of social, financial, and health care barriers as intercorrelated variables. Therefore, the management of hematologic malignancies needs concerted efforts to incorporate SDHs into clinical care, research, and public health policies, identifying and addressing the barriers at a patient-based level to enhance outcome equity (PROSPERO CRD42022346854)

Over the last decades, there have been significant improvements in the survival expectation of patients with hematologic malignancies.1 Such progress in outcomes has been possible through a combination of advances in cancer biology research, the implementation of more accurate risk and prognostic scoring systems, and the breakthrough of novel drugs and subsequent lines of therapies.2,3 Nevertheless, this enhancement has not been experienced equally across all populations, and certain groups experience disproportionately higher mortality rates.4 

The prevalence of cancer outcome inequity can be attributed to the complex interaction among multiple aspects, including genetic factors, health behaviors, and social determinants of health (SDHs).5 SDHs are defined as the set of nonbiological factors and systems that shape the environment of daily life, such as the conditions where people are born, grow, work, live, and age, and affect health outcomes. SDHs are categorized into 5 key domains: health care access and quality, education access and quality, social and community context, economic stability, and neighborhood and built environment.6 

Economic instability, a lower education level, decreased access to health care, residential segregation, discrimination, and a lack of social support systems have been linked to lower cancer screening rates, diagnosis at advanced stages, and worse cancer survival.7-10 Although that evidence has enormously contributed to our understanding of the factors that underlie the inequity, the studies reporting on this topic have varied on the specific SDH analyzed, the scope and size of the population studied, and the reported influence of SDHs.

To better assess the impact of SDHs on cancer-treatment outcomes, we performed a systematic review to understand the degree to which multiple factors may contribute to cancer outcome disparities. Identifying these influences is an important step in building strategies and interventions to address the SDHs and accelerate progress toward health equity in cancer. Our broad review included all cancer types, and this article focused on reporting the findings for hematologic malignancies affecting adult patients.

We conducted and reported this systematic review in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines (supplemental Tables 1 and 2).11 We registered our protocol at the Prospective Register of Systematic Reviews, which can be accessed through the registration number CRD42022346854.

Eligibility criteria

We based our inclusion criteria on the PECOS approach (population, exposure, comparison, outcome), consisting of observational studies investigating any SDH's impact on cancer-treatment survival. Because of the cross-national differences in social behaviors, economic conditions, and education and health systems, we limited our review to those studies, including only data from the United States from 2002 onward. We focused our review on recognizing actionable SDHs that clinicians could identify and address in routine clinical practice. Thus, we excluded studies for the following reasons: (1) studies that evaluated sex, age, race/ethnicity, geography, and rurality as SDH; (2) studies that did not assess the relationship between SDHs and cancer survival; (3) studies in which the results according to cancer type cannot be individualized; (4) studies written in languages other than English; and (5) reviews, letters, conference articles, personal opinions, and book chapters.

Information sources and search strategy

We performed a customized literature search using PubMed, Cochrane, EMBASE, Scopus, and the Web of Science databases. We conducted an additional search in the gray literature using Google Scholar and examined the reference lists of included studies (supplemental Table 3). Our searches included articles published on or after 1 January 2002 until 1 September 2022. We used the reference manager software EndNote 20.2.1 (Clarivate Analytics)12 to export references and remove duplicate articles.

Studies selection process

We identified relevant studies in 2 phases. In phase 1, we imported references into Rayyan Systems Inc software,13 where 2 authors (M.M.G. and K.C.T.) independently screened the titles and abstracts for eligibility. We excluded those articles that did not meet our inclusion criteria. In phase 2, we accessed the full text of the selected studies independently by the same 2 reviewers. We resolved disagreements in any phase by discussion among reviewers and, when necessary, arbitration by a third author (J.E.C.) (supplemental Table 4).

Data collection process and data items

One author (M.M.G.) extracted the data from the studies that fulfilled the inclusion criteria through the NVivo software. The second author (K.C.T.) cross-checked the data and verified their accuracy, and other authors (J.E.C., E.A. B., and G. A.) were involved when necessary, according to their area of expertise.

Risk of bias by individual studies

We independently assessed the risk of bias for the included studies by 2 reviewers (M.M.G. and K.C.T.) using the Joanna Briggs Institute checklist for analytical cross-sectional studies.14 We categorized the studies according to the scores of items “yes” as high risk (<49%), moderate risk (50%-69%), and low risk (>70%).

Effect measures

Our outcomes were any cancer-treatment survival measures, such as early mortality, disease-free survival, cancer-specific survival, and overall survival (OS).

Synthesis of results

We conducted a qualitative synthesis of the data by categorizing studies across the 5 domains of SDH.6 We next grouped the results into the following categories: (1) significant association between SDH and any cancer-treatment survival in multivariable-adjusted analysis, (2) significant association in multivariable-adjusted analysis only in a subgroup of the study population (such as according to age or cancer subtype), (3) significant association only in the unadjusted analysis, and (4) not significant association. We considered significant any association that was found to be statistically significant, between SDHs and survival by the authors of each manuscript. In case the statistical significance criterion was not described in the manuscript, we set the statistical significance as confidence intervals of hazard ratio not including 1. Because of the variability of methods, we did not attempt a uniform definition. For cases in which multiple SDHs, types of cancer, databases, and/or outcomes were studied, we extracted results for each variable separately and reported them individually. We presented the outcomes as hazard ratio with stated confidence interval as measures of significance.

Study selection

We identified 38 654 records from the electronic databases we searched. These were reduced to 15 319 after removing duplicate studies. After screening of the title and abstract, 1477 records meet our inclusion criteria. We excluded 719 reports that presented data from countries other than the United States. We then selected the 28 studies that assessed hematologic malignancies in adult patients. In addition, we retrieved 13 additional studies from the reference list. After reading the full text, we retained 41 studies for data extraction (supplemental Figure 1).

Characteristics of included studies

Twenty-five of the 41 studies we included in our analysis evaluated national cohorts (61%), whereas 8 comprised state data studies (19.5%) and 6 single-center cohorts (14.6%). Two studies (4.9%) analyzed 2 different cohorts, national and institutional data. Studies used multiple data sources, including 17 studies evaluating the nationwide US databases, such as the National Cancer Database (NCDB; 41.5%), 7 used the Surveillance, Epidemiology, and End Results (SEER; 17.1%), and 1 used the Center for International Blood and Marrow Transplant Research (CIBMTR; 2.4%). State studies assessed cancer registries: 4 studies from California (11.1%), 1 from Florida (2.2%), 1 from New York (2.2%), 1 from North Carolina (2.2%), and 1 from Virginia (2.2%). Institutional studies retrieved data from the patients’ medical records.

Owing to the diversity of study settings, the sample size ranges widely, from 95 to 132 402 patients (mean, 24 353; SD, 32 330), with a median value of 7073 (IQR, 3461-42 718). Overall, studies addressed the impact of SDHs on cancer outcomes for a variety of hematologic malignancies as follows: lymphoma in 16 studies (34.1%), myeloma in 11 (31.7%), and leukemia in 14 (34.2%). Five studies analyzed a mixed group of patients, in which 4 studies evaluated 2 cancer types and 1 evaluated 4 types of hematologic malignancies.

Most of the studies in our analysis (n = 30, 73.2%) examined more than 1 SDH, ranging from 1 to 8. Thus, this systematic review covered the analysis from 132 variables (median, 2; IQR, 2-4) of SDHs assessed in the 41 included manuscripts. The most common SDH domains explored were health care access and quality (n = 70, 53%) and economic stability (n = 33, 25%); others investigated were education (n = 19, 14.4%) and social and community context (n = 10, 7.6%). Although most of the studies evaluated a unique outcome (n = 28, 68.3%), 12 studies explored 2 outcomes (29.3%), and 1 assessed 3 outcomes (2.4%). The primary outcome was OS in 41 studies (73.2%). Other outcomes measured less frequently were early mortality in 6 studies (10.7%), cancer-specific survival in 5 (8.9%), progression-free survival in 2 (3.6%), disease-free survival in 1 (1.8%), and transplant-related mortality in 1 (1.8%) (supplemental Tables 5 and 6).

Risk of bias

The risk of bias from individual studies had low risk except for 1 study that was considered a moderate risk (supplemental Figure 2).

Synthesis of results

We presented the synthesis of our results in Tables 1-4 and Figure 1.

Table 1.

Summary of descriptive characteristics of included studies evaluating health insurance coverage (n = 33)

Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Yung et al,19 (2011) NYSCR
2001–2008 
NY HL
4752 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.98 (1.47-2.68)  
Medicaid 
 CCR
2000–2007 
CA HL
4752 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.89 (1.43-2.49)  
Medicaid 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.58 (0.49-0.69),
1.60 (1.34-1.91) 
1.05 (0.70-1.60) 
Uninsured and Medicaid 
Abodunrin (2022) SEER
2007-2015 
National NHL
44 609 
OS/multi Private
Public
Uninsured 
Ref.
2.11 (2.00-2.24),
1.76 (1.62-1.91)  
Public insurance and uninsured 
Goldstein et al,21 (2019) NCDB
2004-2014 
National NHL <65 y.o.
22 133 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.96 (1.69-2.28),
1.83 (1.57-2.12),
1.96 (1.71-2.24)  
Uninsured, Medicaid, and Medicare 
   NHL. ≥65 y.o.
21 515 
OS/multi Private
Medicare 
Ref.
1.28 (1.17-1.4)  
Medicare 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/multi Private
Medicaid
Medicare
Uninsured 
Ref.
1.60 (1.34-1.92),
1.75 (1.57-1.94),
1.77 (1.43-2.19)  
Medicaid, Medicare, and uninsured 
    OS/multi Private
Medicaid
Medicare
Uninsured
Other government 
Ref.
1.61 (1.53-1.70),
1.66 (1.61-1.71),
1.44 (1.35-1.54),
1.29 (1.17-1.43)  
Medicaid, Medicare, uninsured, and other government insurance 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Private
Medicare
Medicaid
Government
Uninsured 
Ref.
1.66 (1.64-1.69),
1.74 (1.70-1.79),
1.16 (1.07-1.25),
1.37 (1.34-1.40)  
Medicare, Medicaid, government insurance, and uninsured 
Han (2014) NCDB
2004-2010 
National NHL
3858 
OS/multi Private
Uninsured
Medicaid 
Ref.
1.18 (0.96-1.44)
1.27 (1.06-1.53)  
Medicaid 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.44 (1.22-1.70),
1.33 (1.15-1.54) 
1.05 (0.98-1.13) 
Uninsured and Medicaid 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL CSS/multi Private/military
Public/uninsured 
Ref.
1.46 (1.30-1.63)  
Public insurance or uninsured 
   16 133
 
OS/multi Private/military
Public/uninsured 
Ref.
1.54 (1.40-1.69)  
Public insurance or uninsured 
Ermann (2020) NCDB
2004-2015 
National NHL
27 690 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.71 (NS),
1.25 (NS),
1.76 (NS)  
Uninsured, Medicaid, and Medicare 
Goldstein et al,21 (2019) NCDB
2004–2014 
National NHL
aged <65 y.o.
314 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
0.85 (0.4-1.6)
0.78 (0.5-1.2)
1.2 (0.7-2.1) 
   NH
aged ≥65
161 
OS/multi Private
Medicaid
Medicare 
Ref.
1.49 (0.1-14.8)
1.34 (0.4-4.3) 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM <65 y.o.
1249 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
3.11 (1.77-5.45),
1.88 (1.01-3.48),
2.78 (1.76-4.38)  
Uninsured, Medicare, and Medicaid 
   WM ≥65 y.o.
2629 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
0.25 (0.03-1.77)
1.05 (0.45-2.43)
1.08 (0.85-1.36) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Private
Government
Uninsured 
Ref.
1.31 (1.07-1.59) 
1.50 (0.91-1.47) 
Government insurance 
Goldstein et al,21 (2019) NCDB
2004–2014 
National BL < 65 y.o.
5235 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.44 (1.2-1.7),
1.22 (1.0-1.4),
1.53 (1.2-1.9)  
Uninsured, Medicaid, and Medicare 
   BL ≥ 65 y.o.
1838 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
8.38 (2.5-28.3) 
0.75 (0.3-1.8)
1.29 (0.9-1.8) 
Uninsured 
Fiala et al,15 (2015) Institutional
2000-2009 
MO MM
652 
OS/Uni Private
Medicare
Medicaid
Uninsured 
67.5 mo
37.5 mo,
38.3 mo,
59.0 mo  
Uninsured, Medicaid, and Medicare 
    OS/multi Private
Medicare
Medicaid
Uninsured 
Ref.
0.74 (0.53-1.03)
1.22 (0.70-2.14)
1.59 (0.94-2.70) 
Makhani (2021) SEER
2007-2016 
National MM.
41 789 
CSS/multi Private
Uninsured
Medicaid
Insurance 
Ref.
1.26 (1.11-1.42),
1.44 (1.36-1.53) 
1.11 (1.06-1.17) 
Medicaid and uninsured 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.83 (0.78-0.89),
1.09 (1.01-1.17),
0.90 (0.84-0.96)  
Uninsured and Medicaid 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality Private
Medicare
Public
Uninsured 
Ref.
1.07 (0.88-1.30)
1.08 (0.81-1.44)
2.44 (1.45-4.13)  
Uninsured 
Chamoun (2021) NCDB
2005-2014 
National MM
117 926 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.62 (1.32-1.99),
1.59 (1.36-1.87) 
1.09 (0.99-1.2) 
Medicaid and uninsured 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/multi Insured
Uninsured
Medicaid 
Ref.
1.13 (1.00-1.28),
1.25 (1.16-1.36)  
Uninsured and Medicaid 
    OS/multi Insured
Uninsured
Medicaid 
Ref.
1.33 (1.20-1.48),
1.67 (1.56-1.78)  
Uninsured and Medicaid 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
12-month mortality Insured
Medicaid
Uninsured 
Ref.
2.04 (1.74-2.41),
1.72 (1.34-2.19)  
Medicaid and uninsured 
    OS/multi Insured
Medicaid
Uninsured 
Ref.
1.76 (1.59-1.94),
1.43 (1.23-1.67)  
Medicaid and uninsured 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/Multi. Uninsured
Private
Government 
Ref.
0.70 (0.48-0.98) 
1.02 (0.72-1.46) 
Uninsured 
   PM
P-EM
1468 
OS/multi Uninsured
Private
Government 
Ref.
0.44 (0.26-0.73) 
0.64 (0.37-1.09) 
Uninsured 
Yung et al,19 (2011) NYSCR
2001–2008 
NY AML
3506 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.00 (0.84-1.19) 
 CCR
2000–2007 
CA AML
3506 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.02 (0.89-1.16) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi Private
Uninsured
Medicaid
Medicare 
Ref.
2.02 (1.80-2.27),
1.05 (0.94-1.16)
1.29 (1.21-1.37)  
Uninsured and Medicare 
    OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.18 (1.12-1.25),
1.11 (1.07-1.16),
1.20 (1.17-1.23)  
Uninsured, Medicaid, and Medicare 
Byrne et al,35 (2011) FCDS
1998-2002 
FL AML
4659 
OS/multi Private
Uninsured
Medicare
Medicaid 
Ref.
1.08 (0.83-1.39)
0.97 (0.87-1.07)
1.25 (1.06-1.47)  
Medicaid 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi Private
Medicare
Medicaid 
Ref.
0.91 (0.71-1.17)
1.36 (1.00-1.85)  
Medicaid 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
2-month mortality Insured
Medicaid
Uninsured 
Ref. OR.
1.34 (1.07-1.66),
1.99 (1.46-2.73)  
Medicaid and uninsured 
    OS/multi Insured
Medicaid
Uninsured 
Ref.
1.24 (1.13-1.37),
1.24 (1.07-1.44)  
Medicaid and uninsured 
Bradley et al,34 (2011) VCR/VHI
1999-2006 
VA AML
No CTX
NS 
OS/multi Private/military
Uninsured
Public 
Ref.
1.29 (1.02-1.84),
1.39 (1.02-1.88)  
Uninsured and public 
   AML
CTX
NS 
OS/multi Private/military
Uninsured
Public 
Ref.
1.07 (0.74-1.54)
1.31 (0.96-1.77) 
Master et al,28 (2016) NCDB
1998-2012 
National AML
67 443 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.20 (1.14-1.27),
1.16 (1.12-1.22),
1.19 (1.15-1.23)  
Medicaid, uninsured and Medicare 
Al-Ameri et al,20 (2014) Institutional
2002-2010 
OH AML/MDS
94 
OS/multi Private
Medicaid/Medicare
Medicare with suppl. 
Ref.
0.61 (0.33-1.13)
0.93 (0.49-1.76) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL < 65 y.o.
5380 
1-month mortality Private
Medicaid
Medicare
Uninsured 
Ref.
1.14 (0.86-1.51)
1.78 (1.29-2.47),
2.31 (1.61-3.32)  
Medicare and uninsured 
   APL ≥ 65 y.o.
1520 
1-month mortality Private
Nonprivate 
Ref.
1.16 (0.76-1.77) 
   APL < 65 y.o.
5380 
OS/multi Private
Medicaid
Medicare
Uninsured 
Ref.
1.40 (1.19-1.64),
1.89 (1.58-2.26) 
1.24 (0.96-1.60) 
Medicaid and Medicare 
   APL ≥ 65 y.o.
1520 
OS/multi Private
Nonprivate 
Ref.
0.86-1.33 
Krakora (2020) Institutional
2007-2017 
NJ ALL
136 
PFS/multi Insured
Uninsured 
Ref.
3.31 (1.53-7.17)  
Uninsured 
    OS/multi Insured
Uninsured 
Ref.
1.84 (0.92-3.66) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Insured
Medicaid
Uninsured 
Ref.
1.83 (1.39-2.40),
1.93 (1.40-2.66)  
Uninsured or Medicaid 
   CML ≥ 65 y.o.
2142 
OS/multi Insured
Medicaid 
Ref.
1.26 (1.00-1.64) 
Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Yung et al,19 (2011) NYSCR
2001–2008 
NY HL
4752 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.98 (1.47-2.68)  
Medicaid 
 CCR
2000–2007 
CA HL
4752 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.89 (1.43-2.49)  
Medicaid 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.58 (0.49-0.69),
1.60 (1.34-1.91) 
1.05 (0.70-1.60) 
Uninsured and Medicaid 
Abodunrin (2022) SEER
2007-2015 
National NHL
44 609 
OS/multi Private
Public
Uninsured 
Ref.
2.11 (2.00-2.24),
1.76 (1.62-1.91)  
Public insurance and uninsured 
Goldstein et al,21 (2019) NCDB
2004-2014 
National NHL <65 y.o.
22 133 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.96 (1.69-2.28),
1.83 (1.57-2.12),
1.96 (1.71-2.24)  
Uninsured, Medicaid, and Medicare 
   NHL. ≥65 y.o.
21 515 
OS/multi Private
Medicare 
Ref.
1.28 (1.17-1.4)  
Medicare 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/multi Private
Medicaid
Medicare
Uninsured 
Ref.
1.60 (1.34-1.92),
1.75 (1.57-1.94),
1.77 (1.43-2.19)  
Medicaid, Medicare, and uninsured 
    OS/multi Private
Medicaid
Medicare
Uninsured
Other government 
Ref.
1.61 (1.53-1.70),
1.66 (1.61-1.71),
1.44 (1.35-1.54),
1.29 (1.17-1.43)  
Medicaid, Medicare, uninsured, and other government insurance 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Private
Medicare
Medicaid
Government
Uninsured 
Ref.
1.66 (1.64-1.69),
1.74 (1.70-1.79),
1.16 (1.07-1.25),
1.37 (1.34-1.40)  
Medicare, Medicaid, government insurance, and uninsured 
Han (2014) NCDB
2004-2010 
National NHL
3858 
OS/multi Private
Uninsured
Medicaid 
Ref.
1.18 (0.96-1.44)
1.27 (1.06-1.53)  
Medicaid 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.44 (1.22-1.70),
1.33 (1.15-1.54) 
1.05 (0.98-1.13) 
Uninsured and Medicaid 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL CSS/multi Private/military
Public/uninsured 
Ref.
1.46 (1.30-1.63)  
Public insurance or uninsured 
   16 133
 
