(2024) 22:343
Huang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03563-0

BMC Medicine

RESEARCH

Open Access

Association between social determinantsof health and survival among the US cancer
survivors population
Hongbo Huang1, Tingting Wei1, Ying Huang1, Aijie Zhang2, Heng Zhang1, Ze Zhang1, Yijing Xu1, Haonan Pan3,
Lingquan Kong1, Yunhai Li1* and Fan Li1*

Abstract
Background Racial and ethnic disparities in mortality persist among US cancer survivors, with social determinantsof health (SDoH) may have a significant impact on these disparities.
Methods A population-based cohort study of a nationally representative sample of adult cancer survivors, whoparticipated in the US National Health and Nutrition Examination Survey from 1999 to 2018 was included. Sociodemographic characteristics and SDoH were self-reported using standardized questionnaires in each survey cycle.
The SDoH was examined by race and estimated for associations with primary outcomes, which included all-cause
and cancer-specific mortality. Multiple mediation analysis was performed to assess the contribution of each unfavorable SDoH to racial disparities to all-cause and cancer-specific mortality.
Results Among 5163 cancer survivors (2724 [57.7%] females and 3580 [69.3%] non-Hispanic White individuals),
only 881 (24.9%) did not report an unfavorable SDoH. During the follow-up period of up to 249 months (median
81 months), 1964 deaths were recorded (cancer, 624; cardiovascular, 529; other causes, 811). Disparities in all-cause
and cancer-specific mortality were observed between non-Hispanic Black and White cancer survivors. Unemployment, lower economic status, education less than high school, government or no private insurance, renting a homeor other arrangements, and social isolation were significantly and independently associated with worse overall
survival. Unemployment, lower economic status, and social isolation were significantly associated with cancer-specificmortality. Compared to patients without an unfavorable SDoH, the risk of all-cause mortality was gradually increasedin those with a cumulative number of unfavorable SDoHs (1 unfavorable SDoH: hazard ratio [HR] = 1.54, 95% CI
1.25–1.89; 2 unfavorable SDoHs: HR = 1.81, 95% CI 1.46–2.24; 3 unfavorable SDoHs: HR = 2.42, 95% CI 1.97–2.97; 4 unfavorable SDoHs: HR = 3.22, 95% CI 2.48–4.19; 5 unfavorable SDoHs: HR = 3.99, 95% CI 2.99–5.33; 6 unfavorable SDoHs:
HR = 6.34 95% CI 4.51–8.90). A similar trend existed for cancer-specific mortality.
Conclusions In this cohort study of a nationally representative sample of US cancer survivors, a greater numberof unfavorable SDoH was associated with increased risks of mortality from all causes and cancer. Unfavorable SDoH
levels were critical risk factors for all-cause and cancer-specific mortality, as well as the underlying cause of racial allcause mortality disparities among US cancer survivors.
*Correspondence:
Yunhai Li
leeyh90@hospital.cqmu.edu.cn
Fan Lilf628@163.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Huang et al. BMC Medicine(2024) 22:343

Page 2 of 15

Keywords Health inequities, Racial disparities, Cancer survivors, Mortality, Social determinants of health, Policy

BackgroundAn increase in the cancer survivor population poses a significant health care and economic burden worldwide, andcancer is the second leading cause of death in the UnitedStates (US). Specifically, there will be approximately
2,001,140 new cancer cases and an estimated 611,720
deaths from cancer in the US in 2024 [1]. Although cancer mortality has declined overall by 33% since 1991,
improved survival outcomes have not benefitted equallyfor all cancer populations [1, 2]. Substantial racial andethnic disparities in all-cause and cancer-related mortality rates persist in US cancer survivors [1–4]. For
example, Black individuals have lower relative cancer
survival rates than White individuals for almost every
cancer type [1, 5]. Interestingly, the most striking gaps insurvival involve cancers that are most amenable to prevention and early detection, such as cervical cancer [5].
Recently, the racial and ethnic disparities in cancer mortality have slowly narrowed; however, these disparities incancer health have become increasingly understood in
the context of social determinants of health (SDoH) [2,5, 6], which are responsible for an extremely importantfactor associated with cancer risk and treatment [7]. The
World Health Organization (WHO) defined SDoH as
non-medical factors that affect health outcomes, including the conditions in which people are born, grow, live,
work, and age, and a wider set of forces and systems
shaping daily life conditions [8]. The SDoH included factors related to economic stability, education, health careaccess, residential environment, and social context andsupport [9–11], associated with the health outcomes ofcancer survivors [12, 13]. Addressing social disparities incancer health is essential in the quest to improve survival
outcomes among cancer survivors, which reflects a commitment to health equity to achieve optimal health foreveryone.
Previous studies have tended to examine the contribution of individual variables involving unfavorable SDoHs in the separate associations with mortality
or morbidity, most of which focused on the direct
and indirect influence of socioeconomic factors on
the disparity in survival [9, 14–23]. Among the general population, a large multicohort study and metaanalysis with more than 1.7 million individuals from
7 WHO member countries reported that low socioeconomic status was associated with a 46% (95% CI,
39–53%) and 43% (95% CI, 34–52%) greater risk of
all-cause and cancer mortality, respectively, compared to high socioeconomic status [15]. A low levelof education, poverty, and a lack of health insurance
coverage explain in part the continuous widening inmortality inequities across some adult sociodemographic groups in the US [14, 17, 20, 24]. Additionally,a recent analysis demonstrated that the cumulative
SDoH count was associated with an increased premature mortality risk [25]. However, limited evidence has
been reported on the effect of SDoH in cancer survivors. Although previous cohort studies have shownthat disadvantaged SDoH are associated with poor
mental and physical health [26], resulting in a delay inmedical and surgical treatment [27], and an increased
risk of all-cause and cancer-related mortality among
patients with cancers (such as breast and pancreaticcancer) [12, 13]. To the best of our knowledge, few
studies have examined the impact of the comprehensive and accumulating burden of SDoH on all-cause
and cause-specific mortality, using methods published previously [13, 25]. There is no study that hasreported the relative contributions of these SDoH on
racial disparities in the all-cause and cancer-specificmortality rates among the US cancer survivors at the
population level.
The objective of the present study was to evaluate therelationships of multiple SDoH with all-cause, cancerspecific, and non-cancer mortality, and to investigate
how SDoH mediates racial differences in all-cause andcancer-specific mortality among cancer survivors. We
hypothesized that disparities exist in the cumulative
number of unfavorable SDoH across racial and ethnic
groups and that a higher number of these unfavorable
SDoH is associated with higher mortality rates.

Methods
Study population

In this retrospective study, 10 cycles of cross-sectional
data were collected from the National Health andNutrition Examination Survey (NHANES) database,which used a complex, multistage, and probability
sampling design to recruit participants representativeof the civilian non-institutionalized US population[28]. Each participant was invited to attend an inperson or in-home interview to complete the questionnaire. The present study examined and analyzed
existing data involving sociodemographic characteristics and several SDoH co-variables among cancer survivors of 20 years or older with information linked to
the National Death Index through 31 December 2019

Huang et al. BMC Medicine(2024) 22:343

for 10 survey cycles of NHANES from 1999–2000 to
2017–2018. All the NHANES protocols were approvedby the Research Ethics Review Board of the National
Center for Health Statistics (NCHS), and written
informed consent was provided by all participants atthe time of recruitment.
Sociodemographic characteristics

In each 2-year survey, age, gender, and racial or ethnic
groups (non-Hispanic White [NHW], Hispanic, nonHispanic Black [NHB], and other [American Indian/
Alaska Native/Pacific Islander, Asian, and multiracial])
were obtained from standardized questionnaires of inhome interviews by self or parent/guardian report fromprovided categories.
SDoH assessment

We included several variables that reflected SDoH information from standardized questionnaires, which were
defined according to the Healthy People 2030 [11] andWorld Health Organization [29] by the following factors:
economic stability; education access and quality; healthaccess and quality; neighborhood and built environment;
and social and community context. In the present study,
we finally chose eight SDoH variables (employment status, family poverty income ratio, food security, educationlevel, regular health care access, type of health insurance,home ownership, and marital status) in each NHANES
cycles from 1999 to 2018, according to previously published studies [25, 30]. Social support was excluded
because it was only visible in surveys conducted between
1999 and 2008. More detailed description information on
SDoH was provided in the supplement (Additional file 1:
Table S1) [31–33], and the definition for unfavorable
SDoH was based on the conventional cutoff points [10,11, 23, 34, 35]. Furthermore, the associations between
several single SDoH measures and all-cause mortality were investigated using various categorizations withadjustment for age, gender, race, and ethnicity regardless of survey weights (Additional file 1: Table S2). Each
SDoH was divided into two levels based on the conventional cut-off points [11, 23, 34, 35]. Unfavorable SDoH
was significantly associated with a lower survival rate.
During the in-person interview, participants were asked
to respond to several questions about these SDoH. Economic stability was operationalized using self-reported
measures of the family poverty income ratio (PIR, lessthan 2.4 [unfavorable SDoH] and more than 2.4 [favorable SDoH]), employment status (employed, student, or
retired [favorable SDoH] and unemployed [unfavorable
SDoH]) and household food security category, which
was dichotomized as fully food security (no affirmative