OS/multi Private/military
Public/uninsured 
Ref.
1.54 (1.40-1.69)  
Public insurance or uninsured 
Ermann (2020) NCDB
2004-2015 
National NHL
27 690 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.71 (NS),
1.25 (NS),
1.76 (NS)  
Uninsured, Medicaid, and Medicare 
Goldstein et al,21 (2019) NCDB
2004–2014 
National NHL
aged <65 y.o.
314 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
0.85 (0.4-1.6)
0.78 (0.5-1.2)
1.2 (0.7-2.1) 
   NH
aged ≥65
161 
OS/multi Private
Medicaid
Medicare 
Ref.
1.49 (0.1-14.8)
1.34 (0.4-4.3) 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM <65 y.o.
1249 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
3.11 (1.77-5.45),
1.88 (1.01-3.48),
2.78 (1.76-4.38)  
Uninsured, Medicare, and Medicaid 
   WM ≥65 y.o.
2629 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
0.25 (0.03-1.77)
1.05 (0.45-2.43)
1.08 (0.85-1.36) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Private
Government
Uninsured 
Ref.
1.31 (1.07-1.59) 
1.50 (0.91-1.47) 
Government insurance 
Goldstein et al,21 (2019) NCDB
2004–2014 
National BL < 65 y.o.
5235 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.44 (1.2-1.7),
1.22 (1.0-1.4),
1.53 (1.2-1.9)  
Uninsured, Medicaid, and Medicare 
   BL ≥ 65 y.o.
1838 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
8.38 (2.5-28.3) 
0.75 (0.3-1.8)
1.29 (0.9-1.8) 
Uninsured 
Fiala et al,15 (2015) Institutional
2000-2009 
MO MM
652 
OS/Uni Private
Medicare
Medicaid
Uninsured 
67.5 mo
37.5 mo,
38.3 mo,
59.0 mo  
Uninsured, Medicaid, and Medicare 
    OS/multi Private
Medicare
Medicaid
Uninsured 
Ref.
0.74 (0.53-1.03)
1.22 (0.70-2.14)
1.59 (0.94-2.70) 
Makhani (2021) SEER
2007-2016 
National MM.
41 789 
CSS/multi Private
Uninsured
Medicaid
Insurance 
Ref.
1.26 (1.11-1.42),
1.44 (1.36-1.53) 
1.11 (1.06-1.17) 
Medicaid and uninsured 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi Uninsured
Private
Medicaid
Medicare 
Ref.
0.83 (0.78-0.89),
1.09 (1.01-1.17),
0.90 (0.84-0.96)  
Uninsured and Medicaid 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality Private
Medicare
Public
Uninsured 
Ref.
1.07 (0.88-1.30)
1.08 (0.81-1.44)
2.44 (1.45-4.13)  
Uninsured 
Chamoun (2021) NCDB
2005-2014 
National MM
117 926 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.62 (1.32-1.99),
1.59 (1.36-1.87) 
1.09 (0.99-1.2) 
Medicaid and uninsured 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/multi Insured
Uninsured
Medicaid 
Ref.
1.13 (1.00-1.28),
1.25 (1.16-1.36)  
Uninsured and Medicaid 
    OS/multi Insured
Uninsured
Medicaid 
Ref.
1.33 (1.20-1.48),
1.67 (1.56-1.78)  
Uninsured and Medicaid 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
12-month mortality Insured
Medicaid
Uninsured 
Ref.
2.04 (1.74-2.41),
1.72 (1.34-2.19)  
Medicaid and uninsured 
    OS/multi Insured
Medicaid
Uninsured 
Ref.
1.76 (1.59-1.94),
1.43 (1.23-1.67)  
Medicaid and uninsured 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/Multi. Uninsured
Private
Government 
Ref.
0.70 (0.48-0.98) 
1.02 (0.72-1.46) 
Uninsured 
   PM
P-EM
1468 
OS/multi Uninsured
Private
Government 
Ref.
0.44 (0.26-0.73) 
0.64 (0.37-1.09) 
Uninsured 
Yung et al,19 (2011) NYSCR
2001–2008 
NY AML
3506 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.00 (0.84-1.19) 
 CCR
2000–2007 
CA AML
3506 
OS/multi Non-Medicaid
Medicaid 
Ref.
1.02 (0.89-1.16) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi Private
Uninsured
Medicaid
Medicare 
Ref.
2.02 (1.80-2.27),
1.05 (0.94-1.16)
1.29 (1.21-1.37)  
Uninsured and Medicare 
    OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.18 (1.12-1.25),
1.11 (1.07-1.16),
1.20 (1.17-1.23)  
Uninsured, Medicaid, and Medicare 
Byrne et al,35 (2011) FCDS
1998-2002 
FL AML
4659 
OS/multi Private
Uninsured
Medicare
Medicaid 
Ref.
1.08 (0.83-1.39)
0.97 (0.87-1.07)
1.25 (1.06-1.47)  
Medicaid 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi Private
Medicare
Medicaid 
Ref.
0.91 (0.71-1.17)
1.36 (1.00-1.85)  
Medicaid 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
2-month mortality Insured
Medicaid
Uninsured 
Ref. OR.
1.34 (1.07-1.66),
1.99 (1.46-2.73)  
Medicaid and uninsured 
    OS/multi Insured
Medicaid
Uninsured 
Ref.
1.24 (1.13-1.37),
1.24 (1.07-1.44)  
Medicaid and uninsured 
Bradley et al,34 (2011) VCR/VHI
1999-2006 
VA AML
No CTX
NS 
OS/multi Private/military
Uninsured
Public 
Ref.
1.29 (1.02-1.84),
1.39 (1.02-1.88)  
Uninsured and public 
   AML
CTX
NS 
OS/multi Private/military
Uninsured
Public 
Ref.
1.07 (0.74-1.54)
1.31 (0.96-1.77) 
Master et al,28 (2016) NCDB
1998-2012 
National AML
67 443 
OS/multi Private
Uninsured
Medicaid
Medicare 
Ref.
1.20 (1.14-1.27),
1.16 (1.12-1.22),
1.19 (1.15-1.23)  
Medicaid, uninsured and Medicare 
Al-Ameri et al,20 (2014) Institutional
2002-2010 
OH AML/MDS
94 
OS/multi Private
Medicaid/Medicare
Medicare with suppl. 
Ref.
0.61 (0.33-1.13)
0.93 (0.49-1.76) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL < 65 y.o.
5380 
1-month mortality Private
Medicaid
Medicare
Uninsured 
Ref.
1.14 (0.86-1.51)
1.78 (1.29-2.47),
2.31 (1.61-3.32)  
Medicare and uninsured 
   APL ≥ 65 y.o.
1520 
1-month mortality Private
Nonprivate 
Ref.
1.16 (0.76-1.77) 
   APL < 65 y.o.
5380 
OS/multi Private
Medicaid
Medicare
Uninsured 
Ref.
1.40 (1.19-1.64),
1.89 (1.58-2.26) 
1.24 (0.96-1.60) 
Medicaid and Medicare 
   APL ≥ 65 y.o.
1520 
OS/multi Private
Nonprivate 
Ref.
0.86-1.33 
Krakora (2020) Institutional
2007-2017 
NJ ALL
136 
PFS/multi Insured
Uninsured 
Ref.
3.31 (1.53-7.17)  
Uninsured 
    OS/multi Insured
Uninsured 
Ref.
1.84 (0.92-3.66) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Insured
Medicaid
Uninsured 
Ref.
1.83 (1.39-2.40),
1.93 (1.40-2.66)  
Uninsured or Medicaid 
   CML ≥ 65 y.o.
2142 
OS/multi Insured
Medicaid 
Ref.
1.26 (1.00-1.64) 

AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; BL, Burkitt lymphoma; CA, California; CCR, California Cancer Registry; CI, confidential interval; CML, chronic myeloid leukemia; CSS, cancer-specific survival; CTX, chemotherapy; FCDS, Florida Cancer Data System; FL, Florida; HL, Hodgkin lymphoma; HR, hazard ratio; MDS, myelodysplastic syndromes; MM, multiple myeloma; multi, multivariate analysis; N, sample size; NC, North Carolina; NC CCR, North Carolina Central Cancer Registry; NCDB, National Cancer Database; NHL, non-Hodgkin lymphoma; NJ, New Jersey; NOS, not otherwise specified; NS, not specified; NY, New York; NYSCR, New York State Cancer Registry; OH, Ohio; OR, odds ratio; P-bone, plasmacytoma originating in bone; P-EM, plasmacytoma originating in extramedullary; PFS, progression-free survival; PM, plasmacytoma; Ref., reference; SEER, Surveillance, Epidemiology, and End Results Registry; suppl., supplement; VA, Virginia; VCR, Virginia Cancer Registry; VHI, Virginia Health Information; WM, Waldenstrom macroglobulinemia; y.o., years old.

Reported as statistical significance by the author.

Statistical significance but not discussed by the author.

Table 2.

Summary of descriptive characteristics of included studies evaluating facility type and characteristics (n = 20), distance traveled to treatment center (n = 11), treatment facility volume (n = 4), and provider expertise (n = 2)

Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Treatment facility type and characteristics 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi Community
Comprehensive
Academic 
Ref.
0.89 (0.79-1.01)
0.78 (0.69-0.89)  
Treatment at community center 
Patel et al,5 (2022) Institutional 2007-2020 TX NHL.
95 
PFS at 2 years Public Hospital
NCI-CCC 
56%
43% 
    OS at 2 years Public Hospital
NCI-CCC 
77%
55% 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/uni Academic
Nonacademic 
Data not showed Treatment at nonacademic center 
    1-month mortality/multi Academic
Nonacademic 
Ref.
0.95 (0.86-1.06) 
    OS/uni Academic
Nonacademic 
95 vs 78 months  Treatment at nonacademic center 
    OS/multi Academic
Non-academic 
Ref.
1.01 (0.97-1.05) 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Academic
Comprehensive
Community 
Ref.
1.05 (1.04-1.07),
1.03 (1.01-1.06)  
Treatment at comprehensive or community center 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi Academic
Community
Comprehensive 
Ref.
1.12 (1.03-1.22),
1.06 (1.00-1.12)  
Treatment at community or comprehensive center 
Ermann (2020) NCDB
2004-2015 
National NHL
27 690 
OS/multi Nonacademic Academic Ref.
0.86 (NS) 
Treatment at nonacademic center 
Tao et al,25 (2014) CCR.
2001-2009 
CA NHL
16 133 
CSS/multi Non-NCI
NCI-designated 
Ref.
0.90 (0.84-0.96) 
Treatment at non–NCI-designated center 
    OS/multi No NCI-designated
NCI-designated 
Ref.
0.96 (0.90-1.01) 
Treatment at non–NCI-designated center 
Ai et al,26 (2012) CCR.
2001-2008 
CA NHL
213 
OS/multi Dx at no NCI-CCC
Dx at NCI-CCC 
Ref.
0.96 (0.59-1.54) 
Chohan et al,18 (2022) NCDB
2005-2014 
National WM. <65 y.o.
1249 
OS/uni Community
Academic 
Ref.
0.81 (0.60-1.11) 
    OS/multi Community
Academic 
Ref.
0.90 (0.62-1.30) 
   WM ≥65 y.o.
2629 
OS/uni Community
Academic 
Ref.
0.86 (0.75-0.97) 
Treatment at community center 
    OS/multi Community
Academic 
Ref.
0.93 (0.80-1.08) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Academic
Non-academic 
Ref.
0.90 (0.77-1.05) 
Jayakrishnan (2021) NCDB.
2004-2016 
National MM
50 543 
OS/multi Community
Comprehensive
Academic 
Ref.
1.1 (0.97-1.05)
0.95 (0.91-0.98)  
Treatment at community center 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
1-month mortality/multi Private
Kaiser
NCI-designated
Teaching 
Ref.
0.91 (0.73-1.14)
0.45 (0.36-0.56) 
0.92 (0.72-1.19) 
Treatment at private hospital 
Chamoun (2021) NCDB
2005-2014 
National MM
117 926 
OS/multi Academic
Other types 
Ref.
1.49 (1.39-1.59)  
Treatment at community, comprehensive, or integrated center 
Ailawadhi (2016)  SEER-18 2003-2011 National MM
34 529 
OS/multi 0 NCI centers
1 NCI centers
≥ 2 NCI centers 
Ref.
0.99 (0.95-1.04)
0.88 (0.84-0.93)  
No access to NCI centers 
     0 NCCN centers
1 NCCN centers 
Ref.
0.91 (0.86-0.95)  
No access to NCCN centers 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/multi Community
Comprehensive
Academic/research
Integrated 
Ref.
0.95 (0.79-1.15)
0.76 (0.62-0.92) 
0.97 (0.77-1.22) 
Treatment at community center 
   PM
P-EM
1468 
OS/multi Community
Comprehensive
Academic/research
Integrated 
Ref.
1.38 (0.98-1.93)
1.29 (0.90-1.84)
1.48 (0.98-2.24) 
Master et al,28 
(2016) 
NCDB
1998 -2012 
National AML
67 443 
OS/multi Same facility
Different facility 
Ref.
0.87 (0.85-0.89)  
Diagnosed and treated at the same facility 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month/multi Academic
Nonacademic 
Ref.
1.52 (1.46-1.59)  
Treatment at nonacademic center 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
553 
OS/multi Tx at NCI-CCC
Non-NCI-CCC 
Ref.
1.25 (0.95-1.65) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi Academic
Community Comprehensive 
Ref.
1.02 (0.98-1.07)
1.04 (1.02-1.06)  
Treatment at comprehensive center 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality Academic
Community Comprehensive 
Ref.
0.92 (0.37-2.28)
1.18 (0.89-1.57) 
   APL ≥65 y.o.
1520 
1-month mortality Academic
Community Comprehensive 
Ref.
1.16 (0.53-2.50)
1.53 (1.14-2.04)  
Treatment at comprehensive center 
   APL <65 y.o.
5380 
OS/multi Academic
Community Comprehensive 
Ref.
0.77 (0.47-1.28)
1.06 (0.90-1.24) 
   APL ≥65 y.o.
1520 
OS/multi Academic
Community Comprehensive 
Ref.
1.17 (0.80-1.69)
1.19 (1.03-1.39)  
Treatment at comprehensive center 
Distance traveled (in miles) to treatment center 
Loberiza et al,30 (2009) UNMC
1982-2006 
NE HL
2330 
OS/multi NS NS 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
OS/multi < 5.7
5.7-13.3
13.4-36.9 
Ref.
0.96 (0.93-0.98)
0.92 (0.89-0.94)  
Distance traveled 13.4 to 36.9 miles 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM. < 65 y.o.
1249 
OS/multi < 10
10-25
≥ 25 
Ref.
0.92 (0.59-1.45)
1.49 (0.92-2.41) 
   WM ≥ 65 y.o.
2629 
OS/uni < 10
10-25
≥ 25 
Ref.
0.89 (0.76-1.04)
0.82 (0.69-0.98)  
Distance traveled less than 10 miles 
    OS/multi < 10
10-25
≥ 25 
Ref.
0.97 (0.81-1.15)
0.95 (0.76-1.19) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi >20
9–20
4–9 
Ref.
1.1 (0.83-1.26)
1.10 (0.89-1.36) 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/uni ≤ 12.5
12.6-50
> 50 
Ref.
0.85 (0.76-0.95) 
0.87 (0.73-1.04) 
Distance less than 12.5 miles 
   PM
P-bone
4056 
OS/multi ≤ 12.5
12.6-50
> 50 
Ref.
0.91 (0.81-1.03)
0.97 (0.79-1.18) 
   PM
P-EM
1468 
OS/multi ≤ 12.5
12.6-50
> 50 
Ref.
0.98 (0.79-1.21)
0.93 (0.66-1.32) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi ≥ 34.8
0-4.9
5-11.9 
Ref.
1.26 (1.18-1.34),
1.12 (1.05-1.19)  
Distance traveled 0 to 11.9 miles 
Rodriguez et al,33 (2008) Institutional
1997-2005 
OH AML
281 
OS/multi Per 20 increase 1.00 (0.98-1.02) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi ≥ 30
< 30 
Ref.
0.99 (0.97-1.02) 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML.
553 
OS/multi < 20
> 20 
Ref.
1.14 (0.85-1.52) 
Borate et al,38 (2015) SEER
2007-2011 
National AML.
5541 
OS/uni < 20
20-70
70-200 
Ref.
0.98 (0.91-1.04)
0.95 (0.89-1.02) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality < 6
6-13.9
14-39.9 
Ref.
0.71 (0.53-0.96) 
0.87 (0.65-1.16) 
Distance traveled to less than 6 miles 
   APL ≥65 y.o.
1520 
1-month mortality/multi < 6
6-13.9
14-39.9 
Ref.
1.1 (0.70-1.47)
1.01 (0.70-1.46) 
   APL <65 y.o.
5380 
OS/HR < 6
6-13.9
14-39.9 
Ref.
0.95 (0.80-1.12)
0.97 (0.82-1.15) 
   APL ≥65 y.o.
1520 
OS/multi < 6
6-13.9
14-39.9
≥ 40 
Ref.
0.92 (0.76-1.12)
1.05 (0.88-1.27)
0.94 (0.77-1.15) 
Treatment facility volume (patients treated per year) 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/multi Quartile1: lowest
Quartile 2.
Quartile 3.
Quartile 4: highestc 
Ref.
1.1 (0.91-1.13)
0.92 (0.82-1.03)
0.89 (0.78-1.01) 
    OS/multi Quartile1: lowest
Quartile 2.
Quartile 3.
Quartile 4: highestc 
Ref.
0.98 (0.95-1.01),
0.96 (0.92-0.99)
0.89 (0.84-0.94)  
Treatment at facility with lowest volume 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Quartile 4: ≥33
Quartile 3: 21-32
Quartile 2:14-20
Quartile 1: 2-13 
Ref.
1.05 (1.04-1.06),
1.08 (1.07-1.10),
1.14 (1.11-1.17)  
Treatment at facility with lowest volume 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Quartile 4c: highest
Quartile 1: lowest
Quartile 2
Quartile 3 
Ref.
1.5 (1.18-1.88),
1.4 (1.17-1.72) 
1.13 (0.92-1.38) 
Treatment at facility with lowest volume 
Go (2017) NCDB
2003-2011 
National MM
94 722 
OS/multi Quartile 4: >10.3
Quartile 3: 6.2-10.3
Quartile 2: 3.6-6.1
Quartile 1: <3.6 
Ref.b
1.16 (1.1-1.22),
1.23 (1.16-1.3),
1.26 (1.17-1.3)  
Treatment at facility with lowest volume 
Provider expertise 
Loberiza et al,30 (2009) UNMC Database
1982-2006 
NE HL
2330 
OS/multi University-based
Community-based 
Ref.
1.26 (1.06-1.49)  
Rural patients treated by community-based providers 
Shanafelt et al,29 (2012) The Mayo Clinic CLL Database
1999-2009 
MN CLL/SLL.
1309 
OS/multi CLL hematologist
Hematologist 
Ref.
1.49 (1.08-2.04)  
Treatment by a non-CLL hematologist 
     Hematologist
Fellow 
Ref.
0.98 (0.64-1.50) 
Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Treatment facility type and characteristics 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi Community
Comprehensive
Academic 
Ref.
0.89 (0.79-1.01)
0.78 (0.69-0.89)  
Treatment at community center 
Patel et al,5 (2022) Institutional 2007-2020 TX NHL.
95 
PFS at 2 years Public Hospital
NCI-CCC 
56%
43% 
    OS at 2 years Public Hospital
NCI-CCC 
77%
55% 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/uni Academic
Nonacademic 
Data not showed Treatment at nonacademic center 
    1-month mortality/multi Academic
Nonacademic 
Ref.
0.95 (0.86-1.06) 
    OS/uni Academic
Nonacademic 
95 vs 78 months  Treatment at nonacademic center 
    OS/multi Academic
Non-academic 
Ref.
1.01 (0.97-1.05) 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Academic
Comprehensive
Community 
Ref.
1.05 (1.04-1.07),
1.03 (1.01-1.06)  
Treatment at comprehensive or community center 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi Academic
Community
Comprehensive 
Ref.
1.12 (1.03-1.22),
1.06 (1.00-1.12)  
Treatment at community or comprehensive center 
Ermann (2020) NCDB
2004-2015 
National NHL
27 690 
OS/multi Nonacademic Academic Ref.
0.86 (NS) 
Treatment at nonacademic center 
Tao et al,25 (2014) CCR.
2001-2009 
CA NHL
16 133 
CSS/multi Non-NCI
NCI-designated 
Ref.
0.90 (0.84-0.96) 
Treatment at non–NCI-designated center 
    OS/multi No NCI-designated
NCI-designated 
Ref.
0.96 (0.90-1.01) 
Treatment at non–NCI-designated center 
Ai et al,26 (2012) CCR.
2001-2008 
CA NHL
213 
OS/multi Dx at no NCI-CCC
Dx at NCI-CCC 
Ref.
0.96 (0.59-1.54) 
Chohan et al,18 (2022) NCDB
2005-2014 
National WM. <65 y.o.
1249 
OS/uni Community
Academic 
Ref.
0.81 (0.60-1.11) 
    OS/multi Community
Academic 
Ref.
0.90 (0.62-1.30) 
   WM ≥65 y.o.
2629 
OS/uni Community
Academic 
Ref.
0.86 (0.75-0.97) 
Treatment at community center 
    OS/multi Community
Academic 
Ref.
0.93 (0.80-1.08) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Academic
Non-academic 
Ref.
0.90 (0.77-1.05) 
Jayakrishnan (2021) NCDB.
2004-2016 
National MM
50 543 
OS/multi Community
Comprehensive
Academic 
Ref.
1.1 (0.97-1.05)
0.95 (0.91-0.98)  
Treatment at community center 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
1-month mortality/multi Private
Kaiser
NCI-designated
Teaching 
Ref.
0.91 (0.73-1.14)
0.45 (0.36-0.56) 
0.92 (0.72-1.19) 
Treatment at private hospital 
Chamoun (2021) NCDB
2005-2014 
National MM
117 926 
OS/multi Academic
Other types 
Ref.
1.49 (1.39-1.59)  
Treatment at community, comprehensive, or integrated center 
Ailawadhi (2016)  SEER-18 2003-2011 National MM
34 529 
OS/multi 0 NCI centers
1 NCI centers
≥ 2 NCI centers 
Ref.
0.99 (0.95-1.04)
0.88 (0.84-0.93)  
No access to NCI centers 
     0 NCCN centers
1 NCCN centers 
Ref.
0.91 (0.86-0.95)  
No access to NCCN centers 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/multi Community
Comprehensive
Academic/research
Integrated 
Ref.
0.95 (0.79-1.15)
0.76 (0.62-0.92) 
0.97 (0.77-1.22) 
Treatment at community center 
   PM
P-EM
1468 
OS/multi Community
Comprehensive
Academic/research
Integrated 
Ref.
1.38 (0.98-1.93)
1.29 (0.90-1.84)
1.48 (0.98-2.24) 
Master et al,28 
(2016) 
NCDB
1998 -2012 
National AML
67 443 
OS/multi Same facility
Different facility 
Ref.
0.87 (0.85-0.89)  
Diagnosed and treated at the same facility 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month/multi Academic
Nonacademic 
Ref.
1.52 (1.46-1.59)  
Treatment at nonacademic center 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
553 
OS/multi Tx at NCI-CCC
Non-NCI-CCC 
Ref.
1.25 (0.95-1.65) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi Academic
Community Comprehensive 
Ref.
1.02 (0.98-1.07)
1.04 (1.02-1.06)  
Treatment at comprehensive center 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality Academic
Community Comprehensive 
Ref.
0.92 (0.37-2.28)
1.18 (0.89-1.57) 
   APL ≥65 y.o.
1520 
1-month mortality Academic
Community Comprehensive 
Ref.
1.16 (0.53-2.50)
1.53 (1.14-2.04)  
Treatment at comprehensive center 
   APL <65 y.o.
5380 
OS/multi Academic
Community Comprehensive 
Ref.
0.77 (0.47-1.28)
1.06 (0.90-1.24) 
   APL ≥65 y.o.
1520 
OS/multi Academic
Community Comprehensive 
Ref.
1.17 (0.80-1.69)
1.19 (1.03-1.39)  
Treatment at comprehensive center 
Distance traveled (in miles) to treatment center 
Loberiza et al,30 (2009) UNMC
1982-2006 
NE HL
2330 
OS/multi NS NS 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
OS/multi < 5.7
5.7-13.3
13.4-36.9 
Ref.
0.96 (0.93-0.98)
0.92 (0.89-0.94)  
Distance traveled 13.4 to 36.9 miles 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM. < 65 y.o.
1249 
OS/multi < 10
10-25
≥ 25 
Ref.
0.92 (0.59-1.45)
1.49 (0.92-2.41) 
   WM ≥ 65 y.o.
2629 
OS/uni < 10
10-25
≥ 25 
Ref.
0.89 (0.76-1.04)
0.82 (0.69-0.98)  
Distance traveled less than 10 miles 
    OS/multi < 10
10-25
≥ 25 
Ref.
0.97 (0.81-1.15)
0.95 (0.76-1.19) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi >20
9–20
4–9 
Ref.
1.1 (0.83-1.26)
1.10 (0.89-1.36) 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/uni ≤ 12.5
12.6-50
> 50 
Ref.
0.85 (0.76-0.95) 
0.87 (0.73-1.04) 
Distance less than 12.5 miles 
   PM
P-bone
4056 
OS/multi ≤ 12.5
12.6-50
> 50 
Ref.
0.91 (0.81-1.03)
0.97 (0.79-1.18) 
   PM
P-EM
1468 
OS/multi ≤ 12.5
12.6-50
> 50 
Ref.
0.98 (0.79-1.21)
0.93 (0.66-1.32) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi ≥ 34.8
0-4.9
5-11.9 
Ref.
1.26 (1.18-1.34),
1.12 (1.05-1.19)  
Distance traveled 0 to 11.9 miles 
Rodriguez et al,33 (2008) Institutional
1997-2005 
OH AML
281 
OS/multi Per 20 increase 1.00 (0.98-1.02) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi ≥ 30
< 30 
Ref.
0.99 (0.97-1.02) 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML.
553 
OS/multi < 20
> 20 
Ref.
1.14 (0.85-1.52) 
Borate et al,38 (2015) SEER
2007-2011 
National AML.
5541 
OS/uni < 20
20-70
70-200 
Ref.
0.98 (0.91-1.04)
0.95 (0.89-1.02) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality < 6
6-13.9
14-39.9 
Ref.
0.71 (0.53-0.96) 
0.87 (0.65-1.16) 
Distance traveled to less than 6 miles 
   APL ≥65 y.o.
1520 
1-month mortality/multi < 6
6-13.9
14-39.9 
Ref.
1.1 (0.70-1.47)
1.01 (0.70-1.46) 
   APL <65 y.o.
5380 
OS/HR < 6
6-13.9
14-39.9 
Ref.
0.95 (0.80-1.12)
0.97 (0.82-1.15) 
   APL ≥65 y.o.
1520 
OS/multi < 6
6-13.9
14-39.9
≥ 40 
Ref.
0.92 (0.76-1.12)
1.05 (0.88-1.27)
0.94 (0.77-1.15) 
Treatment facility volume (patients treated per year) 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/multi Quartile1: lowest
Quartile 2.
Quartile 3.
Quartile 4: highestc 
Ref.
1.1 (0.91-1.13)
0.92 (0.82-1.03)
0.89 (0.78-1.01) 
    OS/multi Quartile1: lowest
Quartile 2.
Quartile 3.
Quartile 4: highestc 
Ref.
0.98 (0.95-1.01),
0.96 (0.92-0.99)
0.89 (0.84-0.94)  
Treatment at facility with lowest volume 
Go (2016) NCDB
1998-2011 
National NHL
278 985 
OS/multi Quartile 4: ≥33
Quartile 3: 21-32
Quartile 2:14-20
Quartile 1: 2-13 
Ref.
1.05 (1.04-1.06),
1.08 (1.07-1.10),
1.14 (1.11-1.17)  
Treatment at facility with lowest volume 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi Quartile 4c: highest
Quartile 1: lowest
Quartile 2
Quartile 3 
Ref.
1.5 (1.18-1.88),
1.4 (1.17-1.72) 
1.13 (0.92-1.38) 
Treatment at facility with lowest volume 
Go (2017) NCDB
2003-2011 
National MM
94 722 
OS/multi Quartile 4: >10.3
Quartile 3: 6.2-10.3
Quartile 2: 3.6-6.1
Quartile 1: <3.6 
Ref.b
1.16 (1.1-1.22),
1.23 (1.16-1.3),
1.26 (1.17-1.3)  
Treatment at facility with lowest volume 
Provider expertise 
Loberiza et al,30 (2009) UNMC Database
1982-2006 
NE HL
2330 
OS/multi University-based
Community-based 
Ref.
1.26 (1.06-1.49)  
Rural patients treated by community-based providers 
Shanafelt et al,29 (2012) The Mayo Clinic CLL Database
1999-2009 
MN CLL/SLL.
1309 
OS/multi CLL hematologist
Hematologist 
Ref.
1.49 (1.08-2.04)  
Treatment by a non-CLL hematologist 
     Hematologist
Fellow 
Ref.
0.98 (0.64-1.50) 

AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; CA, California; CCR, California Cancer Registry; CI, confidential interval; CLL, chronic lymphocytic leukemia; CSS, cancer-specific survival; Dx, diagnosis; HL, Hodgkin lymphoma; HR, Hazard Ratio; MM, Multiple myeloma; MN, Minnesota; multi, Multivariate analysis; N, Sample size; NC, North Carolina; NCDB, National Cancer Database; NC CCR, North Carolina Central Cancer Registry; NCCN, National Comprehensive Cancer Network; NCI-CCC, National Cancer Institute–designated comprehensive cancer center; NHL, non-Hodgkin Lymphoma; NS, not specified; OH, Ohio; P-bone, plasmacytoma originating in bone; P-EM, pasmacytoma originating in extramedullary; PFS, progression-free survival; PM, plasmacytoma; Ref., reference; SEER, Surveillance, Epidemiology, and End Results Registry; SLL, small lymphocytic lymphoma; TX, Texas; Tx, treatment; uni, univariate analysis; UNMC, University of Nebraska Medical Center Oncology Database; WM, Waldenstrom macroglobulinemia; y.o., years old.

Reported as statistical significance by the author.

Statistical significance but not discussed by the author.

Study reported results according to different periods. The most recent year was considered.

Table 3.

Summary of descriptive characteristics of included studies evaluating median household income (n = 27), percentage of population living in poverty (n = 4), and unemployment rate (n = 2)

Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Median household income (annual) 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi < $30 000
$30 000-34 999
$35 000-45 999
≥46 000 
Ref.
0.86 (0.74-0.99),
0.84 (0.72-0.97),
0.81 (0.69-0.95)  
Income lower than $30 000 
Loberiza et al,30 (2009) UNMC
1982-2006 
NE HL
2 330 
OS/multi ≤ $30 000
$30 001-39 999
$ 40 000-44 999
≥ $45 000 
Data not showed 
Keegan (2009) CCR
1988-2006 
CA HL 15-44 y.o.
8228 
CCS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.64 (1.23-2.20),
1.48 (1.13-1.94),
1.63 (1.26-2.10) 
1.23 (0.95-1.61) 
Lower income 
    OS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.81 (1.46-2.24),
1.57 (1.29-1.92),
1.58 (1.30-1.91),
1.28 (1.06-1.56)  
Lower income 
   HL ≥ 45 y.o.
4264 
CCS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.36 (1.10-1.68)
1.27 (1.04-1.54)
1.19 (0.98-1.44)
1.00 (0.82-1.21) 
Lower income 
    OS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.44 (1.26-1.66)
1.26 (1.01-1.44)
1.31 (1.15-1.48)
1.06 (0.93-1.20) 
Lower income 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/uni < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Data not showed Lower income 
    1-month mortality/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Data not showed 
    OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.95 (0.92-0.98)
0.91 (0.88-0.95),
0.86 (0.82-0.90)  
Income lower than $38 000 
Go (2016) NCDB
1998-2011 
National
 
NHL
278 985 
OS/multi ≥ $46 000
$35 000-$45 999
$30 000-$34 999
< $30 000 
Ref.
1.10 (1.09-1.11),
1.16 (1.15-1.18),
1.25 (1.23-1.27)  
Income lower than $45 999 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL
16 133 
CSS/multi 5. Highest 20% 
1. Lowest 20%
2.
3.
4. 
Ref.
1.24 (1.16-1.32),
1.23 (1.16-1.30),
1.12 (1.06-1.18),
1.07 (1.02-1.13)  
Lower income 
    OS/multi 5. Highest 20% 
1. Lowest 20%
2.
3.
4. 
Ref.
1.34 (1.27-1.40),
1.26 (1.20-1.32),
1.17 (1.12-1.22),
1.09 (1.05-1.14)  
Lower income 
Ai et al,26 (2012) CCR
2001-2008 
CA NHL
213 
OS/multi High: Quartile 4, 5
Low: Quartile 1-3 
Ref.
1.07 (0.73-1.57) 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi ≥ $63 000
< $38 000
$38 000-47 999
$48 000-62 999 
Ref.
1.21 (1.10-1.33) 
1.06 (0.98-1.15)
1.01 (0.94-1.08) 
Income lower than $38 000 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM < 65 y.o.
1249 
OS/uni < $40 227
$40 227–50 353
$50 354–63 332
≥$63 333 
Ref.
0.77 (0.48-1.24)
0.60 (0.38-0.96),
0.55 (0.36-0.83)  
Income lower than $40 227 
    OS/multi < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
1.16 (0.65-2.07)
0.97 (0.51-1.82)
1.12 (0.56-2.23) 
   WM ≥ 65 y.o.
2629 
OS/uni < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
0.98 (0.77-1.25)
0.84 (0.67-1.07)
0.76 (0.61-0.95)  
Income lower than $40 227 
    OS/multi < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
1.02 (0.78-1.35)
0.93 (0.70-1.24)
0.90 (0.66-1.22) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3 064 
OS/multi ≥ $63 000
$48 000–$62 999
$38 000–$47 999
<$38 000 
Ref.
1.19 (0.98-1.44)
1.21 (0.98-1.51)
1.25 (0.96-1.63) 
Fiala et al,15 (2015) Institutional
2000-2009 
MO MM
652 
OS/HR ≥ $57 177
$41 401-57 177
≤ $41 400 
Ref.
1.25 (0.95-1.65)
1.54 (1.13-2.09)  
Income lower than $41 400 
 SEER
2000-2009 
National MM
45 505 
OS/HR ≥ $57 177
$41 401-57 177
≤ $41 400 
Ref.
1.10 (1.07-1.13),
1.18 (1.15-1.22)  
Income lower than $57 177 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.96 (0.93-0.99),
0.93 (0.90-0.97),
0.91 (0.87-0.95)  
Income lower than $38 000 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/uni ≥$63 000
< $63 000 
Ref.
1.25 (1.09-1.43)  
Income lower than $63 000 
    OS/multi ≥$63 000
< $63 000 
Ref.
1.13 (0.89-1.43) 
 NCDB
2004-2015 
National MM
122 858 
OS/multi ≥$63 000
< $63 000 
Ref.
1.11 (1.09-1.14)  
Income lower than $63 000 
Chamoun (2021) NCDB.
2005-2014 
National MM.
117 926 
OS/multi ≥ $46 000
< $46 000 
Ref.
1.16 (1.08-1.25)  
Income lower than $46 000 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality High SES 
Low SES 
Ref.
1.21 (1.03-1.41) 
Lower income 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
OS/multi > $68 500
$56 601-$68 500
$49 301-$56 600
< $49 301 
Ref.
0.97 (0.85-1.10)
1.19 (1.03-1.37),
1.27 (1.09-1.49)  
Income lower than $49 301 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
0.98 (0.90-1.07)
0.92 (0.84-1.01)
0.85 (0.78-0.94),
0.84 (0.76-0.92)  
Income lower than $5000 
    CSS/multi $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
1.10 (0.91-1.30)
1.04 (0.85-1.26)
0.96 (0.76-1.22)
0.97 (0.75-1.25) 
    OS/uni $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
0.95 (0.88-1.02)
0.86 (0.79-0.92),
0.75 (0.70-0.81),
0.69 (0.63-0.75)  
Income lower than $5000 
    OS/multi $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
1.07 (0.92-1.24)
0.91 (0.77-1.08)
0.93 (0.77-1.11)
1.00 (0.79-1.28) 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.78 (0.67-0.92),
0.77 (0.65-0.89),
0.72 (0.61-0.85)  
Income lower than $38 000 
   PM
P-EM
1468 
OS/uni < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.85 (0.65-1.11)
0.77 (0.59-1.01)
0.76 (0.58-0.98)  
Income lower than $38 000 
    OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.94 (0.71-1.25)
0.84 (0.63-1.12)
0.84 (0.63-1.13) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi ≥ 46 000
<$30 000
$30 000-34 999
$35 000-45 999 
Ref.
1.26 (1.18-1.35),
1.16 (1.10-1.24),
1.12 (1.06-1.17)  
Income lower than $45 999 
    OS/multi ≥ 46 000
< $30 000
$30 000-34 999
$35 000-45 999 
Ref.
1.09 (1.05-1.12),
1.07 (1.05-1.10),
1.05 (1.03-1.07)  
Income lower than $45 999 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi Fourth Quartile 
First Quartile
Second Quartile
Third Quartile 
Ref.
1.22 (0.81-1.86)
1.18 (0.86-1.62)
1.02 (0.77-1.33) 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
2-month mortality/uni Quintile 5 
Quintile 1
Quintile 2
Quintile 3
Quintile 4 
Ref. OR 1.60 (1.20-2.13),
1.54 (1.16-2.04),
1.43 (1.06-1.93),
1.39 (1.04-1.86)  
Lower income 
    OS/multi Quintile 5 
Quintile 1
Quintile 2
Quintile 3
Quintile 4 
Ref. OR 1.21 (1.08-1.36),
1.25 (1.11-1.40),
1.25 (1.11-1.40) 
1.11 (0.99-1.25) 
Lower income 
Baker (2009) CIBMTR
1995-2004 
National AML/ALL/CML/ MDS. 6207 DFS > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.03 (0.94-1.13)
1.02 (0.93-1.12)
1.12 (1.01-1.23) 
    TRM > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.11 (0.99-1.24)
1.11 (0.99-1.24)
1.21 (1.07-1.36)  
Income lower than $34 700 
    OS > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.06 (0.97-1.16)
1.06 (0.97-1.16)
1.15 (1.04-1.26)  
Income lower than $34 700 
Rodriguez et al,33 (2008) Institutional
1997-2005 
OH AML
281 
OS/multi Per $10 000 increment 1.02 (0.92-1.13) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi ≥ $46 000
$30 000
$30 000-34 000
$35 000-45 000 
Ref.
1.10 (1.06-1.15),
1.08 (1.05-1.12),
1.05 (1.02-1.08)  
Income lower than $46 000 
Bradley et al,34 (2011) VCR/ VHI
1999-2006 
VA AML No CTX
NS 
OS/multi Advantage Disadvantage Ref.
1.00 (0.99-1.01) 
   AML CTX
NS 
OS/multi Advantage Disadvantage Ref.
1.00 (0.99-1.01) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.02 (0.75-1.37)
1.08 (0.77-1.50) 
   APL ≥65 y.o.
1520 
1-month mortality ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.06 (0.72-1.54)
1.31 (0.87-1.98) 
   APL <65 y.o.
5380 
OS/multi ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.19 (1.00-1.42) 
1.03 (0.85-1.26) 
Income from $50 354 to $63 332 
   APL ≥65 y.o.
1520 
OS/multi ≥ $63 333
$50 354-63 332
$40 227-50 353
< $40 227 
Ref.
1.22 (1.00-1.49)
1.29 (1.03-1.60),
1.36 (1.05-1.76)  
Income lower than $63 333 
Perry et al,16 (2017) SEER
2007-2012 
National CML < 64 y.o.
3626 
OS/HR Above median household income 0.98 (0.69-1.40) 
   CML ≥ 65 y.o.
2142 
OS/HR Above median household income 1.02 (0.83-1.27) 
Percentage of population living in poverty 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni < 10%
10%–19.99%
≥ 20% 
Ref.
1.10 (1.02-1.19),
1.20 (1.09-1.33)  
Poverty level higher than 10% 
    CSS/multi < 10%
10%–19.99%
≥ 20% 
Ref.
0.97 (0.87-1.10)
0.90 (0.73-1.10) 
    OS/uni < 10%
10%–19.99%
≥ 20% 
Ref.
1.27 (1.19-1.35),
1.50 (1.38-1.62)  
Poverty level higher than 10% 
    OS/multi < 10%
10%–19.99%
≥ 20% 
Ref.
1.04 (0.95-1.13)
1.05 (0.91-1.22) 
Byrne et al,35 (2011) FCDS
1998-2002 
FL AML
4659 
OS/multi ≤ 5%
5.1%-10%
10.1%-15%
> 15% 
Ref.
1.04 (0.97-1.14)
1.11 (1.00-1.22)
1.15 (1.04-1.27)  
Poverty level higher than 15% 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi NS 0.99 (0.98-1.01) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.03 (0.69-1.54) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.06 (0.83-1.36) 
Unemployment rate 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML.
900 
OS/multi NS 0.99 (0.97-1.02) 
Perry et al,16 (2017) SEER
2007-2012 
National CML.15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.01 (0.78-1.32) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.02 (0.86-1.20) 
Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95% CI)Social determinant
associated with worse survival
Median household income (annual) 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/multi < $30 000
$30 000-34 999
$35 000-45 999
≥46 000 
Ref.
0.86 (0.74-0.99),
0.84 (0.72-0.97),
0.81 (0.69-0.95)  
Income lower than $30 000 
Loberiza et al,30 (2009) UNMC
1982-2006 
NE HL
2 330 
OS/multi ≤ $30 000
$30 001-39 999
$ 40 000-44 999
≥ $45 000 
Data not showed 
Keegan (2009) CCR
1988-2006 
CA HL 15-44 y.o.
8228 
CCS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.64 (1.23-2.20),
1.48 (1.13-1.94),
1.63 (1.26-2.10) 
1.23 (0.95-1.61) 
Lower income 
    OS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.81 (1.46-2.24),
1.57 (1.29-1.92),
1.58 (1.30-1.91),
1.28 (1.06-1.56)  
Lower income 
   HL ≥ 45 y.o.
4264 
CCS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.36 (1.10-1.68)
1.27 (1.04-1.54)
1.19 (0.98-1.44)
1.00 (0.82-1.21) 
Lower income 
    OS/multi 5: Highest 
1: Lowest
2
3
Ref.
1.44 (1.26-1.66)
1.26 (1.01-1.44)
1.31 (1.15-1.48)
1.06 (0.93-1.20) 
Lower income 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/uni < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Data not showed Lower income 
    1-month mortality/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Data not showed 
    OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.95 (0.92-0.98)
0.91 (0.88-0.95),
0.86 (0.82-0.90)  
Income lower than $38 000 
Go (2016) NCDB
1998-2011 
National
 