Page 3 of 15

response) or marginal, low, or very low security (1–10
affirmative responses) based on the responses to the US
Food Security Survey Module questions (Bickel et al.
[36]). Education access and quality measurement used
the highest grade or level of schooling completed or thehighest degree received, dichotomized as less than highschool (unfavorable SDoH) and high school graduate or
higher (favorable SDoH). Health care access and quality were assessed by self-reported questionnaire about
routine places for health care (at least one regular healthcare facility [favorable SDoH] and none or hospital emergency room [unfavorable SDoH]) and health insurance
type (private [favorable SDoH] and none or government[unfavorable SDoH]). The residential environment wasassessed by home ownership (owned or being bought[favorable SDoH] and rental or other arrangement [unfavorable SDoH]). Social community context was assessedby self-reported marital status (defined as married or living with a partner [favorable SDoH] and not married norliving with a partner [unfavorable SDoH]).
The cumulative number of unfavorable SDoH variables
with a range from 0 (no unfavorable SDoH) to 6 or more(≥ 6 unfavorable SDoH) was calculated to explore thecumulative effect of unfavorable SDoH on all-cause andcancer-specific mortality. Because only a small proportion of participants reported having 6, 7, or 8 unfavorable
SDoH variables simultaneously, thus we created a category of six or more, indicating the combination of 6, 7, or
8 unfavorable SDoH variables.
Definition of cancer survivors

Information on cancer diagnosis was collected from survey questionnaires during the in-person interview using
the computer-assisted personal interview system, including cancer type(s), with up to three cancer diagnoses
recorded and the age at first diagnosis for each cancer.
Participants were asked, “Have you ever been told by adoctor or other health professional that you had cancer ora malignancy of any kind?” If individuals who answered“yes” were defined as cancer survivors and were asked
further, “What kind of cancer was it?” and “How old were
you when this cancer was first diagnosed?”.
Ascertainment of mortality

The NCHS provided mortality data that were linked
to the National Death Index, with follow-up until 31
December 2019 [37]. Cause-of-death coding for all US
deaths occurring after 1998 followed the 10th revisionof the International Statistical Classification of Diseases, Injuries, and Causes of Death (ICD-10) guidelines. Cancer-related mortality was classified as death
due to malignant neoplasms (ICD-10, codes C00-C97).
The follow-up duration was defined as the intervalHuang et al. BMC Medicine(2024) 22:343

Page 4 of 15

elapsing from the date of the baseline interview to thedate of death or the follow-up cut-off (31 December2019) for those participants who did not have a deathevent in the whole study. We investigated the association
between SDoH and all-cause, cancer-related, and noncancer mortality (mortality instead of cancer, ICD-10
codes instead of C00-97). All-cause and cancer-specificmortality were the main outcomes of this study. The
all-cause mortality was measured from the date of thebaseline interview to the date of death from any cause or
the follow-up cut-off. The cancer-specific mortality wascalculated from the date of the baseline interview to thedate of death from cancer.

was considered statistically significant. Data analyses
were performed from 1 June to 1 August 2023.
Because of racial disparity in all-cause and cancerspecific mortality between NHW and NHB among cancer survivors in the US, therefore mediation analysis was
performed to explore whether SDoH factors contributed
to White-Black disparity in mortality or not. We estimated the relative effect (corresponding direct or indirect
effect divided by the total effect) of each SDoH variable
to explain the racial and ethnic difference in mortality
using R package mma [31–33]. More detailed information was contained in the Supplementary material (Additional file 1: Methods S1).

Statistical analysis

Results
NHANES (1999–2018) data from 5163 individuals wereenrolled in the final analysis (Additional file 1: Fig. S1). A
total of 101,316 persons ≥ 1 year of age who participatedin the in-person or in-home interview and 96,153 were
excluded, as follows: (1) 46 235 participants < 20 years ofage; (2) 49,915 whom were not diagnosed with cancer,and (3) 3 individuals who did not have unique identifiers
to allow linkage to the National Death Index. Of the 5163
cancer survivors (weighted population, 32,623 176; 57.7%
female) in this study cohort, 3580 (69.3%) were NHW,
631 (12.2%) were Hispanic, 718 (13.9%) were NHB, and234 (4.5%) individuals of were classified as race and ethnicity, including American Indian/native Alaskan, PacificIslander, Asian, and multiracial (Table 1). Compared to
NHW, Hispanic, NHB and other race and ethnic cancer
survivors were more likely to have unfavorable SDoH factors, including not being married nor living with a partner, education less than high school, a PIR < 2.4, renting
a home or other arrangement, unemployment, government or none health insurance, and marginal, low, orvery low security. However, a lower proportion of NHB
participants had no place routine place when sick or inneed of advice about healthcare compared with cancer
survivors from all other racial and ethnic subgroups.
Approximately 24.9% of cancer survivors did not havea cumulative number of unfavorable SDoH. The higher
proportion of NHW cancer survivors with 0 and 1
cumulative unfavorable SDoH was observed comparedto patients from all other race and ethnic subgroups. In
addition, a higher proportion of Hispanic cancer survivors with 3, 4, 5, and 6 or more unfavorable SDoH wasobserved compared to patients from all other race andethnic subgroups. NHB and Hispanic individuals had ahigher prevalence of multiple unfavorable SDoH (cumulative of 3 or more) compared to NHW cancer survivors.
Then, we analyzed the relationship between the eight
SDoH variables. The results showed that all eight SDoH
variables were significantly correlated with each otherAll statistical analyses were conducted with the use of
R (version 4.3.1) following the NHANES analysis guidance. The survey interview weights were used for analysis
as appropriate to obtain nationally representative estimates. We calculated weighted sample sizes to be nationally representative and population-weighted percentages
according to race and ethnicity. The chi-square test wasused to determine the differences in participants’ sociodemographic characteristics and SDoH variables acrossfour classifications of racial and ethnic groups. The pairwise correlation among the eight dichotomous SDoH
was evaluated using the Spearman method. The weighted
proportions of cancer survivors in each number of unfavorable SDoH category were estimated by gender, race,and ethnicity. Kaplan–Meier survival curves were examined to determine the all-cause cumulative mortality andcancer-specific cumulative mortality rates among cancer survivors stratified by SDoH. Furthermore, Kaplan–
Meier analysis was used to plot the cumulative hazard
for all-cause and cancer-specific mortality in entire andgender subgroups and race and ethnicity subgroups using
age as the timescale [38].
Multivariable Cox proportional hazards regression
models with the use of imputation-adjusted survey
weights were applied to estimate the mortality risks (hazard ratio [HR]) and 95% confidence interval (CI) for the
associations between cumulative SDoH variables andrace with all-cause, cancer-specific, and non-cancer mortality. Final stage multivariable Cox models were adjusted
for age, gender, race, and ethnicity, and additionally
included the other SDoHs to identify independent, indirect associations. We plotted the HRs of the cumulative
SDoH variables to visualize whether the relationship withall-cause and cancer-specific mortality was linear or nonlinear. Sensitivity analyses were performed by excluding participants of deaths that occurred within the first2-year follow-up to lessen the probability of reverse causation [39]. All statistical tests were 2-sided and P < 0.05

Huang et al. BMC Medicine(2024) 22:343

Page 5 of 15

Table 1 Sample ­sizea and characteristics for US cancer survivors age 20 years and older, NHANES 1999 to 2018
No. of participants by race and ethnicity (weighted %)Characteristic

All

NHW

Hispanic

NHB

OtherbOverall(N = 5163)(N = 3580)(N = 631)(N = 718)(N = 234)

341 (8.5)215 (7.8)

67 (18.9)45 (10.8)

14 (7.1)

Age, years
20–39< 0.001

40–591033 (29.9)652 (29.0)

159 (38.2)

147 (32.1)

75 (36.4)

60–792578 (47.1)

1658 (47.5)352 (37.7)

450 (48.0)

118 (48.9)  ≥ 801211 (14.5)

1055 (15.7)

53 (5.3)

76 (9.1)

27 (7.6)

Male2439 (42.3)

1758 (43.3)

213 (28.6)

373 (42.6)

95 (37.5)

Female2724 (57.7)

1822 (56.7)

418 (71.4)

345 (57.4)

139 (62.5)

Married or living with a partner

3049 (64.8)2201 (66.4)

372 (60.4)

333 (43.7)

143 (63.6)

Not married nor living with a partner

2076 (34.5)

1355 (33.0)

253 (37.8)

379 (55.2)

89 (36.0)

High school graduate or higher

3920 (85.0)2917 (87.4)

320 (57.8)

491 (72.0)

192 (85.7)

Less than high school

1234 (14.9)

660 (12.6)

308 (41.8)

225 (27.8)

41 (14.1)  < 2.42323 (33.7)

1491 (31.1)

375 (56.7)

357 (49.6)

100 (38.7)  ≥ 2.42345 (57.6)