NHL
278 985 
OS/multi ≥ $46 000
$35 000-$45 999
$30 000-$34 999
< $30 000 
Ref.
1.10 (1.09-1.11),
1.16 (1.15-1.18),
1.25 (1.23-1.27)  
Income lower than $45 999 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL
16 133 
CSS/multi 5. Highest 20% 
1. Lowest 20%
2.
3.
4. 
Ref.
1.24 (1.16-1.32),
1.23 (1.16-1.30),
1.12 (1.06-1.18),
1.07 (1.02-1.13)  
Lower income 
    OS/multi 5. Highest 20% 
1. Lowest 20%
2.
3.
4. 
Ref.
1.34 (1.27-1.40),
1.26 (1.20-1.32),
1.17 (1.12-1.22),
1.09 (1.05-1.14)  
Lower income 
Ai et al,26 (2012) CCR
2001-2008 
CA NHL
213 
OS/multi High: Quartile 4, 5
Low: Quartile 1-3 
Ref.
1.07 (0.73-1.57) 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi ≥ $63 000
< $38 000
$38 000-47 999
$48 000-62 999 
Ref.
1.21 (1.10-1.33) 
1.06 (0.98-1.15)
1.01 (0.94-1.08) 
Income lower than $38 000 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM < 65 y.o.
1249 
OS/uni < $40 227
$40 227–50 353
$50 354–63 332
≥$63 333 
Ref.
0.77 (0.48-1.24)
0.60 (0.38-0.96),
0.55 (0.36-0.83)  
Income lower than $40 227 
    OS/multi < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
1.16 (0.65-2.07)
0.97 (0.51-1.82)
1.12 (0.56-2.23) 
   WM ≥ 65 y.o.
2629 
OS/uni < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
0.98 (0.77-1.25)
0.84 (0.67-1.07)
0.76 (0.61-0.95)  
Income lower than $40 227 
    OS/multi < $40 227
$40 227–50 353
$50 354–63 332
≥ $63 333 
Ref.
1.02 (0.78-1.35)
0.93 (0.70-1.24)
0.90 (0.66-1.22) 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3 064 
OS/multi ≥ $63 000
$48 000–$62 999
$38 000–$47 999
<$38 000 
Ref.
1.19 (0.98-1.44)
1.21 (0.98-1.51)
1.25 (0.96-1.63) 
Fiala et al,15 (2015) Institutional
2000-2009 
MO MM
652 
OS/HR ≥ $57 177
$41 401-57 177
≤ $41 400 
Ref.
1.25 (0.95-1.65)
1.54 (1.13-2.09)  
Income lower than $41 400 
 SEER
2000-2009 
National MM
45 505 
OS/HR ≥ $57 177
$41 401-57 177
≤ $41 400 
Ref.
1.10 (1.07-1.13),
1.18 (1.15-1.22)  
Income lower than $57 177 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.96 (0.93-0.99),
0.93 (0.90-0.97),
0.91 (0.87-0.95)  
Income lower than $38 000 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/uni ≥$63 000
< $63 000 
Ref.
1.25 (1.09-1.43)  
Income lower than $63 000 
    OS/multi ≥$63 000
< $63 000 
Ref.
1.13 (0.89-1.43) 
 NCDB
2004-2015 
National MM
122 858 
OS/multi ≥$63 000
< $63 000 
Ref.
1.11 (1.09-1.14)  
Income lower than $63 000 
Chamoun (2021) NCDB.
2005-2014 
National MM.
117 926 
OS/multi ≥ $46 000
< $46 000 
Ref.
1.16 (1.08-1.25)  
Income lower than $46 000 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality High SES 
Low SES 
Ref.
1.21 (1.03-1.41) 
Lower income 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
OS/multi > $68 500
$56 601-$68 500
$49 301-$56 600
< $49 301 
Ref.
0.97 (0.85-1.10)
1.19 (1.03-1.37),
1.27 (1.09-1.49)  
Income lower than $49 301 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
0.98 (0.90-1.07)
0.92 (0.84-1.01)
0.85 (0.78-0.94),
0.84 (0.76-0.92)  
Income lower than $5000 
    CSS/multi $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
1.10 (0.91-1.30)
1.04 (0.85-1.26)
0.96 (0.76-1.22)
0.97 (0.75-1.25) 
    OS/uni $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
0.95 (0.88-1.02)
0.86 (0.79-0.92),
0.75 (0.70-0.81),
0.69 (0.63-0.75)  
Income lower than $5000 
    OS/multi $1926-5000
$5007-6081
$6090-6504
$6517-8185
$8197-11 097 
Ref.
1.07 (0.92-1.24)
0.91 (0.77-1.08)
0.93 (0.77-1.11)
1.00 (0.79-1.28) 
Ghiassi-Nejad et al,23 (2019) NCDB
2004-2013 
National PM
P-bone
4056 
OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.78 (0.67-0.92),
0.77 (0.65-0.89),
0.72 (0.61-0.85)  
Income lower than $38 000 
   PM
P-EM
1468 
OS/uni < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.85 (0.65-1.11)
0.77 (0.59-1.01)
0.76 (0.58-0.98)  
Income lower than $38 000 
    OS/multi < $38 000
$38 000-47 999
$48 000-62 999
≥ $63 000 
Ref.
0.94 (0.71-1.25)
0.84 (0.63-1.12)
0.84 (0.63-1.13) 
Bhatt (2017) NCDB
2003-2011 
National AML
60 738 
1-month mortality/multi ≥ 46 000
<$30 000
$30 000-34 999
$35 000-45 999 
Ref.
1.26 (1.18-1.35),
1.16 (1.10-1.24),
1.12 (1.06-1.17)  
Income lower than $45 999 
    OS/multi ≥ 46 000
< $30 000
$30 000-34 999
$35 000-45 999 
Ref.
1.09 (1.05-1.12),
1.07 (1.05-1.10),
1.05 (1.03-1.07)  
Income lower than $45 999 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi Fourth Quartile 
First Quartile
Second Quartile
Third Quartile 
Ref.
1.22 (0.81-1.86)
1.18 (0.86-1.62)
1.02 (0.77-1.33) 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
2-month mortality/uni Quintile 5 
Quintile 1
Quintile 2
Quintile 3
Quintile 4 
Ref. OR 1.60 (1.20-2.13),
1.54 (1.16-2.04),
1.43 (1.06-1.93),
1.39 (1.04-1.86)  
Lower income 
    OS/multi Quintile 5 
Quintile 1
Quintile 2
Quintile 3
Quintile 4 
Ref. OR 1.21 (1.08-1.36),
1.25 (1.11-1.40),
1.25 (1.11-1.40) 
1.11 (0.99-1.25) 
Lower income 
Baker (2009) CIBMTR
1995-2004 
National AML/ALL/CML/ MDS. 6207 DFS > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.03 (0.94-1.13)
1.02 (0.93-1.12)
1.12 (1.01-1.23) 
    TRM > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.11 (0.99-1.24)
1.11 (0.99-1.24)
1.21 (1.07-1.36)  
Income lower than $34 700 
    OS > $56 300
$43 600-56 300
$34 700-43 600
< $34 700 
Ref.
1.06 (0.97-1.16)
1.06 (0.97-1.16)
1.15 (1.04-1.26)  
Income lower than $34 700 
Rodriguez et al,33 (2008) Institutional
1997-2005 
OH AML
281 
OS/multi Per $10 000 increment 1.02 (0.92-1.13) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML
67 443 
OS/multi ≥ $46 000
$30 000
$30 000-34 000
$35 000-45 000 
Ref.
1.10 (1.06-1.15),
1.08 (1.05-1.12),
1.05 (1.02-1.08)  
Income lower than $46 000 
Bradley et al,34 (2011) VCR/ VHI
1999-2006 
VA AML No CTX
NS 
OS/multi Advantage Disadvantage Ref.
1.00 (0.99-1.01) 
   AML CTX
NS 
OS/multi Advantage Disadvantage Ref.
1.00 (0.99-1.01) 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.02 (0.75-1.37)
1.08 (0.77-1.50) 
   APL ≥65 y.o.
1520 
1-month mortality ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.06 (0.72-1.54)
1.31 (0.87-1.98) 
   APL <65 y.o.
5380 
OS/multi ≥ $63 333
$50 354-63 332
$40 227-50 353 
Ref.
1.19 (1.00-1.42) 
1.03 (0.85-1.26) 
Income from $50 354 to $63 332 
   APL ≥65 y.o.
1520 
OS/multi ≥ $63 333
$50 354-63 332
$40 227-50 353
< $40 227 
Ref.
1.22 (1.00-1.49)
1.29 (1.03-1.60),
1.36 (1.05-1.76)  
Income lower than $63 333 
Perry et al,16 (2017) SEER
2007-2012 
National CML < 64 y.o.
3626 
OS/HR Above median household income 0.98 (0.69-1.40) 
   CML ≥ 65 y.o.
2142 
OS/HR Above median household income 1.02 (0.83-1.27) 
Percentage of population living in poverty 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni < 10%
10%–19.99%
≥ 20% 
Ref.
1.10 (1.02-1.19),
1.20 (1.09-1.33)  
Poverty level higher than 10% 
    CSS/multi < 10%
10%–19.99%
≥ 20% 
Ref.
0.97 (0.87-1.10)
0.90 (0.73-1.10) 
    OS/uni < 10%
10%–19.99%
≥ 20% 
Ref.
1.27 (1.19-1.35),
1.50 (1.38-1.62)  
Poverty level higher than 10% 
    OS/multi < 10%
10%–19.99%
≥ 20% 
Ref.
1.04 (0.95-1.13)
1.05 (0.91-1.22) 
Byrne et al,35 (2011) FCDS
1998-2002 
FL AML
4659 
OS/multi ≤ 5%
5.1%-10%
10.1%-15%
> 15% 
Ref.
1.04 (0.97-1.14)
1.11 (1.00-1.22)
1.15 (1.04-1.27)  
Poverty level higher than 15% 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi NS 0.99 (0.98-1.01) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.03 (0.69-1.54) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.06 (0.83-1.36) 
Unemployment rate 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML.
900 
OS/multi NS 0.99 (0.97-1.02) 
Perry et al,16 (2017) SEER
2007-2012 
National CML.15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.01 (0.78-1.32) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.02 (0.86-1.20) 

ALL, acute lymphoblastic lymphoma, AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; CA, California; CCR, California Cancer Registry; CI, confidential interval; CIBMTR, Center for International Blood and Marrow Transplant Research; CML, chronic myeloid leukemia; CSS, cancer-specific survival; CTX, chemotherapy; DFS, disease-free survival; FCDS, Florida Cancer Data System; FL, Florida; HL, Hodgkin lymphoma; HR, hazard ratio; MDS, myelodysplastic syndromes; MO, Missouri; MM, multiple myeloma; multi, multivariate analysis; N, sample size; NC, North Carolina; NCDB, National Cancer Database; NC CCR, North Carolina Central Cancer Registry; NE, Nebraska; NHL, Nnn-Hodgkin Lymphoma; NS, not specified; OH, Ohio; OR, odds ratio; P-bone, plasmacytoma originating in bone; P-EM, plasmacytoma originating in extramedullary; PM, plasmacytoma; Ref., reference; SES, socioeconomic status; SEER, Surveillance, Epidemiology, and End Results Registry; TRM, transplant-related mortality; uni, univariate analysis; UNMC, University of Nebraska Medical Center Oncology Database; VA, Virginia; VCR, Virginia Cancer Registry; VHI, Virginia Health Information; WM, Waldenstrom macroglobulinemia; y.o., years old.

Reported as statistical significance by the author.

Statistical significance but not discussed by the author.

Not specified values.

Table 4.

Summary of descriptive characteristics of included studies evaluating marital status (n = 10) and education level (n = 19)

Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95%-CI)Social determinant
associated with worse survival
Marital status 
Yung et al,19 (2011) NYSCR
2001–2008 
NY HL
4752 
OS/multi Married
Unmarried 
Ref.
1.49 (1.11-1.99)  
Unmarried 
 CCR
2000–2007 
CA HL
4752 
OS/multi Married
Unmarried 
Ref.
1.26 (0.97-1.64) 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL
16 133 
CSS/multi Married
Never married
Previously married 
Ref.
1.13 (1.07-1.20),
1.13 (1.09-1.18)  
Unmarried 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/multi Married
Single 
Ref.
1.15 (1.06-1.24)  
Single 
    OS/multi Married
Single
Other 
Ref.
1.29 (1.22-1.38),
1.35 (1.26-1.44)  
Single, divorced, separated, domestic partner, or widowed 
Makhani (2021) SEER
2007-2016 
National MM
41 789 
OS/multi Married
Not Married 
Ref.
1.18 (1.13-1.22)  
Unmarried 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/multi Married
Unmarried 
Ref.
1.32 (1.03-1.69)  
Unmarried 
Costa et al,37 
2016 
SEER
2007-2012 
National MM
10 161 
1-year mortality Married
Divorced
Single
Widowed 
Ref.
1.31 (1.09-1.58),
1.74 (1.48-2.03),
1.38 (1.01-1.88)  
Divorced, single or widowed 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality Married
Single 
Ref.
1.25 (1.07-1.46)  
Single 
Yung et al,19 (2011) NYSCR
2001–2008 
NY AML
3506 
OS/multi Married
Unmarried 
Ref.
1.25 (1.07-1.45)  
Unmarried 
 CCR
2000–2007 
CA AML
3506 
OS/multi Married
Unmarried 
Ref.
1.31 (1.17-1.48)  
Unmarried 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
OS/multi Married
Single
Divorced
Widowed 
Ref.
1.26 (1.15-1.38),
1.16 (1.04-1.30) 
1.14 (0.93-1.41) 
Single or divorced 
    OS/multi Married
Divorced
Single
Widowed 
Ref.
1.24 (1.11-1.39),
1.39 (1.27-1.53),
1.43 (1.20-1.71)  
Divorced, single, or widowed 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Married
Single
Divorced/separated/widowed 
Reference
1.65 (1.29-2.13) 
1.30 (0.94-1.79) 
Single 
   CML 65 y.o.
2142 
OS/multi Married
Single
Divorced, separated, or widowed 
Ref.
1.15 (0.89-1.48)
1.35 (1.16-1.59)  
Divorced, widowed, or separated 
Education: percentage of population without high school degree 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/uni ≥ 29%
20%-28.9%
14%-19.9%
<14% 
Ref.
0.83 (0.78-0.89),
0.78 (0.74-0.83),
0.62 (0.58-0.66)  
Higher than 29% 
    OS/multi ≥ 29%
20%-28.9%
14%-19.9%
<14% 
Ref.
0.98 (0.86-1.13)
1.14 (0.98-1.32)
0.97 (0.82-1.13) 
Goldstein et al,21 (2019) NCDB
2004–2014 
National NHL ≥ 65y.o.
161 
OS/uni < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
1.18 (0.7-2)
1.09 (0.6-1.9)
1.75 (1.3-1) 
Han (2014) NCDB
2004-2010 
National NHL
3 858 
OS/multi < 14%
14%-9.9%
20%-28.9%
≥ 29% 
Ref.
1.17 (0.99-1.39)
1.27 (1.07-1.50),
1.51 (1.25-1.82)  
Higher than 20% 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi < 7%
≥ 21%
13%-20%
7%-12.99% 
Ref.
1.14 (1.03-1.27),
1.16 (1.07-1.26),
1.12 (1.05-1.20)  
Higher than 7% 
Goldstein et al,21 (2019) NCDB
2004-2014 
National NHL < 65 y.o.
22 133 
OS/multi < 7%
7%-12.9%
13%-20.9%
> 21% 
Ref.
1.19 (1.05-1.34),
1.40 (1.24-1.59),
1.42 (1.22-1.64)  
Higher than 7% 
   NHL ≥ 65 y.o.
21 515 
OS/multi < 7%
7%-12.9%
13%-20.9%
> 21% 
Ref.
1.03 (0.96-1.11)
1.09 (1.01-1.18),
1.19 (1.09-1.31)  
Higher than 13% 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/Uni NS Data not showed Lower education level 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi High 
Middle-2
Middle-1
Low 
Ref.
1.16 (0.96-1.4)
1.30 (1.04-1.63)
1.28 (0.97-1.69) 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM < 65 y.o.
1249 
OS/uni ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
0.90 (0.56-1.43)
0.81 (0.52-1.28)
0.58 (0.36-0.93)  
Higher than 17.6% 
    OS/multi ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
1.02 (0.59-1.78)
1.02 (0.55-1.88)
0.86 (0.43-1.75) 
   WM ≥ 65 y.o.
2629 
OS/multi ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
0.97 (0.75-1.26)
0.98 (0.75-1.27)
0.84 (0.72-0.97)  
Higher than 17.6% 
Goldstein et al,21 (2019) NCDB
2004–2014 
National BL 65 y.o.
5235 
OS/multi < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
1.1 (0.9-1.3)
1.35 (1.1-1.6)
1.39 (1.1-1.7)  
Higher than 21% 
   BL ≥ 65 y.o.
1838 
OS/multi < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
0.8 (0.6-1),
0.9 (0.7-1.2),
0.72 (0.5-1)  
Higher than 7% 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/uni ≤ 7%
>7% 
Ref.
1.02 (0.88-1.18) 
 NCDB
2004-2015 
National MM
2543 
OS/multi ≤ 7%
>7% 
Ref.
1.08 (1.06-1.11)  
Higher than 7% 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi ≥ 21%
13.0%-20.9%
7.0%-12.9%
< 7.0% 
Ref.
0.99 (0.95-1.02)
0.98 (0.94-1.02)
0.94 (0.89-0.99)  
Higher than 21% 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
12 month- mortality/multi < 21.3%
> 36.8%
29.7%-36.8%
21.3%-29.7% 
Ref.
0.92 (0.72-1.17)
1.03 (0.80-1.32)
1.34 (1.05-1.72)  
From 21.3% to 29.7% 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.87 (0.80-0.96),
0.87 (0.79-0.95),
0.87 (0.80-0.96),
0.80 (0.73-0.88)  
From 68.3% to 21.52% 
    OS/uni < 21.3%
> 36.8%
29.7%-36.8%
21.3%-29.7% 
Ref.
1.13 (1.02-1.27),
1.19 (1.07-1.33),
1.46 (1.31-1.62)  
Higher than 21.3% 
    CSS/multi 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.95 (0.85-1.06)
0.93 (0.82-1.05)
1.02 (0.89-1.17)
0.96 (0.82-1.13) 
    OS/uni 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.88 (0.81-0.94),
0.91 (0.85-0.98),
0.80 (0.75-0.86),
0.68 (0.63-0.74)  
From 6.83% to 21.52% 
    OS/multi 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.98 (0.90-1.06)
1.07 (0.97-1.18)
1.02 (0.92-1.14)
0.90 (0.79-1.03) 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi NS 1.00 (0.99-1.02) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML.
67 443 
OS/multi < 14%
14%-19.9%
20%-28.9%
≥ 29% 
Ref.
1.03 (1.00-1.06),
1.05 (1.02-1.08) 
1.01 (0.97-1.05) 
From 14% to 28.9% 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
OS/uni Quintile 5 
Quintile1
Quintile 2
Quintile 3
Quintile 4 
Ref.
1.27 (1.13-1.42) 
1.17 (1.05-1.31)
1.17 (1.04-1.31)
1.15 (1.02-1.30) 
Lower education level 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.18 (0.86-1.61)
1.02 (0.72-1.46)
1.40 (0.96-2.06) 
   APL ≥65 y.o.
1520 
1-month mortality <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.38- (0.95-2.02)
1.14 (0.73-1.76)
1.18 (0.71-1.97) 
   APL <65 y.o.
5380 
OS/multi <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
0.99 (0.83-1.19)
1.08 (0.89-1.32)
1.14 (0.91-1.43) 
   APL ≥65 y.o.
1520 
OS/multi <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.12 (0.91-1.37)
1.12 (0.89-1.41)
1.11 (0.85-1.45) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.05 (0.73-1.52) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.20 (0.94-1.53) 
Education: percentage of population without <ninth grade degree 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/HR Above median % ninth grade education NS 
   CML ≥ 65 y.o.
2142 
OS/HR Above median % ninth grade education NS 
Author (year)Data source
years
US regionPatients
N
Outcome/analysisCohortsHR (95%-CI)Social determinant
associated with worse survival
Marital status 
Yung et al,19 (2011) NYSCR
2001–2008 
NY HL
4752 
OS/multi Married
Unmarried 
Ref.
1.49 (1.11-1.99)  
Unmarried 
 CCR
2000–2007 
CA HL
4752 
OS/multi Married
Unmarried 
Ref.
1.26 (0.97-1.64) 
Tao et al,25 (2014) CCR
2001-2009 
CA NHL
16 133 
CSS/multi Married
Never married
Previously married 
Ref.
1.13 (1.07-1.20),
1.13 (1.09-1.18)  
Unmarried 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/multi Married
Single 
Ref.
1.15 (1.06-1.24)  
Single 
    OS/multi Married
Single
Other 
Ref.
1.29 (1.22-1.38),
1.35 (1.26-1.44)  
Single, divorced, separated, domestic partner, or widowed 
Makhani (2021) SEER
2007-2016 
National MM
41 789 
OS/multi Married
Not Married 
Ref.
1.18 (1.13-1.22)  
Unmarried 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/multi Married
Unmarried 
Ref.
1.32 (1.03-1.69)  
Unmarried 
Costa et al,37 
2016 
SEER
2007-2012 
National MM
10 161 
1-year mortality Married
Divorced
Single
Widowed 
Ref.
1.31 (1.09-1.58),
1.74 (1.48-2.03),
1.38 (1.01-1.88)  
Divorced, single or widowed 
Ho et al,24 (2017) CCR
1999-2012 
CA MM
6359 
60-day mortality Married
Single 
Ref.
1.25 (1.07-1.46)  
Single 
Yung et al,19 (2011) NYSCR
2001–2008 
NY AML
3506 
OS/multi Married
Unmarried 
Ref.
1.25 (1.07-1.45)  
Unmarried 
 CCR
2000–2007 
CA AML
3506 
OS/multi Married
Unmarried 
Ref.
1.31 (1.17-1.48)  
Unmarried 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
OS/multi Married
Single
Divorced
Widowed 
Ref.
1.26 (1.15-1.38),
1.16 (1.04-1.30) 
1.14 (0.93-1.41) 
Single or divorced 
    OS/multi Married
Divorced
Single
Widowed 
Ref.
1.24 (1.11-1.39),
1.39 (1.27-1.53),
1.43 (1.20-1.71)  
Divorced, single, or widowed 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Married
Single
Divorced/separated/widowed 
Reference
1.65 (1.29-2.13) 
1.30 (0.94-1.79) 
Single 
   CML 65 y.o.
2142 
OS/multi Married
Single
Divorced, separated, or widowed 
Ref.
1.15 (0.89-1.48)
1.35 (1.16-1.59)  
Divorced, widowed, or separated 
Education: percentage of population without high school degree 
Parikh et al,39 (2015) NCDB
1998-2011 
National HL
45 777 
OS/uni ≥ 29%
20%-28.9%
14%-19.9%
<14% 
Ref.
0.83 (0.78-0.89),
0.78 (0.74-0.83),
0.62 (0.58-0.66)  
Higher than 29% 
    OS/multi ≥ 29%
20%-28.9%
14%-19.9%
<14% 
Ref.
0.98 (0.86-1.13)
1.14 (0.98-1.32)
0.97 (0.82-1.13) 
Goldstein et al,21 (2019) NCDB
2004–2014 
National NHL ≥ 65y.o.
161 
OS/uni < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
1.18 (0.7-2)
1.09 (0.6-1.9)
1.75 (1.3-1) 
Han (2014) NCDB
2004-2010 
National NHL
3 858 
OS/multi < 14%
14%-9.9%
20%-28.9%
≥ 29% 
Ref.
1.17 (0.99-1.39)
1.27 (1.07-1.50),
1.51 (1.25-1.82)  
Higher than 20% 
Shah (2019) NCDB
2004-2013 
National NHL
18 120 
OS/multi < 7%
≥ 21%
13%-20%
7%-12.99% 
Ref.
1.14 (1.03-1.27),
1.16 (1.07-1.26),
1.12 (1.05-1.20)  
Higher than 7% 
Goldstein et al,21 (2019) NCDB
2004-2014 
National NHL < 65 y.o.
22 133 
OS/multi < 7%
7%-12.9%
13%-20.9%
> 21% 
Ref.
1.19 (1.05-1.34),
1.40 (1.24-1.59),
1.42 (1.22-1.64)  
Higher than 7% 
   NHL ≥ 65 y.o.
21 515 
OS/multi < 7%
7%-12.9%
13%-20.9%
> 21% 
Ref.
1.03 (0.96-1.11)
1.09 (1.01-1.18),
1.19 (1.09-1.31)  
Higher than 13% 
Dhakal et al,22 (2019) NCDB
2006-2012 
National NHL
132 402 
1-month mortality/Uni NS Data not showed Lower education level 
Gunaratne et al,35 (2021) NCBD
2004-2014 
National WM
3064 
OS/multi High 
Middle-2
Middle-1
Low 
Ref.
1.16 (0.96-1.4)
1.30 (1.04-1.63)
1.28 (0.97-1.69) 
Chohan et al,18 (2022) NCDB
2004-2017 
National WM < 65 y.o.
1249 
OS/uni ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
0.90 (0.56-1.43)
0.81 (0.52-1.28)
0.58 (0.36-0.93)  
Higher than 17.6% 
    OS/multi ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
1.02 (0.59-1.78)
1.02 (0.55-1.88)
0.86 (0.43-1.75) 
   WM ≥ 65 y.o.
2629 
OS/multi ≥ 17.6%
10.9%–17.5%
6.3%–10.8%
<6.3% 
Ref.
0.97 (0.75-1.26)
0.98 (0.75-1.27)
0.84 (0.72-0.97)  
Higher than 17.6% 
Goldstein et al,21 (2019) NCDB
2004–2014 
National BL 65 y.o.
5235 
OS/multi < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
1.1 (0.9-1.3)
1.35 (1.1-1.6)
1.39 (1.1-1.7)  
Higher than 21% 
   BL ≥ 65 y.o.
1838 
OS/multi < 7%
7% to 12.9%
13% to 20.9%
> 21% 
Ref.
0.8 (0.6-1),
0.9 (0.7-1.2),
0.72 (0.5-1)  
Higher than 7% 
Evans et al,40 (2021) Institutional 2005-2015 NS MM
2543 
OS/uni ≤ 7%
>7% 
Ref.
1.02 (0.88-1.18) 
 NCDB
2004-2015 
National MM
2543 
OS/multi ≤ 7%
>7% 
Ref.
1.08 (1.06-1.11)  
Higher than 7% 
Jayakrishnan (2021) NCDB
2004-2016 
National MM
50 543 
OS/multi ≥ 21%
13.0%-20.9%
7.0%-12.9%
< 7.0% 
Ref.
0.99 (0.95-1.02)
0.98 (0.94-1.02)
0.94 (0.89-0.99)  
Higher than 21% 
Costa et al,37 (2016) SEER
2007-2012 
National MM
10 161 
12 month- mortality/multi < 21.3%
> 36.8%
29.7%-36.8%
21.3%-29.7% 
Ref.
0.92 (0.72-1.17)
1.03 (0.80-1.32)
1.34 (1.05-1.72)  
From 21.3% to 29.7% 
Huang et al,31 (2021) SEER
2007-2016 
National MM
17 981 
CSS/uni 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.87 (0.80-0.96),
0.87 (0.79-0.95),
0.87 (0.80-0.96),
0.80 (0.73-0.88)  
From 68.3% to 21.52% 
    OS/uni < 21.3%
> 36.8%
29.7%-36.8%
21.3%-29.7% 
Ref.
1.13 (1.02-1.27),
1.19 (1.07-1.33),
1.46 (1.31-1.62)  
Higher than 21.3% 
    CSS/multi 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.95 (0.85-1.06)
0.93 (0.82-1.05)
1.02 (0.89-1.17)
0.96 (0.82-1.13) 
    OS/uni 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.88 (0.81-0.94),
0.91 (0.85-0.98),
0.80 (0.75-0.86),
0.68 (0.63-0.74)  
From 6.83% to 21.52% 
    OS/multi 6.83%–21.52%
21.62%–30.99%
31.08%–34.03%
34.07%–41.84%
42.01%–65.45% 
Ref.
0.98 (0.90-1.06)
1.07 (0.97-1.18)
1.02 (0.92-1.14)
0.90 (0.79-1.03) 
Freeman et al,32 (2016) NC CCR
2003-2009 
NC AML
900 
OS/multi NS 1.00 (0.99-1.02) 
Master et al,28 (2016) NCDB
1998 -2012 
National AML.
67 443 
OS/multi < 14%
14%-19.9%
20%-28.9%
≥ 29% 
Ref.
1.03 (1.00-1.06),
1.05 (1.02-1.08) 
1.01 (0.97-1.05) 
From 14% to 28.9% 
Borate et al,38 (2015) SEER
2007-2011 
National AML
5541 
OS/uni Quintile 5 
Quintile1
Quintile 2
Quintile 3
Quintile 4 
Ref.
1.27 (1.13-1.42) 
1.17 (1.05-1.31)
1.17 (1.04-1.31)
1.15 (1.02-1.30) 
Lower education level 
Dhakal et al,17 (2022) NCDB
2004-2015 
National APL <65 y.o.
5380 
1-month mortality <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.18 (0.86-1.61)
1.02 (0.72-1.46)
1.40 (0.96-2.06) 
   APL ≥65 y.o.
1520 
1-month mortality <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.38- (0.95-2.02)
1.14 (0.73-1.76)
1.18 (0.71-1.97) 
   APL <65 y.o.
5380 
OS/multi <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
0.99 (0.83-1.19)
1.08 (0.89-1.32)
1.14 (0.91-1.43) 
   APL ≥65 y.o.
1520 
OS/multi <6.3
6.3-10.8
10.9-17.5
≥17.6 
Ref.
1.12 (0.91-1.37)
1.12 (0.89-1.41)
1.11 (0.85-1.45) 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/multi Below median
Above median 
Ref.
1.05 (0.73-1.52) 
   CML ≥ 65 y.o.
2142 
OS/multi Below median
Above median 
Ref.
1.20 (0.94-1.53) 
Education: percentage of population without <ninth grade degree 
Perry et al,16 (2017) SEER
2007-2012 
National CML 15-64 y.o.
3626 
OS/HR Above median % ninth grade education NS 
   CML ≥ 65 y.o.
2142 
OS/HR Above median % ninth grade education NS 

AML, acute myeloid leukemia; APL, acute promyelocytic leukemia; BL, Burkitt lymphoma; CA, California; CCR, California Cancer Registry; CI, confidential interval; CML, chronic myeloid leukemia; CSS, cancer-specific survival; HL, Hodgkin lymphoma; HR, hazard ratio; MM, multiple myeloma; multi, multivariate analysis; N, sample size; NC, North Carolina; NCDB, National Cancer Database; NC CCR, North Carolina Central Cancer Registry; NHL, non-Hodgkin lymphoma; NOS, not otherwise specified; NS, not specified; NY, New York; NYSCR, New York state cancer registry; Ref., Reference; SEER, Surveillance, Epidemiology, and End Results Registry; uni, univariate analysis; WM, Waldenstrom macroglobulinemia; y.o., years old.