1787 (60.6)

176 (30.3)

283 (39.2)

99 (49.6)

Owned or being bought

3931 (79.9)

2888 (82.6)

404 (59.2)

475 (62.9)

164 (70.0)

Rent home or other arrangement

1146 (18.2)

643 (15.5)

209 (37.8)

234 (36.0)

60 (27.7)

Employed, student, or retired

4096 (81.4)

2998 (83.4)

411 (67.2)

527 (70.4)

160 (69.1)

Unemployment

1061 (18.5)

578 (16.5)

220 (32.8)

191 (29.6)

72 (30.4)

At least one regular health care facility

4778 (92.4)

3338 (92.7)

562 (88.0)

671 (93.3)

207 (89.9)

None or Hospital emergency room

384 (7.6)

241 (7.3)

69 (12.0)

47 (6.7)

27 (10.1)

Sex< 0.001

Marital status< 0.001

Educational attainment< 0.001

Family poverty income ratio< 0.001

Home ownership< 0.001

Employment status< 0.001

Access to routine place for health care

0.002

Health insurance< 0.001

Private2939 (65.0)2243 (67.9)

228 (40.8)

357 (50.7)

111 (51.6)

Government or none

2188 (34.4)

1318 (31.7)

396 (58.3)

353 (48.3)

121 (47.3)

Fully food security4058 (83.1)

3017 (86.0)

368 (55.0)

507 (67.8)

166 (74.2)

Marginal, low, or very low security

972 (14.3)

480 (11.4)

238 (41.0)

197 (30.4)

57 (23.1)

881 (24.9)

734 (27.2)

43 (8.2)

71 (9.1)

33 (15.2)

Food security< 0.001

Cumulative number of unfavorable SDoH ­variabled0

P ­valuec< 0.00111049 (23.9)

832 (25.3)

66 (9.8)

119 (16.8)

32 (19.9)2817 (14.2)

592 (14.3)

80 (12.3)

105 (13.1)

40 (16.7)

3630 (10.2)

440 (9.8)

71 (14.5)

95 (12.5)

24 (10.3)

4553 (8.0)

304 (6.9)

115 (17.6)

101 (14.5)

33 (11.0)

5369 (5.2)

182 (4.1)

84 (12.9)

84 (13.0)

19 (6.9)

6 or more

273 (3.2)

129 (2.5)

78 (9.8)

51 (7.4)

15 (6.5)

Abbreviations: NHANES National health and nutrition examination survey, NHW non-hispanic white, NHB non-hispanic blacka

Weighted to be nationally representative. Weighted percentage may not sum to 100% because of missing datab

Including American Indian/Alaska Native/Pacific Islander, Asian, and multiracialc

P value is for the comparison of age group, sex, and each SDoH variable’s prevalence by racial and ethnic groupd
Cumulative number of unfavorable SDoH ranging from 0 (no risk at all) to 6 or more (highest risk), indicating that individuals with SDoH risk factors are more sociallydisadvantaged

Huang et al. BMC Medicine(2024) 22:343(Additional file 1: Fig. S2). Furthermore, the proportion ofmale participants decreased stepwise from 34.8% (0 unfavorable SDoH) to 2.2% (6 or more number of unfavorable SDoH), whereas the proportion of female participants
increased from 22.6% (0 unfavorable SDoH) to 24.8% (1unfavorable SDoH), and then gradually decreased to 4.7%
(6 or more number of unfavorable SDoH; Additional
file 1: Fig. S3). Breast and prostate cancer were the mostcommon malignant neoplasm type in males and females,
respectively (Additional file 1: Table S3).
During the median follow-up of 81 months (ranged
0–249 months) in the 10 NHANES cycles linked mortality file cohort, a total of 1964 deaths occurred (all-cause),
including 624 cancer patients who died from cancer (cancer-related mortality), 529 who died from cardiovasculardisease, and 811 who died from other cause. Comparedto participants who were NHW, NHB adults with cancer
had a significantly higher overall mortality rate (HR, 1.57;
95% CI, 1.34–1.89) and cancer-specific mortality (HR,2.03; 95% CI, 1.60–2.59; Fig. 1). Cancer survivors witheach unfavorable SDoH variable, except access to regular
health care, was significantly associated with higher allcause, cancer-specific, and non-cancer mortality in themultivariable model adjusted for age (MV model 1), andadjusted for age, gender, race and ethnicity (MV model 2;
Table 2). After adjustment for age, gender, race and ethnicity and other SDoHs, including unemployment status(HR, 1.83; 95% CI, 1.58–2.12; P < 0.001), family incometo-poverty less than 2.4 (HR, 1.51; 95% CI, 1.32–1.72;
P < 0.001), education less than high school attached

Page 6 of 15(HR, 1.23; 95% CI, 1.05–1.44; P = 0.012), government or
none of health insurance (HR, 1.19; 95% CI, 1.05–1.36;
P = 0.007), renting a home or other housing arrangement
environment (HR, 1.39; 95% CI, 1.20–1.62; P < 0.001), andnot being married nor living with a partner (HR, 1.22;
95% CI, 1.08–1.38; P < 0.001) were significantly associated with an increased risk for all-cause mortality, which
was similar to non-cancer mortality (Table 2). Furthermore, unemployed individuals (HR, 2.13; 95% CI, 1.62–
2.79; P < 0.001), family income-to-poverty less than 2.4(HR, 1.35; 95% CI, 1.09–1.66; P = 0.006), and not being
married nor living with a partner (HR, 1.22; 95% CI,
1.08–1.38; P < 0.001) were significantly associated withan increased cancer-specific mortality risk compared tothose with favorable SDoH (Table 2). Specifically, individuals of being unemployed status were associated withalmost more than 1.9- and 2.2-fold higher all-cause mortality and cancer-specific mortality rates, respectively.
Cancer survivors with a greater cumulative number of SDoHs were significantly associated with anincreased risk of death from all-cause and cancer(Additional file 1: Fig. S4; P < 0.001). In the multivariable of MV model 1 (adjusted for age, gender, race,and ethnic), the HRs for all-cause and cancer-specificmortality were 1.54 (95% CI, 1.25–1.89) and 1.52 (95%CI, 1.04–2.22) for cancer survivors with 1 unfavorable SDoH, 1.81 (95% CI, 1.46–2.24) and 1.70 (95%CI, 1.20–2.24) for those with 2 unfavorable SDoHs,
2.42 (95% CI, 1.97–2.97) and 2.22 (95% CI, 1.51–3.26)
for those with 3 unfavorable SDoHs, 3.22 (95% CI,

Fig. 1 All-cause mortality (A), cancer-specific mortality (B), and hazard ratios in US adults diagnosed with cancers aged 20 years or older by raceand ethnicity. Note: Kaplan–Meier curves showed cumulative mortality probability race and ethnicity using age as the timescale. The number
at risk was unweighted observed frequencies. Cumulative mortality rates were estimated with the use of survey weights. The bar chart showed HRsof all-cause and cancer-specific mortality associated with race and ethnicity, adjusted for age, and gender. Error bars were 95% CIs. NHW indicatednon-Hispanic White; NHB indicated non-Hispanic Black; HR indicated hazard ratio; ns was the abbreviation of no significance; *** meant p < 0.001, **meant p < 0.01, and * meant p < 0.05

Huang et al. BMC Medicine(2024) 22:343

Page 7 of 15

Table 2 Association of SDoH With All-Cause and Cancer Mortality Among US Cancer Survivors Age 20 Years or Older, NHANES, 1999
to 2018
Hazard ratio (95% CI)
Age ­adjusteda

Mortality outcome

Death/No.

Weighted death ( %[95%CI])All cause

1964/5163

5 653 048 (100%)

1580/4096

4 486 752 (26.6% [25.0-28.3])

1 [Reference]

382/1061

1 163 126 (30.4% [27.2-33.6])

1.97 (1.72-2.26)

P value

MV model 1­ a,b,cP value

MV model 2­ a,b,c,d

P value

Employment status
Employed, student,or retired
Unemployed

1 [Reference]<0.001

2.17 (1.89-2.51)<0.001

1.88 (1.66-2.12)<0.0011.65 (1.39-1.95)

1 [Reference]<0.001

1.83 (1.58-2.12)<0.0011.51 (1.32-1.72)<0.0011.03 (0.84-1.25)<0.001

Family income-to-poverty ratio>=2.4

745/2345

2 421 629 (20.3% [18.6-22.1])

1 [Reference]<2.41027/2323

2 722 447 (39.1% [36.5-41.7])

1.77 (1.58-1.99)

1 [Reference]

1 [Reference]<0.001

Food security
Full security

1646/4058

4 853 469 (28.2% [26.5-30.0])

1 [Reference]

267/972

660 807 (22.3% [19.0-25.5])

1.57 (1.32-1.87)

High school graduate
or higher

1317/3920

4 177 555 (23.7% [22.2-25.3])

1 [Reference]Less than high school

644/1234

1 470 342 (47.7% [43.3-52.1])

1.62 (1.42-1.85)

1852/4778

5 347 052 (28.0% [26.4-29.5])

1 [Reference]

112/384

305 995 (19.4% [15.3-23.6])

1.07 (0.80-1.42)

Marginal, low, or very low
security

1 [Reference]

1 [Reference]
0.786

Education level
1 [Reference]<0.0011.62 (1.41-1.88)

1 [Reference]<0.001

1.23 (1.05-1.44)

0.012

Regular health-care access
At least one regular
health-care facility
None or emergency room

1 [Reference]
0.652

1.05 (0.80-1.39)<0.0011.47 (1.31-1.66)

1 [Reference]
0.725

1.01 (0.75-1.35)<0.001

1.19 (1.05-1.36)

0.956

Type of health insurance
Private1052/2939

3 156 032 (23.5% [21.7-25.2])

1 [Reference]

Government or none

895/2188

2 458 097 (34.5% [32.2-36.8])

1.47 (1.30-1.66)

1 [Reference]

1 [Reference]Own home or beingbrought

1510/3931

4 476 806 (27.1% [25.4-28.7])

1 [Reference]

Rent home or otherarrangement435/1146

1 128 558 (30.1% [26.5-33.6])

1.65 (1.45-1.86)

Married or livingwith a partner

1047/3049

3 123 525 (23.3% [21.5-25.1])

1 [Reference]

Not married nor livingwith a partner

893/2076

2 453 699 (34.3% [32.0-36.6])

1.37 (1.23-1.53)<0.0011.53 (1.37-1.72)<0.0011.22 (1.08-1.38)

Per 1 unfavorable SDoH
increased

NANA

1.44 (1.31-1.57)<0.0011.64 (1.50-1.78)<0.001

NA

Cancer-specific624/5163

1 863 858 (100%)

476/4096

1 394 041 (8.3% [7.3-9.2])

1 [Reference]

148/1061

469 817 (12.3% [10.1-14.4])

2.13 (1.69-2.67)

0.007

Home ownership
1 [Reference]<0.0011.73 (1.52-1.97)

1 [Reference]<0.001

1.39 (1.20-1.62)<0.001

Marital status
1 [Reference]

1 [Reference]
0.001

Employment status
Employed, student,or retired
Unemployed

1 [Reference]<0.0012.36 (1.85-3.03)<0.001

1.66 (1.40-1.99)

0.003

1.50 (1.15-1.97)

1 [Reference]<0.0012.13 (1.62-2.79)<0.0011.35 (1.09-1.66)

0.003

1.00 (0.76-1.33)<0.001

Family income-to-poverty ratio>=2.4

252/2345

920 138 (7.7% [6.7-8.8])

1 [Reference]<2.4

330/2323

838 185 (12.0% [10.6-13.4])

1.58 (1.32-1.88)

1 [Reference]

1 [Reference]
0.006

Food security
Full security
Marginal, low, or very low
security

508/4058

1 579 770 (9.2% [8.2-10.2])

1 [Reference]

101/972247 400 (8.3% [6.6-10.1])

1.47 (1.14-1.91)

1 [Reference]

1 [Reference]
0.973

Huang et al. BMC Medicine(2024) 22:343

Page 8 of 15

Table 2 (continued)
Hazard ratio (95% CI)
Death/No.

Weighted death ( %[95%CI])Age ­adjusteda

High school graduate
or higher

432/3920

1 450 299 (8.2% [7.4-9.1])

1 [Reference]Less than high school

191/1234

411 577 (13.4% [11.0-15.7])

1.47 (1.16-1.85)

581/4778

1 747 065 (9.1% [8.3-10.0])

1 [Reference]

43/384

116 793 (7.4% [4.8-10.0])

1.09 (0.75-1.57)

Mortality outcome

P value

MV model 1­ a,b,cP value

MV model 2­ a,b,c,d

P value

Education level
1 [Reference]
0.001

1.42 (1.12-1.81)

1 [Reference]
0.004

1.08 (0.81-1.44)

0.586

Regular health-care access
At least one regular
health-care facility
None or emergency room

1 [Reference]
0.662

1.06 (0.73-1.54)<0.0011.45 (1.21-1.74)

1 [Reference]
0.756

1.04 (0.70-1.54)<0.001

1.18 (0.97-1.44)

0.853

Type of health insurance
Private339/2939

1 063 829 (7.9% [6.8-9.0])

1 [Reference]

Government or none

280/2188

791 069 (11.1% [9.8-12.4])

1.47 (1.23-1.77)

1 [Reference]

1 [Reference]Own home or beingbrought

482/3931

1 518 767 (9.2% [8.2-10.2])

1 [Reference]

Rent home or otherarrangement

138/1146

339 039 (9.0% [7.4-10.7])

1.40 (1.12-1.75)

Married or livingwith a partner

342/3049

1 067 533 (8.0% [7.0-10.0])

1 [Reference]

Not married nor livingwith a partner

273/2076

765 428 (10.7% [9.4-12.1])

1.38 (1.17-1.64)<0.001

1.54 (1.29-1.84)<0.0011.30 (1.07-1.56)

Per 1 unfavorable SDoH
increased

NANA

1.39 (1.31-1.46)<0.0011.53 (1.45-1.60)<0.001NA

Non-cancer

1340/5163

3 789 190 (100%)1104/4096

3 092 711 (18.4% [16.8-19.9])

1 [Reference]

234/1061

693 309 (18.1% [15.4-20.8])

1.90 (1.57-2.29)

0.102Home ownership
1 [Reference]
0.003

1.42 (1.13-1.79)

1 [Reference]
0.002

1.11 (0.85-1.44)

0.440

Marital status
1 [Reference]

1 [Reference]
0.007

Employment status
Employed, student,or retired
Unemployed

1 [Reference]<0.0012.08 (1.70-2.55)<0.0011.99 (1.70-2.33)<0.0011.73 (1.37-2.17)

1 [Reference]<0.001

1.69 (1.36-2.10)<0.0011.60 (1.36-1.87)<0.0011.06 (0.82-1.37)<0.001

Family income-to-poverty ratio>=2.4

493/2345

1 501 492 (12.6% [11.2-14.0])

1 [Reference]<2.4

697/2323

1 884 262 (27.0% [24.5-29.6])

1.88 (1.62-2.18)

1 [Reference]

1 [Reference]<0.001

Food security
Full security

1138/4058

3 273 698 (19.0% [17.5-20.6])

1 [Reference]

166/972

413 408 (13.9% [11.0-16.9])

1.62 (1.28-2.03)

High school graduate
or higher

885/3920

2 727 255 (15.5% [14.2-16.8])

1 [Reference]Less than high school

453/1234

1 058 764 (34.4% [30.3-38.5])

1.69 (1.45-1.97)

1271/4778

3 599 988 (18.8% [17.5-20.2])

1 [Reference]

69/384

189 202 (12.0% [8.1-15.9])

1.05 (0.69-1.60)

Marginal, low, or very low
security

1 [Reference]

1 [Reference]
0.650

Education level
1 [Reference]<0.0011.72 (1.46-2.02)

1 [Reference]<0.0011.30 (1.09-1.54)

0.003

Regular health-care access
At least one regular
health-care facility
None or emergency room

1 [Reference]
0.814

1.04 (0.68-1.58)<0.0011.47 (1.28-1.70)

1 [Reference]
0.856

0.98 (0.64-1.53)<0.0011.20 (1.03-1.40)

0.945Type of health insurance
Private713/2939

2 092 202 (15.5% [14.2-16.9])

1 [Reference]

Government or none

615/2188

1 667 028 (23.4% [21.1-25.7])

1.46 (1.27-1.68)

Own home or beingbrought

1028/3931

2 958 039 (17.9% [16.5-19.3])

1 [Reference]

Rent home or otherarrangement297/1146

789 519 (21.0% [17.5-24.5])

1.77 (1.48-2.11)

1 [Reference]

1 [Reference]
0.018

Home ownership
1 [Reference]<0.001

1.89 (1.57-2.27)

1 [Reference]<0.001

1.57 (1.28-1.92)<0.001

Huang et al. BMC Medicine(2024) 22:343

Page 9 of 15

Table 2 (continued)
Hazard ratio (95% CI)
Death/No.