Reported as statistical significance by the author.

Statistical significance but not discussed by the author.

Not specified values.

Figure 1.

Distribution of the studies according to the impact of SDH within the major domains on cancer-treatment outcomes.

Figure 1.

Distribution of the studies according to the impact of SDH within the major domains on cancer-treatment outcomes.

Close modal

Health care access and quality

Included studies encompassed a variety of variables to evaluate health care access and quality, including health insurance coverage (n = 33, 47.1%), treatment facility type (n = 20, 28.5%), distance traveled from residence to treatment facility (n = 11, 15.7%), provider case volume (n = 4, 5.7%), and provider expertise (n = 2, 2.8%) (supplemental Table 6).

Health insurance coverage

Health insurance coverage at diagnosis was the most studied SDH across all included manuscripts. There was variability in the grouping of insurance status across studies. However, broadly, the cohorts were compared in terms of private, or managed care vs uninsured, Medicare or Medicaid. Most studies (n = 30, 90.9%) found a significant association between health insurance coverage and survival in multivariable, univariate, and/or subgroup analysis. Overall, patients with Medicaid, Medicare, or other government insurance or who are uninsured have inferior survival rates compared with those with private or military insurance coverage. Of note, one of these studies reported that the difference was significant only in univariate analysis. Data source from that study was single-center data with a small sample size (652 patients) compared with other studies included.15 In 3 out of the 30 studies, insurance coverage influenced survival only in patients younger than 65 years old.16-18 In contrast, 3 studies (9.1%) reported no association between insurance status and cancer outcomes, including 2 studies of acute myeloid leukemia (AML)19,20 and 1 of non-Hodgkin lymphoma (NHL)21 (Table 1).

Facility type

Twenty studies examined the impact of the type of facility on cancer outcome, including treatment facility type (n = 18, 64.3%), being diagnosed at National Cancer Institute (NCI)–designated cancer centers (n = 1, 3.6%), and access to NCI- and National Comprehensive Cancer Network (NCCN)–designated cancer centers (n = 1, 3.6%). Fourteen of 18 studies (77.8%) demonstrated a significant improvement in early and OS for patients treated at academic/research cancer centers compared with those receiving treatment at community cancer centers, comprehensive community cancer centers, or integrated network cancer programs. Of those, 1 study for NHL22 found a statistically significant association in unadjusted analysis for patients treated at academic cancer centers (vs nonacademic centers) that was not confirmed in multivariate analysis. Three studies described significant association in multivariable-adjusted analysis only in subgroup analysis. Two of them17,18 observed that treatment facility type influenced survival in patients older than 65, but this difference did not translate to patients younger. Another study23 classified plasmacytomas as originating in bone (P-bone) or extramedullary tissue (P-EM) and noted a significant association between facility type and mortality only in the P-bone cohort.

Two studies24,25 evaluated and reported better outcomes among patients treated at NCI–designated cancer centers vs non-NCI–designated cancer centers. However, one other study26 did not observe differences in survival rates based on whether patients were diagnosed at an NCI–designated cancer center or not. In 1 study exploring the impact of NCI/NCCN CC access on outcomes of patients with multiple myeloma (MM),27 access to 1 NCCN CC or 2 or more NCI CCs correlated with OS improvement in a multivariable-adjusted analysis. Finally, 1 study28 established that patients diagnosed and treated at the same facility had an increased risk of death than those diagnosed at one facility and treated at another (Table 2).

Distance traveled to the treatment center

The relationship between distance traveled to the treatment center and cancer outcomes was investigated in 11 studies with inconsistent results. None of the studies specified how distance was measured (eg, straight line vs car driving distance). Unexpectedly, 5 studies (45.5%) identified that 1-month and overall mortality were higher for those patients who traveled shorter distances (range, 6-40 miles), whereas 6 studies (54.5%) did not find a significant association. Three of the 5 studies identified significant differences in multivariable-adjusted analysis in subgroup analyses. Two studies categorized patients according to age (<65 vs ≥65 years old) and found contradictory results. Although in the cohort of Dhakal et al,17 distance traveled affected 1-month mortality only in patients younger than 65 years old, Chohan et al18 reported that this SDH was significantly associated with lower OS in the unadjusted analysis exclusively in subjects older than 65. Similar to the previous results in the insurance status section, Ghiassi-Nejad et al23 described that distance traveled influenced mortality in the unadjusted analysis in patients with P-bone but not in the P-EM cohort (Table 2).

Treatment facility volume

Four studies evaluated facility volume as determined by the mean annual volume of newly diagnosed patients with the disease of interest treated at each treatment facility. All 4 studies supported a volume-outcome relationship: patients treated at lower-volume facilities had a higher mortality risk (Table 2).

Provider expertise

Two studies examining whether provider expertise influences cancer-treatment outcomes reported a significant relationship in multivariable-adjusted analysis. Shanafelt29 showed that patients treated by hematologists/oncologists who specifically focused on their specific malignancy had superior OS than those treated by general hematologists/oncologists. Loberiza et al30 observed a higher risk of death among rural patients treated by community-based providers vs rural patients treated by university-based providers (Table 2).

Economic stability

Economic stability was investigated in 33 studies assessed by median household income (n = 27, 81.8%), poverty (n = 4, 12.1%), and unemployment rate (n = 2, 6.1%) (supplemental Table 6).

Median household income

Household income was typically categorized into quartiles based on the patient’s zip code. Low-income and high-income quantiles varied among studies from 30 000 to 63 332 and 30 000 to 68 500, respectively. Twenty of the 27 studies (74.1%) assessing this SDH demonstrated an influence of income on survival. Collectively, the results show an income gradient where lower income was associated with the shortest survival probability. Two of the 20 studies18,31 (7.4%) only found a difference in the unadjusted model. Of those 7 studies (25.9%) that found no significant correlation between income and outcomes, 5 used state databases with a mean sample size of 849.4 patients (range, 213-2330).26,30,32-34 The remaining 2 studies were population-based and reported no relationship between earnings16,35 (Table 3).

Poverty rate

Four studies (12.1%) evaluated poverty rate–related differences in cancer survival and showed significant heterogeneity in the results. A state-level study36 and a population-based study31 concluded that patients living in areas with the highest poverty levels (≤10.1%) had worse OS than those residing in low-poverty neighborhoods. In contrast, Freeman et al32 and Perry et al16 reported no significant difference in mortality among poverty levels (Table 3).

Unemployed rate

The relationship between the unemployed neighborhood rate and cancer outcome was examined in 2 studies.16,32 Both studies reported no significant correlation between unemployment and patients' survival (Table 3).

Social context

Patients' social context was measured directly and exclusively through marital status in 10 studies (supplemental Table 6). Results showed that marital status consistently and significantly influences OS throughout all studies evaluated in multivariable-adjusted analysis. Overall, unmarried patients, including single, divorced, widowed, and/or separated, had a significantly higher probability of dying compared with married patients. In addition, 2 studies evaluating patients with MM24,37 and 1 with AML22 also attempted to understand whether marital status played a role in early mortality. Both MM studies found higher early mortality among unmarried vs married patients. In contrast, the 1 AML analysis38 found no significant effect of marital status on early mortality (Table 4).

Education

Nineteen studies analyzed whether education level played a role in cancer survival. Most of the studies (n = 18, 94.7%) evaluated education level as determined by the percentage of adults without a high school diploma for the patient's geographical area, whereas 1 study16 (5.3%) assessed education through the proportion of the population without ninth grade education (supplemental Table 6). Overall, 12 studies (66.7%) found that residing in low-education areas correlates with higher mortality. Three of those 12 studies22,31,39 reported statistical significance only in the unadjusted mortality analysis. Six other studies (5.6%)16,17,21,32,35,40 reported no significant relationship between the level of education and patients' outcomes. Finally, Perry16 performed a comparative survival analysis between patients residing in areas above and below the median without ninth grade education and found no statistically significant difference between the cohorts (Table 4).

Health care disparities affect patients with cancer in general and hematologic malignancies in particular. The role of SDHs in the outcome of patients with hematologic malignancies has been scarcely studied. In our review of 41 studies assessing patients with hematologic malignancies, we identified strong evidence of 5 variables of SDHs affecting survival: lack of health insurance or having Medicare or Medicaid insurance, receiving cancer treatment at a nonacademic facility, low household income, low education level, and being unmarried. Our findings have meaningful clinical applications, prompting physicians to identify patients who have been economically and socially marginalized and implement focused interventions.

Cumulative evidence assessing insurance status indicates that uninsured patients and those with Medicaid or Medicare insurance had a higher mortality risk than their private or military insurance counterparts. This might be explained by the fact that uninsured patients are less likely to receive preventive care, have more advanced disease at diagnosis, have more comorbid conditions, and have lower odds of receiving guideline-based treatment, likely driven by the cost of care and costly medications.8,41,42 Given the protective financial role that insurance coverage exerts, the disparities found between patients insured by Medicare and Medicaid and those with private insurance are perhaps surprising and may be related to specific line items covered by various insurance plans. It is also possible that other factors (including other SDH) associated with being covered by Medicaid and Medicare are at least in part responsible for these differences.

The analysis of the type of facility where patients receive treatment and provider case volume revealed a similar impact on cancer survival. Overall, patients receiving treatment at academic/research cancer centers and high-volume facilities achieve better outcomes. This imbalance could be related to academic centers and high-volume facilities having multidisciplinary care teams, disease-specific expertise of physicians, adherence to guidelines, and access to new technologies and therapies, including clinical trials.43,44 Consistent with this notion, evidence for our review revealed that provider expertise, access to NCI- and NCCN–designated cancer centers, and treatment at NCI–designated cancer centers provide a survival advantage. All things considered, our findings suggest that addressing health care access barriers through increasing access to preventive health care, reducing delayed cancer diagnosis and treatments, and expanding access to novel evidence–based therapies are focused actions on eliminating cancer-treatment disparities. Shared-care models where there is access to specialized centers for the care areas that most need it and care with local oncology clinics in close communication with the academic institutions would be optimal. The feasibility of such shared-care models for patients with hematologic malignancies has been shown to improve access to curative therapies, such as stem cell transplant (SCT), and the quality of life of patients, without compromising outcomes.45,46 This requires, among other things, increased communication, true integration of electronic health records, more access to telehealth, and decreasing barriers to practice across state lines.

Another important finding in our review is that unmarried patients present a higher mortality risk than married ones. Although the cause of this effect is multifactorial, the literature suggests the effect comes mainly through emotional and social support.47 Married patients seek prompt medical care encouraged by spouses, display higher treatment adherence, and present fewer mental health conditions such as depression, anxiety, and emotional distress.48-50 Furthermore, the caregiver role that spouses provide is an undervalued but critical component of the health care system. Spouses may support patients in different ways, including by assisting with personal care, making household chores, managing finances and legal matters, making medical appointments, assisting with mobility, and administering medications.

On economic stability and education, our results suggest a protective effect of higher median household income and education levels on survival. Importantly, education access and quality were measured as the percentage of adults who did not graduate from high school. We did not identify any study that evaluated the role of postsecondary degrees, such as undergraduate, graduate, and postgraduate. In addition, this has been assessed at the community level rather than at the individual level. There is a close correlation between the level of education and economic status.51,52 Higher educational levels are linked to more stable employment, higher salaries, and access to work with additional benefits,53 including insurance with better coverage.54 

The reports on the effect of poverty level on patients' outcomes are, in contrast, contradictory. It is difficult to draw any conclusion because of the limited number of studies that evaluated this variable. Altogether, the mechanisms involved in the influence of education and economic stability on cancer survival are multifactorial, complex, and strongly related to other domains of SDHs.

One of the most striking results from our analysis is the role of distance traveled to treatment facilities. Nearly half of the evaluated studies reported higher mortality rates for patients who traveled shorter distances. Although those results need to be interpreted carefully, a possible explanation might be that some patients prefer to be treated at high-quality cancer centers instead of local centers, even if that means traveling long distances and incurring additional economic expenses. This assumption is intercorrelated with other SDHs, including having a more outstanding financial position and job flexibility that allows for such travel.

Our results highlight the need to identify patients who have been economically and socially marginalized and need psychosocial support and to invest in socially targeted interventions as part of a comprehensive cancer-treatment approach. Some progress has been made in designing screening tools to identify patients’ social and economic needs.55 These should be routinely assessed in clinical practice and recorded in patients’ charts. Required actions must focus on multidimensional strategies to provide patients with a social network that assists, encourages, and offers emotional and informative support that positively affects cancer outcomes.56,57 

Our analysis is subject to some limitations that need to be acknowledged. First, there is a lack of individual patient data on most SDHs. Most studies used data routinely collected for other purposes and implemented a census-based approach. Second, there is a risk of data duplication considering those studies that analyzed national or state databases. Third, we identified a lack of data in the literature in terms of transportation, debt, higher education, diet, social integration, environmental factors, or stress. Our search strategy may not identify studies that do not identify variables as SDHs. In addition, how SDHs are measured and reported and what SDHs are reported vary across studies, making assessment of the interconnectivity of various factors challenging. However, the strengths of our review compensate for these limitations. To the best of our knowledge, this is the first systematic review assessing the role of SDHs on the outcomes strictly of patients affected by hematologic malignancies. We also restricted our analysis to only studies conducted in the United States because of the particular context of each country regarding health systems, education, the economy, and social behaviors.

In conclusion, our review highlights the extension of disparities among different populations in health care access and quality, financial burden, and social support. Furthermore, these results indicate the major effect of SDHs on survival outcomes in patients with hematologic malignancies. SDHs are a complex matrix of social, financial, and cultural elements as multiple intercorrelated variables. They represent a potential area of intervention independent of tumor biology or therapeutic efficacy. Strategies to incorporate SDHs into clinical care, research, and public health policies are needed, identifying and addressing social barriers at a patient-based level to enhance cancer equity.

Contributions: J.E.C., M.M.G., and K.C.T. conceptualized the study; J.E.C., E.A.B., and G.A., were responsible for methodology; M.M.G., K.C.T., E.A.B., and G.A. curated the data; M.M.G. and J.E.C. wrote the original draft; J.E.C., E.A.B., and G.A. supervised the study; and M.M.G., K.T., E.A.B., G.A., and J.E.C. wrote and reviewed the manuscript.