Weighted death ( %[95%CI])Age ­adjusteda

Married or livingwith a partner

705/3049

2 055 991 (15.3% [13.7-17.0])

1 [Reference]

Not married nor livingwith a partner

620/2076

1 688 271 (23.6% [21.5-25.8])

1.36 (1.17-1.58)<0.0011.53 (1.30-1.79)<0.001

1.18 (1.01-1.39)

Per 1 unfavorable SDoH
increased

NANA

1.47 (1.27-1.66)<0.001

1.69 (1.46-1.93)<0.001

NA

Mortality outcome

P value

MV model 1­ a,b,cP value

MV model 2­ a,b,c,d

P value

Marital status
1 [Reference]

1 [Reference]
0.041

Number of deaths, and number of participants in each category are unweighted observed frequencies. HRs (95% CIs) were estimated with imputation-adjusted
survey weights. MV Multivariablea

Adjusted for agebMultivariable model additionally adjusted for sex (male/female)cMultivariable model additionally adjusted for race and ethnicity (non-Hispanic Black, Hispanic, non-Hispanic White, other race or ethnicity [including American
Indian/Alaska Native/Pacific Islander, Asian, multiracial])dMultivariable model additionally adjusted for other SDoH

2.48–4.19) and 2.44 (95% CI, 1.60–3.72) for those with
4 unfavorable SDoHs, 3.99 (95% CI, 2.99–5.33) and3.60 (95% CI, 2.25–5.75) for those with 5 unfavorable
SDoHs, and 6.34 (95% CI, 4.51–8.90) and 5.00 (95%CI, 3.00–8.31) for those with 6 or more unfavorable
SDoHs, respectively, compared with of whom without unfavorable SDoH (Fig. 2). Kaplan–Meier curves
were used to estimate the cumulative probability of
all-cause and cancer-specific mortality using age as thetimescale. The all-cause and cancer-specific mortality
rates were significant across the several groups witha cumulative number of unfavorable SDoHs (Fig. 2,
P < 0.001). Pairwise comparison using log-rank showedthat the all-cause mortality rate was similar and not significantly different among cancer survivors with 0, 1, 2,
and 3 cumulative number of unfavorable SDoH acrossthe entire age cohort (Additional file 1: Table S4). There
was no significant difference in cancer-specific mortality among cancer survivors with 0, 1, 2, 3, and 4 cumulative number of unfavorable SDoH (Additional file 1:
Table S5). Based on the linear dose–response analysis
fitted curves (unfavorable SDoH ranged from 0 to 8),
every cumulative unfavorable SDoH increase was significantly associated with 64% increased risks of deathfrom all-cause (HR per 1-number increase, 1.64 [95%CI, 1.50–1.78]), and 53% of cancer (HR per 1-number
increase, 1.53 [95% CI, 1.45–1.60]) (Additional file 1:
Fig. S5 and Table 2; P < 0.001 for linear trend).
Age-adjusted/ age-gender-adjusted all-cause mortality
and cancer-specific mortality risk were significantly higher
in NHB cancer survivors when compared with NHW.
Further adjustment for all SDoH factors, black-white disparity in cancer-specific mortality was still observed (HR1.45, 95% CI 1.07–1.96), and the all-cause mortality did
not show a statistically significant difference (HR, 1.08;
95% CI 0.89–1.30; Table 3). In the mediation analysis, thesocioeconomic factor of unemployment (17.5% for allcause mortality; 15.3% for cancer-specific mortality) canmostly explain the racial disparity in all-cause and cancer-specific mortality, and unemployment was associatedwith a nearly 90% and 120% greater all-cause and cancer-specific mortality, respectively. A family income-topoverty ratio less than 2.4 (15.7%), an education less thanhigh school (8.1%), government health insurance (6.9%),
renting a home or other housing arrangement (15.4%),
and not being married nor living with a partner (13.4%)
indicated effective relative contribution to the disparity of
all-cause mortality between NHB and NHW cancer survivors. An additional factor (not being married nor livingwith a partner [10.2%]) contributed significantly to the
racial difference in cancer-specific mortality (Table 3).
In the subgroup analysis, NHW cancer survivors who
were unemployed, a lower level of PIR, an education lessthan high school, government or none of health insurance, renting a home or other housing arrangement, andnot being married nor living with a partner were significantly more likely to die of all-cause mortality comparedto NHW cancer survivors without unfavorable SDoH.
Unemployment and not being married nor living with apartner were significantly associated with a higher riskof cancer-specific mortality (Additional file 1: Table S6).
Being unemployed and having no access to a regular
health care facility or emergency room was significantly
associated with all-cause mortality in NHB cancer survivors. Only unemployed status was associated with cancer-specific mortality (Additional file 1: Table S6). In theHuang et al. BMC Medicine(2024) 22:343

Page 10 of 15

Fig. 2 All-cause mortality (A), cancer-specific mortality (B), and hazard ratios in US adults diagnosed with cancer aged 20 years or older according
to the cumulative number of unfavorable SDoH. Note: Kaplan–Meier curves showed cumulative mortality probability by age and a cumulative
number of unfavorable SDoH using age as the timescale. The number at risk is unweighted observed frequencies. Cumulative mortality rates
were estimated with the use of survey weights. Bar chart showed hazard ratios of all-cause mortality and cancer-specific mortality associatedwith a number of unfavorable SDoH, adjusted for age, gender, and race and ethnicity; error bars were 95% CIs. A Compared to those with 0
unfavorable SDoH, all-cause mortality of hazard ratios (95% CI) for cancer survivors with 1, 2, 3, 4, 5, or ≥ 6 unfavorable SDoH were 1.54 (1.25–1.89),
1.81 (1.46–2.24), 2.42 (1.97–2.97), 3.22 (2.48–4.19), 3.99 (2.99–5.33), and 6.34 (4.51–8.90), respectively. B Compared to those with 0 unfavorable
SDoH, cancer-specific mortality of hazard ratios (95% CI) for cancer survivors with 1, 2, 3, 4, 5, or ≥ 6 unfavorable SDoH were 1.52 (1.04–2.22), 1.70
(1.20–2.24), 2.22 (1.51–3.26), 2.44 (1.60–3.72), 3.60 (2.25–5.75), and 5.00 (3.00–8.31), respectively. ns was the abbreviation of no significance; ***meant p < 0.001, ** meant p < 0.01, and * meant p < 0.05

stratified analysis by gender (female and male), almost
all unfavorable SDoH were significantly associated withgreater all-cause and cancer-specific mortality for female
and male subgroups after adjusting for age, except forcancer-specific mortality for unfavorable home ownership (Additional file 1: Table S7). In all sensitivity analyses excluding mortalities that happened during the first2-year follow-up since the baseline interview, all results
remained similar in association with unfavorable SDoH
with all-cause, cancer-specific, and non-cancer mortality(Additional file 1: Table S8).
In this US nationally representative cohort study ofcancer survivors, we found that NHB and Hispanic adultcancer survivors self-reported a higher proportion ofmultiple unfavorable SDoHs compared to NHW adults
diagnosed with cancer. Compared to NHW cancer survivors, NHB cancer survivors had significantly higher
all-cause and cancer-specific mortality after adjusting forage and gender. In addition, after further adjusting for all
SDoH, there was no longer a difference between NHB
and NHW cancer survivors in all-cause mortality, but asignificant difference in cancer-specific mortality was stillobserved. These findings suggest that racial differencesin all-cause mortality between NHW and NHB cancer
survivors were largely attributable to the explained bydifferences in SDoH, while cancer-specific mortality disparities were partly explained by differences in SDoH.
Furthermore, unfavorable SDoH were associated with ahigher risk of all-cause and cancer-specific mortality forcancer survivors. During the 20 years of follow-up, anincreasing number of unfavorable SDoHs in the same
individual was associated with an increased risk of dying
from all causes, cancer, and noncancer causes, even after
adjusting for demographic factors, such as age, gender,and race. Of note, there were significantly linear dose–
response relationships between the cumulative numberof unfavorable SDoHs and all-cause and cancer-specificmortality among cancer survivors, and cancer survivors
having six or more unfavorable SDoH increased the HR
for mortality of 6.34 and 5.00 compared to those having
no unfavorable SDoH, respectively.
NHB cancer survivors were more likely than NHW
patients to have unfavorable levels of all SDoH. Compared to NHW cancer survivors, NHB and Hispanic
cancer survivors were 3.0 times and 3.9 times more likely
to experience six or more unfavorable SDoHs, respectively, which may partly explain the racial disparity inmortality. Most predominantly, NHB cancer survivors
were 1.6 times more likely than NHW cancer survivors
to have family PIR less than 2.4, which was associatedwith almost 50% and 25% greater all-cause mortality andcancer-specific mortality, respectively. Most recently,
Connolly et al. [30] conducted a study involving a cohortof 3590 participants from NHANES between 1999 and2014, and demonstrated that the SDoH level was morefavorable for NHW compared to NHB adolescents. Our

Huang et al. BMC Medicine(2024) 22:343

Page 11 of 15

Table 3 Mediation analysis by SDoH of the difference between NHB and NHW racial groups in all-cause death among US cancer
survivors aged 20 or older
All-cause mortality

Hazard ratio (95%CI)

Relative contribution, % (95%CI)

1.59 (1.36–1.86)-

1.57 (1.35–1.84)-

1.09 (0.91–1.31)-

Unemployed vs. employed, student, or retired

1.89 (1.64–2.19)

17.5% (6.0 to 29.1)

Family income-to-poverty ratio < 2.4 vs. ≥ 2.4

1.47 (1.28–1.69)

15.7% (4.9 to 26.5)

Marginal, low, or very low security vs. full security

1.04 (0.84–1.29)

2.0% (− 6.4 to 10.4)

Less than high school vs. high school graduate or higher

1.23 (1.04–1.45)

8.1% (0.1 to 16.1)

None or emergency room vs. at least one regular health care facility

0.95 (0.70–1.28)

0.6% (− 2.5 to 3.6)

Government or none vs. private health insurance

1.22 (1.07–1.40)

6.9% (0.6 to 13.3)

Rent a home or other arrangements vs. own home or being brought

1.40 (1.20–1.64)

15.4% (3.3 to 27.4)

Not married nor living with a partner vs. married or living with a partner

1.26 (1.10–1.44)

13.4% (4.1 to 22.7)

Hazard ratio (95% CI)

Relative contribution, % (95% CI)

2.04 (1.60–2.62)-

2.03 (1.59–2.59)-

1.45 (1.07–1.96)-

Unemployed vs. employed, student, or retired

2.19 (1.65–2.90)

15.3% (4.7 to 26.0)

Family income-to-poverty ratio < 2.4 vs. ≥ 2.4

1.25 (1.00–1.56)

3.9% (− 3.3 to 11.0)

Marginal, low, or very low security vs. full security

1.02 (0.75–1.39)− 0.4% (− 8.4 to 7.6)

Less than high school vs. high school graduate or higher

1.11 (0.83–1.50)

3.1% (− 5.5 to 11.7)

None or emergency room vs. at least one regular health care facility

0.99 (0.64–1.51)

0.3% (− 1.6 to 2.3)

Government or none vs. private health insurance

1.21 (0.98–1.50)

4.7% (− 0.9 to 10.4)

Rent home or other arrangement vs. own home or being brought

1.10 (0.84–1.44)

4.3% (− 4.4 to 13.0)

Age adjusted
NHB vs. NHW
Adjusted for age and gender
NHB vs. NHW
Adjusted for age, gender and all other SDoH
NHB vs. NHWStratified by race and adjusted for age and gender

Cancer-specific mortality
Age adjusted
NHB vs. NHW
Adjusted for age and gender
NHB vs. NHW
Adjusted for age, gender and all other SDoH
NHB vs. NHWStratified by race and adjusted for age and gender

Not married nor living with a partner vs. married or living with a partner

1.34 (1.10–1.65)

10.2% (0.8 to 19.6)

Abbreviations: SDoH Social determinants of health, NHB non-Hispanic Black, NHW non-Hispanic White

finding was consistent with another previous study thatreported a lower level of PIR, lower level of education
attachment, lack of health insurance coverage, dietary
insecurity, and limited health access were more common
in NHB compared to NHW, which was a key mediator in
explaining race disparity in all-cause and cause-specificmortality, especially cardiovascular disease and neoplasms [17, 40].
The persistent disparities in survival by race and ethnicity among cancer patients have been well-documented [2–4, 6, 41], and these disparities between NHB
and NHW cancer survivors were particularly stark [42].
Indeed, the overall cancer mortality in 2022 for male andfemale together was 12% (166.8 vs. 149.3 per 100,000

persons, respectively) higher in NHB compared to NHWcancer survivors [6]. However, racial differences were notthe only factor that contributed to observed mortality
disparity and the underlying causes attributed to thesedisparities have not been well established [43]. Various factors have been suggested as contributors to these
racial and ethnic disparities in survival outcomes amongcancer survivors, including differences in tumor characteristics [44, 45], neighborhood socioeconomic deprivation [42], and accessibility to health care. In the current
study, disparities in the all-cause mortality HR for NHB
cancer survivors compared to NHW cancer survivors
decreased from 1.59 (95% CI, 1.36–1.86) to 1.09 (95% CI,
0.91–1.31) after adjusting for all SDoHs, which mostly

Huang et al. BMC Medicine(2024) 22:343

mediated the racial disparity in all-cause mortality. With
respect to cancer-specific mortality, the HR for NHB
cancer survivors compared to NHW cancer survivors
decreased from 2.04 (95% CI, 1.60–2.62) to 1.45 (95%CI, 1.07–1.96) after adjusting for all SDoHs, which has
a partly mediator role in the racial difference. We foundthat cancer survivors with employed, student or retired
status (17.5% relative contribution), and PIR more than
2.4 (15.7% relative contribution) explained the greatest
percentage of disparities in all-cause mortality. Furthermore, we also showed that employed, student, or retired
status (15.3% relative contribution) and being marriedor living with a partner (10.2% relative contribution)
explained the largest portions of disparities in cancerspecific mortality. Taken together, the traditional socioeconomic factors consisting of household income, levelof education completed, and unemployment status were
important explanatory factors, that mediated around
45% and 25% of all-cause and cancer-specific mortality
in survival inequities between NHB and NHW cancer
survivors, respectively, which was consistent with thefindings of Bundy et al. (nearly 50% mediated the differential in all-cause premature mortality) [25]. The SDoH,
through an impact on occupational opportunities andincome levels, have a substantial influence on insurance
coverage, which was one of the main factors determining
access to and delivery of health care services in the US aswell as associated disparities in survival [40]. Conversely,
these traditional economic factors have a greater effecton the racial/ethnic disparities in the general populationcompared to cancer patients. Specifically, Luo et al. [20]suggested that income mediated 62% of the association in
mortality between NHB and NHW, which was consistent
with the dominant contributors to family income (40%)
and education (19%) to the gap between NHB and NHW
adult populations [17]. Interestingly, NHW cancer survivors were approximately 25% more likely to be marriedor living with a partner compared to NHB cancer survivors. Being married or living with a partner was associated with the cancer-related survival benefits, possibly
due to increased social support and higher psychological well-being and instrumental support, helping navigate the health care system [46, 47]. According to Fuzzel
et al. [48], barriers to health care accessibility and insurance coverage have a significant impact on rates of cancer screening, as well as the burden and attributions of
the disease. These findings suggested SDoH factors, as animportant mediator, drive racial health disparities, as well
as all-cause and cancer-specific mortality, highlighting
the necessity of the level of SDoH contexts for all people,
especially those who are more vulnerable to unfavorable
SDoH.

Page 12 of 15

The cumulative adverse SDoHs were associatedwith poor all-cause survival and cause-specific survival rates among the cancer-free population have beenpreviously reported, e.g., among patients with cardiovascular disease. Sameroff et al. [49] reported thatcumulative unfavorable social risk factors, such as foodinsecurity combined with social isolation and loneliness,have a higher relevance to poor health outcomes thansingle social risk factors. Jilani et al. [50] suggested that
greater SDoH adversity was linked to a higher burden of
cardiovascular risk factors and poor health outcomes,
such as stroke, myocardial infarction, coronary heart disease, heart failure, and mortality. Similarly, Zhang et al.
[16] combined family income level, occupation, educationlevel, and health insurance to measure socioeconomic
status, and reported that participants who met low socioeconomic status had higher risks of all-cause mortality(HR, 2.13 and 95% CI, 1.90–2.38 in the US NHANES;
HR, 1.96 and 95% CI, 1.87–2.06 in the UK Biobank), cardiovascular disease mortality (HR, 2.25; 95% CI, 2.00–
2.53), and incident cardiovascular disease (HR, 1.65; 95%
CI, 1.52–1.79) in UK Biobank, compared to high socioeconomic status. Our results were consistent with the findings of a study in which each additional SDoH conferred
additional cancer-related mortality, compared to cancer
survivors without any SDoH (1 SDoH [HR, 1.39; 95%
CI, 1.11–1.75], 2 SDoHs [HR, 1.61; 95% CI, 1.26–2.07],
and ≥ 3 SDoHs [HR, 2.09; 95% CI, 1.58–2.75]) [13]. In
contrast, Weires et al. [51] observed that women with ahigher socioeconomic status showed increased mortality
due to breast cancer in Sweden. This finding may be dueto the structure of the Swedish family cancer database(Swedes born after 1931 and their biological parents), as
well as analytical restrictions on individuals 30–60 yearsof age in 1960, which may exclude low-socioeconomic
adults with severe health problems. Previous studies haveshown that these unfavorable SDoH have a tendency tocluster in individuals [13, 23]. For example, individuals in
the general US population who self-reported food insecurity were more likely to be combined with a low level of
education attachment, not being married, a low level offamily income, and a bad lifestyle. This finding was consistent with our observation that these unfavorable SDoH
were not isolated but interrelated, and each unfavorable
SDoH included in our study has been found to independently increase the risk of mortality. Compared to mostprevious studies based on a single SDoH, we found that
there was a simple linear dose–response relationshipreflecting the cumulative effect of multiple unfavorable
SDoHs on all-cause and cancer-specific mortality. Collectively, these SDoH appear to synergistically increase
the risk of all-cause and cancer-specific mortality among

Huang et al. BMC Medicine(2024) 22:343

cancer survivors. However, the cumulative risk derived
from a sum of the number of unfavorable SDoH assumed
that all SDoH had equal and independent effects on survival outcomes, which might not be precise. We suggestthat future research may need to use more complex models, such as interaction models, to more accurately capture the complex interactions of unfavorable SDoH.
Strengths and limitations

The major strength of this study was the use of large
sample size data from the NHANES, which provides anopportunity to comprehensively evaluate the complex
relations of SDoH with all-cause and cancer-specific mortality among cancer survivors. In addition, we focused on
multiple SDoH factors and estimated the effect of accumulating unfavorable SDoH burden on mortality. We also
performed mediation analysis to show the contribution of
SDoH to disparities in all-cause and cancer-specific mortality. There were some limitations in the present study.
First, we conducted the analyses based on the follow-up oftime-to-event, however, all data on SDoH variables were
only assessed at the baseline interview, which may notreflect factors that changed during the follow-up period.
Therefore, our study was not able to quantify the effect ofchanges in eight SDoH on the mortality of cancer survivors over time. It is essential to conduct several repeatedinterviews about the level of SDoH during the follow-up
period to reveal the influence of SDoH factors on survival
among cancer survivors. Second, the assessment of SDoH
was limited by the availability of variables in the NHANES
database. Some SDoH such as neighborhood environment, social support, and exposure to racism, were notwidely available, which may also contribute to the allcause and cancer-specific mortality. Third, the follow-up
duration was relatively short (median, 81 months) and an
important bias among these cancer survivors such thatsocially disadvantaged who died during the study period
might have had severe disease at baseline.