Conflicts of interest disclosures: The authors declare no competing financial interests.

Correspondence: Jorge E. Cortes, Georgia Cancer Center, Augusta University, 1410 Laney Walker Blvd, CN2222 Augusta, GA 30912; e-mail: [email protected].

1.
Pulte
D
,
Jansen
L
,
Brenner
H
.
Changes in long term survival after diagnosis with common hematologic malignancies in the early 21st century
.
Blood Cancer J
.
2020
;
10
(
5
):
56
.
2.
Sochacka-Ćwikła
A
,
Mączyński
M
,
Regiec
A
.
FDA-approved drugs for hematological malignancies-the last decade review
.
Cancers (Basel)
.
2021
;
14
(
1
):
87
.
3.
Haloupek
N
.
The landscape of blood cancer research today-and where the field is headed
.
Blood Cancer Discov
.
2020
;
1
(
1
):
1
-
4
.
4.
Smith-Graziani
D
,
Flowers
CR
.
Understanding and addressing disparities in patients with hematologic malignancies: approaches for clinicians
.
Am Soc Clin Oncol Educ Book
.
2021
;
41
(
41
):
351
. 7.
5.
Patel
MI
,
Lopez
AM
,
Blackstock
W
, et al
.
Cancer disparities and health equity: a policy statement from the American Society Of Clinical Oncology
.
J Clin Oncol
.
2020
;
38
(
29
):
3439
-
3448
.
6.
Marmot
M
,
Friel
S
,
Bell
R
,
Houweling
TA
,
Taylor
S
;
Commission on Social Determinants of Health
.
Closing the gap in a generation: health equity through action on the social determinants of health
.
Lancet
.
2008
;
372
(
9650
):
1661
-
1669
.
7.
Lofters
AK
,
Schuler
A
,
Slater
M
, et al
.
Using self-reported data on the social determinants of health in primary care to identify cancer screening disparities: opportunities and challenges
.
BMC Fam Pract
.
2017
;
18
(
1
):
31
.
8.
Zhao
X
,
Zhao
J
,
Yang
Q
, et al
.
Health insurance status and cancer stage at diagnosis and survival in the United States
.
Int J Mol Sci
.
2022
;
24
(
1
):
542
-
560
.
9.
Zhang
L
,
Gong
R
,
Shi
L
, et al
.
Association of residential racial and economic segregation with cancer mortality in the US
.
JAMA Oncol
.
2023
;
9
(
1
):
122
-
126
.
10.
Kroenke
CH
,
Kubzansky
LD
,
Schernhammer
ES
,
Holmes
MD
,
Kawachi
I
.
Social networks, social support, and survival after breast cancer diagnosis
.
J Clin Oncol
.
2006
;
24
(
7
):
1105
-
1111
.
11.
Page
MJ
,
McKenzie
JE
,
Bossuyt
PM
, et al
.
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
.
Bmj
.
2021
;
74
(
9
):
790
-
799
.
12.
Team
TE
. EndNote.
Clarivate
;
2013
.
13.
Ouzzani
M
,
Hammady
H
,
Fedorowicz
Z
,
Elmagarmid
A
.
Rayyan—a web and mobile app for systematic reviews
.
Syst Rev
.
2016
;
5
(
1
):
210
.
14.
Moola
S MZ
,
Tufanaru
C
,
Aromataris
E
, et al
. Chapter 7: Systematic reviews of etiology and risk. JBI Manual for Evidence Synthesis. JBI. In:
Aromataris
E MZE
, eds.
JBI Manual for Evidence Synthesis JBI
.
2020
.
15.
Fiala
MA
,
Finney
JD
,
Liu
J
, et al
.
Socioeconomic status is independently associated with overall survival in patients with multiple myeloma
.
Leuk Lymphoma
.
2015
;
56
(
9
):
2643
-
2649
.
16.
Perry
AM
,
Brunner
AM
,
Zou
T
, et al
.
Association between insurance status at diagnosis and overall survival in chronic myeloid leukemia: A population-based study
.
Cancer
.
2017
;
123
(
13
):
2561
-
2569
.
17.
Dhakal
P
,
Joshi
U
,
Lyden
E
,
Pyakuryal
A
,
Gundabolu
K
,
Bhatt
VR
.
Association of insurance types and outcomes in acute promyelocytic leukemia
.
Leuk Lymphoma
.
2022
;
63
(
11
):
2627
-
2635
.
18.
Chohan
KL
,
Abeykoon
JP
,
Ansell
SM
, et al
.
Insurance-based disparities impact survival outcomes in Waldenström macroglobulinemia within the United States
.
Leuk Lymphoma
.
2022
;
63
(
12
):
2879
-
2888
.
19.
Yung
RL
,
Chen
K
,
Abel
GA
, et al
.
Cancer disparities in the context of Medicaid insurance: a comparison of survival for acute myeloid leukemia and Hodgkin's lymphoma by Medicaid enrollment
.
Oncologist
.
2011
;
16
(
8
):
1082
-
1091
.
20.
Al-Ameri
A
,
Anand
A
,
Abdelfatah
M
, et al
.
Outcome of acute myeloid leukemia and high-risk myelodysplastic syndrome according to health insurance status
.
Clin Lymphoma Myeloma Leuk
.
2014
;
14
(
6
):
509
-
513
.
21.
Goldstein
JS
,
Switchenko
JM
,
Behera
M
,
Flowers
CR
,
Koff
JL
.
Insurance status impacts overall survival in Burkitt lymphoma
.
Leuk Lymphoma
.
2019
;
60
(
13
):
3225
-
3234
.
22.
Dhakal
P
,
Chen
B
,
Giri
S
,
Vose
JM
,
Armitage
JO
,
Bhatt
VR
.
Effects of center type and socioeconomic factors on early mortality and overall survival of diffuse large B-cell lymphoma
.
Future Oncol
.
2019
;
15
(
18
):
2113
-
2124
.
23.
Ghiassi-Nejad
Z
,
Ru
M
,
Moshier
E
,
Chang
S
,
Jagannath
S
,
Dharmarajan
K
.
Overall survival trends and clinical characteristics of plasmacytoma in the United States: a national cancer database analysis
.
Clin Lymphoma Myeloma Leuk
.
2019
;
19
(
5
):
310
-
319
.
24.
Ho
G
,
Jonas
BA
,
Li
Q
,
Brunson
A
,
Wun
T
,
Keegan
THM
.
Early mortality and complications in hospitalized adult Californians with acute myeloid leukaemia
.
Br J Haematol
.
2017
;
177
(
5
):
791
-
799
.
25.
Tao
L
,
Foran
JM
,
Clarke
CA
,
Gomez
SL
,
Keegan
TH
.
Socioeconomic disparities in mortality after diffuse large B-cell lymphoma in the modern treatment era
.
Blood
.
2014
;
123
(
23
):
3553
-
3562
.
26.
Ai
WZ
,
Chang
ET
,
Fish
K
,
Fu
K
,
Weisenburger
DD
,
Keegan
TH
.
Racial patterns of extranodal natural killer/T-cell lymphoma, nasal type, in California: a population-based study
.
Br J Haematol
.
2012
;
156
(
5
):
626
-
632
.
27.
Ailawadhi
S
,
Advani
P
,
Yang
D
, et al
.
Impact of access to NCI- and NCCN-designated cancer centers on outcomes for multiple myeloma patients: a SEER registry analysis
.
Cancer
.
2016
;
122
(
4
):
618
-
625
.
28.
Master
S
,
Munker
R
,
Shi
Z
,
Mills
G
,
Shi
R
.
Insurance status and other non-biological factors predict outcomes in acute myelogenous leukemia: analysis of data from the National Cancer Database
.
Anticancer Res
.
2016
;
36
(
9
):
4915
-
4921
.
29.
Shanafelt
TD
,
Kay
NE
,
Rabe
KG
, et al
.
Hematologist/oncologist disease-specific expertise and survival: lessons from chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL)
.
Cancer
.
2012
;
118
(
7
):
1827
-
1837
.
30.
Loberiza
FR
,
Cannon
AJ
,
Weisenburger
DD
, et al
.
Survival disparities in patients with lymphoma according to place of residence and treatment provider: a population-based study
.
J Clin Oncol
.
2009
;
27
(
32
):
5376
-
5382
.
31.
Huang
C
,
Liu
H
,
Jia
L
,
Lu
M
,
Hu
S
.
Survival disparities in multiple myeloma by health insurance status among US non-elderly adults: a SEER-based comparative analysis
.
Acta Haematol
.
2021
;
144
(
5
):
542
-
550
.
32.
Freeman
AT
,
Meyer
AM
,
Smitherman
AB
, et al
.
Statewide geographic variation in outcomes for adults with acute myeloid leukemia in North Carolina
.
Cancer
.
2016
;
122
(
19
):
3041
-
3050
.
33.
Rodriguez
CP
,
Baz
R
,
Jawde
RA
, et al
.
Impact of socioeconomic status and distance from treatment center on survival in patients receiving remission induction therapy for newly diagnosed acute myeloid leukemia
.
Leuk Res
.
2008
;
32
(
3
):
413
-
420
.
34.
Bradley
CJ
,
Dahman
B
,
Jin
Y
,
Shickle
LM
,
Ginder
GD
.
Acute myeloid leukemia: how the uninsured fare
.
Cancer
.
2011
;
117
(
20
):
4772
-
4778
.
35.
Gunaratne
M
,
Sahakian
AJ
,
Abeykoon
JP
, et al
.
Treatment facility volume and patient outcomes in Waldenstrom macroglobulinemia
.
Leuk Lymphoma
.
2021
;
62
(
2
):
308
-
315
.
36.
Byrne
MM
,
Halman
LJ
,
Koniaris
LG
,
Cassileth
PA
,
Rosenblatt
JD
,
Cheung
MC
.
Effects of poverty and race on outcomes in acute myeloid leukemia
.
Am J Clin Oncol
.
2011
;
34
(
3
):
297
-
304
.
37.
Costa
LJ
,
Brill
IK
,
Brown
EE
.
Impact of marital status, insurance status, income, and race/ethnicity on the survival of younger patients diagnosed with multiple myeloma in the United States
.
Cancer
.
2016
;
122
(
20
):
3183
-
3190
.
38.
Borate
UM
,
Mineishi
S
,
Costa
LJ
.
Nonbiological factors affecting survival in younger patients with acute myeloid leukemia
.
Cancer
.
2015
;
121
(
21
):
3877
-
3884
.
39.
Parikh
RR
,
Grossbard
ML
,
Green
BL
,
Harrison
LB
,
Yahalom
J
.
Disparities in survival by insurance status in patients with Hodgkin lymphoma
.
Cancer
.
2015
;
121
(
19
):
3515
-
3524
.
40.
Evans
LA
,
Go
R
,
Warsame
R
, et al
.
The impact of socioeconomic risk factors on the survival outcomes of patients with newly diagnosed multiple myeloma: a cross-analysis of a population-based registry and a tertiary care center
.
Clin Lymphoma Myeloma Leuk
.
2021
;
21
(
7
):
451
-
460.e2
.
41.
Harlan
LC
,
Greene
AL
,
Clegg
LX
,
Mooney
M
,
Stevens
JL
,
Brown
ML
.
Insurance status and the use of guideline therapy in the treatment of selected cancers
.
J Clin Oncol
.
2005
;
23
(
36
):
9079
-
9088
.
42.
Yabroff
KR
,
Reeder-Hayes
K
,
Zhao
J
, et al
.
Health insurance coverage disruptions and cancer care and outcomes: systematic review of published research
.
J Natl Cancer Inst
.
2020
;
112
(
7
):
671
-
687
.
43.
Roessler
M
,
Schmitt
J
,
Bobeth
C
, et al
.
Is treatment in certified cancer centers related to better survival in patients with pancreatic cancer? Evidence from a large German cohort study
.
BMC Cancer
.
2022
;
22
(
1
):
621
.
44.
Moslim
MA
,
Minarich
MJ
,
Deng
M
, et al
.
Treatment at an academic cancer center confers better survival by stage for signet-ring cell and non-signet-ring cell gastric cancer
.
Ann Surg Oncol
.
2021
;
28
(
8
):
4423
-
4432
.
45.
Abel
GA
,
Kim
HT
,
Zackon
I
, et al
.
Non-relapse mortality and quality of life with shared care after allogeneic hematopoietic cell transplantation: a randomized control trial
.
Blood
.
2022
;
140
(
Supplement 1
):
2135
-
2136
.
46.
Fein
JA
,
McAuliffe
A
,
Fischer
K
, et al
.
Impact of a shared-care model between community and academic centers for facilitating access to allogeneic and autologous stem cell transplantation
.
J Clin Oncol
.
2022
;
40
(
16_suppl
):
1510
.
47.
Aizer
AA
,
Chen
M-H
,
McCarthy
EP
, et al
.
Marital status and survival in patients with cancer
.
J Clin Oncol
.
2013
;
31
(
31
):
3869
-
3876
.
48.
Vaingankar
JA
,
Abdin
E
,
Chong
SA
, et al
.
The association of mental disorders with perceived social support, and the role of marital status: results from a national cross-sectional survey
.
Arch Publ Health
.
2020
;
78
(
1
):
108
.
49.
Pandey
KR
,
Yang
F
,
Cagney
KA
,
Smieliauskas
F
,
Meltzer
DO
,
Ruhnke
GW
.
The impact of marital status on health care utilization among Medicare beneficiaries
.
Medicine
.
2019
;
98
(
12
):
e14871
.
50.
Davis
RE
,
Trickey
AW
,
Abrahamse
P
,
Kato
I
,
Ward
K
,
Morris
AM
.
Association of cumulative social risk and social support with receipt of chemotherapy among patients with advanced colorectal cancer
.
JAMA Netw Open
.
2021
;
4
(
6
):
e2113533
.
51.
Woods
LM
,
Rachet
B
,
Coleman
MP
.
Origins of socio-economic inequalities in cancer survival: a review
.
Ann Oncol
.
2006
;
17
:
5
-
19
.
52.
Albano
JD
,
Ward
E
,
Jemal
A
, et al
.
Cancer mortality in the United States by education level and race
.
J Natl Cancer Inst
.
2007
;
99
(
18
):
1384
-
1394
.
53.
Pratap
P
,
Dickson
A
,
Love
M
, et al
.
Public health impacts of underemployment and unemployment in the united states: exploring perceptions, gaps and opportunities
.
Int J Environ Res Public Health
.
2021
;
18
(
19
):
10021
.
54.
Zhao
G
,
Okoro
CA
,
Li
J
,
Town
M
.
Health insurance status and clinical cancer screenings among U.S. adults
.
Am J Prev Med
.
2018
;
54
(
1
):
e11
-
e19
.
55.
Bloch
G
,
Rozmovits
L
.
Implementing social interventions in primary care
.
CMAJ (Can Med Assoc J)
.
2021
;
193
(
44
):
E1696
-
e1701
.
56.
Pasek
M
,
Suchocka
L
,
Gąsior
K
.
Model of social support for patients treated for cancer
.
Cancers (Basel)
.
2021
;
13
(
19
):
4786
.
57.
Lee
I
,
Park
C
.
The mediating effect of social support on uncertainty in illness and quality of life of female cancer survivors: a cross-sectional study
.
Health Qual Life Outcomes
.
2020
;
18
(
1
):
143
.

Author notes

Data that support the findings of this study are available on request from the corresponding author, Jorge E. Cortes ([email protected]).

The full-text version of this article contains a data supplement.

Supplemental data