Conclusions
In conclusion, in this cohort study of a nationally representative sample of US cancer survivors between 1999
and 2018, there were significant differences in SDoH
and mortality rates across self-reported racial and ethnic
groups. Unfavorable SDoH were more common among
NHB cancer survivors than NHW cancer survivors, werestrongly associated with an increased risk of all-cause
and cancer-specific mortality, and largely explained thedifference between NHB and NHW cancer survivors inall-cause mortality, as well as partially explained these
racial disparities in cancer-specific mortality. In addition, the cancer participants with a greater cumulative

Page 13 of 15

number of unfavorable SDoHs also appeared to be associated with higher risks of death from all-causes, andcause-specific (cancer and non-cancer). Taken together
with previous findings, the unfavorable SDoH levels werethe major risk factors for all-cause and cancer-specificmortality and were the underlying causes in all-cause
racial health disparities among US cancer survivors. The
entire government, civil society, local communities, businesses, and international agencies must pay more attention to the upstream SDoH, such as economic resources,
employment, education quality, and racial discrimination [52]. We believe that these findings shed highlighton the cumulative burden of SDoHs on all-cause andcancer-specific mortality among cancer survivors, providing insight for ongoing and future initiatives aimed
at mitigating mortality rates within vulnerable populations, including racial/ethnic minorities and individuals
with an unfavorable level of SDoH status. Addressing
social disparities in cancer health is a very importantpart of improving survival outcomes for cancer survivors, reflecting a commitment to health equity—aimed atachieving the optimal health for everyone.
Abbreviations
SDoH	Social determinants of healthWHO	World Health Organization
NCHS	National Center for Health Statistics
ICD	International Statistical Classification of Diseases, Injuries, andCauses of Death
NHW	Non-Hispanic WhiteNHB	Non-Hispanic BlackHR	Hazard ratio
NHANES	National Health and Nutrition Examination Survey
US	United States

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03563-0.
Additional files 1: Method S1. Details about mediation analysis. Table S1.
SDoH based on the Office of Disease Prevention and Health Promotion’s
Healthy People 2030 and World Health Organization in NHANES 1999–
2018. Table S2. Candidate social determinants of healthand associations
with all-cause mortality in the US cancer survivors, NHANES 1999–2018.
Table S3. Number of cancer survivor by cancer type and gender, NHANES
1999–2018. Table S4. P value for overall survival outcomes pairwise comparisons using log-rank test with Bonferroni adjustment. Table S5. P value
for cancer-specific survival outcomes pairwise comparisons using log-rank
test with Bonferroni adjustment. Table S6. Associations of social determinants of healthwith all-cause and cancer-specific mortality in US cancer
survivors by race/ethnicity, NHANES 1999–2018. Table S7. Associations ofsocial determinants of healthwith all-cause and cancer-specific mortality
in US cancer survivors by gender, NHANES 1999–2018. Table S8. Sensitivity analyses of association social determinants of healthand all-cause,
cancer-specific and non-cancer mortality in weighted and fully adjusted
multivariable analysis among cancer survivors, NHANES 1999–2018. Fig.
S1 Flowchart of participants selection for current analysis from NHANES
1999–2018. Fig. S2 The pairwise correlation between social determinantsof healthusing Spearman method. Fig. S3 The proportion for each cumulative number of social determinants of healthby gender. Fig. S4 All-cause
mortalityand cancer-specific mortalityfor cancer survivors aged 20 years
or older in US between 1999 and 2018 stratified by cumulative number ofHuang et al. BMC Medicine(2024) 22:343

unfavorable social determinants of health. Fig. S5 Linear dose–response
association between cumulative number of unfavorable social determinants of healthand all-cause of death, and cancer deathamong US cancer
survivors.
Acknowledgements
We thank Dr Jing Yi (School of Public Health, Chongqing Medical University,
Chongqing, 400016, China) for suggestions on the used in mediation analysis
and International Science Editing (http://​www.​inter​natio​nalsc​ience​editi​ng.​com) for editing this manuscript.
Authors’ contributions
HBH, YHL and FL designed the study. HBH, YHL, TTW, and YH conducted thestatistical analyses. HBH, TTW, YH, AJZ, ZZ, HZ, YJX, HNP, YHL, and FL drafted
the original manuscript. HBH, YHL, LQK and FL review the manuscript. All
authors approved the final version of manuscript.
Funding
This study was supported by grant 82202913 (Dr Yunhai Li) and 82372996 (DrFan Li) from the National Natural Science Foundation of China.
Availability of data and materials
The US NHANES are publicly available database and all data can be accessedfrom https://​wwwn.​cdc.​gov/​nchs/​nhanes/. The statistical code and datarequired to reproduce the results presented in this article can be requested
from Hongbo Huang (2023150448@stu.cqmu.edu.cn) or Fan Li (lf628@163.
com).

Page 14 of 15

5.
6.
7.
8.
9.
10.
11.

12.
13.

14.
15.

Declarations
Ethics approval and consent to participate
The US NHANES database has been approved by the National Center for
Health Statistics Ethics Review Board (https://​www.​cdc.​gov/​nchs/​nhanes/​
irba98.​htm), and written informed consent was provided by all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Department of Breast and Thyroid Surgery, The First Affiliated Hospitalof Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing 400016, China. 2 Health Management Center of University-Town Hospital
Affiliated to Chongqing Medical University, Chongqing 401331, China. 3 Schoolof Public Health, Chongqing Medical University, Chongqing 400016, China.
Received: 9 March 2024 Accepted: 14 August 2024

References
1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin.
2024;74(1):12–49.
2. Alcaraz KI, Wiedt TL, Daniels EC, Yabroff KR, Guerra CE, Wender RC. Understanding and addressing social determinants to advance cancer healthequity in the United States: A blueprint for practice, research, and policy.
CA Cancer J Clin. 2020;70(1):31–46.
3. Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM,
Jemal A, Kramer JL, Siegel RL. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019;69(5):363–85.
4. Kurani SS, McCoy RG, Lampman MA, Doubeni CA, Finney Rutten LJ,
Inselman JW, Giblon RE, Bunkers KS, Stroebel RJ, Rushlow D, et al.
Association of Neighborhood Measures of Social Determinants of HealthWith Breast, Cervical, and Colorectal Cancer Screening Rates in the US
Midwest. JAMA Netw Open. 2020;3(3).

16.

17.

18.
19.

20.
21.

22.

23.
24.
25.

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin.
2019;69(1):7–34.
Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer
J Clin. 2023;73(1):17–48.
Yu Z, Yang X, Guo Y, Bian J, Wu Y. Assessing the Documentation of Social
Determinants of Health for Lung Cancer Patients in Clinical Narratives.
Front Public Health. 2022;10.
World Health Organization. Social determination of health. [https://​www.​who.​int/​health-​topics/​social-​deter​minan​ts-​of-​health#​tab=​tab_1].
Braveman P, Gottlieb L. The social determinants of health: it’s time to consider the causes of the causes. Public Health Rep. 2014;129 Suppl 2(Suppl2):19–31.
A conceptual framework for action on the social determinants of health.
[https://​iris.​who.​int/​handle/​10665/​44489].
U.S Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2030: Social Determinantsof Health. [https://​health.​gov/​healt​hypeo​ple/​prior​ity-​areas/​social-​deter​minan​ts-​health].
Fabregas JC, Riley KE, Brant JM, George TJ, Orav EJ, Lam MB. Association ofsocial determinants of health with late diagnosis and survival of patientswith pancreatic cancer. J Gastrointest Oncol. 2022;13(3):1204–14.
Pinheiro LC, Reshetnyak E, Akinyemiju T, Phillips E, Safford MM. Social
determinants of health and cancer mortality in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort study. Cancer.
2022;128(1):122–30.
Chetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, Bergeron A,
Cutler D. The Association Between Income and Life Expectancy in the
United States, 2001–2014. JAMA. 2016;315(16):1750–66.
Stringhini S, Carmeli C, Jokela M, Avendaño M, Muennig P, Guida F, Ricceri
F, d’Errico A, Barros H, Bochud M, et al. Socioeconomic status and the
25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet.
2017;389(10075):1229–37.
Zhang YB, Chen C, Pan XF, Guo J, Li Y, Franco OH, Liu G, Pan A. Associations of healthy lifestyle and socioeconomic status with mortality andincident cardiovascular disease: two prospective cohort studies. BMJ.
2021;373:n604.
Beydoun MA, Beydoun HA, Mode N, Dore GA, Canas JA, Eid SM, Zonderman AB. Racial disparities in adult all-cause and cause-specific mortality
among us adults: mediating and moderating factors. BMC Public Health.
2016;16(1):1113.
Braveman PA, Egerter SA, Cubbin C, Marchi KS. An approach to studying social disparities in health and health care. Am J Public Health.
2004;94(12):2139–48.
Meneton P, Kesse-Guyot E, Méjean C, Fezeu L, Galan P, Hercberg S,
Ménard J. Unemployment is associated with high cardiovascular event
rate and increased all-cause mortality in middle-aged socially privileged
individuals. Int Arch Occup Environ Health. 2015;88(6):707–16.
Luo J, Hendryx M, Wang F. Mortality disparities between Black and WhiteAmericans mediated by income and health behaviors. SSM Popul Health.
2022;17:101019.
Petrovic D, de Mestral C, Bochud M, Bartley M, Kivimäki M, Vineis P,
Mackenbach J, Stringhini S. The contribution of health behaviors tosocioeconomic inequalities in health: A systematic review. Prev Med.
2018;113:15–31.
Sun Y, Liu B, Rong S, Du Y, Xu G, Snetselaar LG, Wallace RB, Bao W.
Food Insecurity Is Associated With Cardiovascular and All-Cause
Mortality Among Adults in the United States. J Am Heart Assoc.
2020;9(19):e014629.
Walker RJ, Chawla A, Garacci E, Williams JS, Mendez C, Ozieh MN, Egede
LE. Assessing the relationship between food insecurity and mortality
among U.S. adults. Ann Epidemiol. 2019;32:43–8.
Pappas G, Queen S, Hadden W, Fisher G. The increasing disparity inmortality between socioeconomic groups in the United States, 1960 and
1986. N Engl J Med. 1993;329(2):103–9.
Bundy JD, Mills KT, He H, LaVeist TA, Ferdinand KC, Chen J, He J. Social
determinants of health and premature death among adults in the
USA from 1999 to 2018: a national cohort study. Lancet Public Health.
2023;8(6):e422–31.

Huang et al. BMC Medicine(2024) 22:343

26. Su LJ, O’Connor SN, Chiang TC. Association Between Household Income
and Self-Perceived Health Status and Poor Mental and Physical HealthAmong Cancer Survivors. Front Public Health. 2021;9:752868.
27. Neroda P, Hsieh MC, Wu XC, Cartmell KB, Mayo R, Wu J, Hicks C, Zhang
L. Racial Disparity and Social Determinants in Receiving Timely Surgery
Among Stage I-IIIA Non-small Cell Lung Cancer Patients in a U.S. Southern State. Front Public Health. 2021;9:662876.
28. Centers for Disease Control and Prevention. National Center for Health
Statistics. National Health and Nutrition Examination Survey. [https://​wwwn.​cdc.​gov/​nchs/​nhanes/].
29. Marmot M, Friel S, Bell R, Houweling TA, Taylor S. Closing the gap in a
generation: health equity through action on the social determinants ofhealth. Lancet. 2008;372(9650):1661–9.
30. Connolly SD, Lloyd-Jones DM, Ning H, Marino BS, Pool LR, Perak AM.
Social Determinants of Cardiovascular Health in US Adolescents: National
Health and Nutrition Examination Surveys 1999 to 2014. J Am Heart
Assoc. 2022;11(22):e026797.
31. Yu Q, Li B. mma: an R package for mediation analysis with multiple
mediators. J Open Res Software. 2017. https://​doi.​org/​10.​5334/​jors.​160.
32. Yu Q, Scribner RA, Leonardi C, Zhang L, Park C, Chen L, Simonsen NR.
Exploring racial disparity in obesity: A mediation analysis considering
geo-coded environmental factors. Spat Spatiotemporal Epidemiol.
2017;21:13–23.
33. Yu Q, Wu X, Li B, Scribner RA. Multiple mediation analysis with survival
outcomes: With an application to explore racial disparity in breast cancer
survival. Stat Med. 2019;38(3):398–412.
34. Gómez CA, Kleinman DV, Pronk N, Wrenn Gordon GL, Ochiai E, Blakey C,
Johnson A, Brewer KH. Addressing Health Equity and Social Determinantsof Health Through Healthy People 2030. J Public Health Manag Pract.
2021;27(Suppl 6):S249-s257.
35. He J, Zhu Z, Bundy JD, Dorans KS, Chen J, Hamm LL. Trends in Cardiovascular Risk Factors in US Adults by Race and Ethnicity and Socioeconomic
Status, 1999–2018. JAMA. 2021;326(13):1286–98.
36. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to measuring household food security : revised March, 2000. Alexandria, VA: U.S. Departmentof Agriculture, Food and Nutrition Service, Alexandria VA. March; 2000.
37. National Center for Health Statistics. 2019 Public-Use Linked Mortality
Files. [https://​www.​cdc.​gov/​nchs/​data-​linka​ge/​morta​lity-​public.​htm].
38. Korn EL, Graubard BI, Midthune D. Time-to-event analysis of longitudinal follow-up of a survey: choice of the time-scale. Am J Epidemiol.
1997;145(1):72–80.
39. Sattar N, Preiss D. Reverse Causality in Cardiovascular Epidemiological Research: More Common Than Imagined? Circulation.
2017;135(24):2369–72.
40. Islami F, Guerra CE, Minihan A, Yabroff KR, Fedewa SA, Sloan K, Wiedt TL,
Thomson B, Siegel RL, Nargis N, et al. American Cancer Society’s reporton the status of cancer disparities in the United States, 2021. CA Cancer J
Clin. 2022;72(2):112–43.
41. Aizer AA, Wilhite TJ, Chen MH, Graham PL, Choueiri TK, Hoffman KE,
Martin NE, Trinh QD, Hu JC, Nguyen PL. Lack of reduction in racialdisparities in cancer-specific mortality over a 20-year period. Cancer.
2014;120(10):1532–9.
42. Ellis L, Canchola AJ, Spiegel D, Ladabaum U, Haile R, Gomez SL. Racial andEthnic Disparities in Cancer Survival: The Contribution of Tumor, Sociodemographic, Institutional, and Neighborhood Characteristics. J Clin Oncol.
2018;36(1):25–33.
43. Institute of Medicine Committee on U, Eliminating R, Ethnic Disparities in
Health C. In: Unequal Treatment: Confronting Racial and Ethnic Disparities
in Health Care. edn. Edited by Smedley BD, Stith AY, Nelson AR. Washington (DC): National Academies Press (US). Copyright 2002 by the National
Academy of Sciences. All rights reserved. 2003.
44. Özdemir BC, Dotto GP. Racial Differences in Cancer Susceptibility and Survival: More Than the Color of the Skin? Trends Cancer. 2017;3(3):181–97.
45. Virnig BA, Baxter NN, Habermann EB, Feldman RD, Bradley CJ. A matterof race: early-versus late-stage cancer diagnosis. Health Aff (Millwood).
2009;28(1):160–8.
46. Gomez SL, Hurley S, Canchola AJ, Keegan TH, Cheng I, Murphy JD, Clarke
CA, Glaser SL, Martínez ME. Effects of marital status and economic
resources on survival after cancer: A population-based study. Cancer.
2016;122(10):1618–25.

Page 15 of 15

47. Pinquart M, Duberstein PR. Associations of social networks with cancer
mortality: a meta-analysis. Crit Rev Oncol Hematol. 2010;75(2):122–37.
48. Fuzzell LN, Perkins RB, Christy SM, Lake PW, Vadaparampil ST. Cervicalcancer screening in the United States: Challenges and potential solutions
for underscreened groups. Prev Med. 2021;144:106400.
49. Sameroff AJ, Rosenblum KL. Psychosocial constraints on the development of resilience. Ann N Y Acad Sci. 2006;1094:116–24.
50. Jilani MH, Javed Z, Yahya T, Valero-Elizondo J, Khan SU, Kash B, Blankstein
R, Virani SS, Blaha MJ, Dubey P, et al. Social Determinants of Health andCardiovascular Disease: Current State and Future Directions Towards
Healthcare Equity. Curr Atheroscler Rep. 2021;23(9):55.
51. Weires M, Bermejo JL, Sundquist K, Sundquist J, Hemminki K. Socio-economic status and overall and cause-specific mortality in Sweden. BMC
Public Health. 2008;8:340.
52. Braveman P, Egerter S, Williams DR. The social determinants of health:coming of age. Annu Rev Public Health. 2011;32:381–98.

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