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Impact of a natural disaster on access to care and biopsychosocial outcomes
among Hispanic/Latino cancer survivors
Mary Rodriguez-Rabassa1,2*, Ruthmarie Hernandez1, Zindie Rodriguez3, Claudia B.
Colon-Echevarria5, Lizette Maldonado3, Nelmit Tollinchi1, Estefania Torres1, Adnil
Mulero1, Daniela Albors5, Jaileene Perez-Morales7, Idhaliz Flores4,6, Heather Jim8, Eida
M. Castro1,2, Guillermo N. Armaiz-Pena3,4,5*
1Clinical Psychology Program, School of Behavior and Brain Sciences, Ponce Health
Sciences University, Ponce, Puerto Rico
Divisions of 2Mental Health, 3Cancer Biology and 4Women’s Health, Ponce Research
Institute, Ponce, Puerto Rico
Department of Basic Sciences, Divisions of 5Pharmacology and 6Microbiology, School
of Medicine, Ponce Health Sciences University, Ponce, Puerto Rico
Departments of 7Cancer Epidemiology and 8Health Outcomes and Behavior, H. Lee
Moffitt Cancer Center and Research Institute, Tampa, Florida
Keywords: biopsychosocial, cancer, natural disasters, access to care, cytokines
Funding: This work was supported by grants from the NIH NCI U54CA163071 and
U54CA163068, NIH NIMHD R21MD013674 and G12MD007579, NIH NIGMS
P20GM103475 and NIH NIMHD/NIAID U54MD007587. CBC-E was supported by
grants from the NIH NIGMS R25GM082406 and NIH NCI F31CA232622.
*Corresponding authors:
Email: garmaiz@psm.edu, marodriguez@psm.edu
Conflict of Interest: HJ serves as a consultant to RedHill BioPharma and Janssen
Scientific Affairs. Other authors declare no potential conflicts of interest.
Running Tittle: Natural disaster outcomes in cancer patients.
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Abstract
Cancer is the leading cause of death in Puerto Rico (PR). Hurricane Maria (HM) and its
aftermath lead to widespread devastation in the island, including the collapse of the
healthcare system. Medically fragile populations, such as cancer survivors, were
significantly affected. The goal of this study was to assess the impact of HM on barriers
to care, emotional distress, and inflammatory biomarkers among cancer survivors in
PR. This exploratory longitudinal study was conducted in health care facilities and
community support groups from PR. Cancer survivors (n=50) and non-cancer
participants (n=50) completed psychosocial questionnaires and provided blood samples
that were used to assess inflammatory cytokines levels. Data were analyzed through
descriptive, frequencies, correlational, and linear regression analyses. Cancer survivors
that were affected by HM reported increased barriers in accessing medical care, which
were directly associated with anxiety, perceived stress, and post-traumatic
symptomatology. Moreover, being a cancer survivor, along with closeness in time from
HM predicted more barriers to receiving health care. Several inflammatory cytokines,
such as CD31, BDNF, TFF3, Serpin E-1, Vitamin D BP, VCAM-1, Osteopontin,
Chitinase 3 like 1, MMP-9 and MIF were significantly upregulated in cancer survivors
while BDNF, MMP9 and Osteopontin had significant positive correlations with barriers to
care. HM significantly impacted Puerto Ricans psychosocial well-being. Cancer
survivors had significant barriers to care and showed increased serum inflammatory
cytokines, but didn’t show differences in anxiety, stress and post-traumatic symptoms
compared to non-cancer participants.
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Introduction
Natural disasters can significantly alter an individual’s daily life and lead to
psychological distress, particularly in medically fragile populations such as cancer
survivors. Hurricane Maria made landfall in Puerto Rico (PR) on September 20, 2017
as Category 4 storm, killing an estimated 2,975 people and causing an estimated $90
million in damages (1-3). Widespread devastation included loss of power and potable
water infrastructure; destruction of buildings, bridges, and roads; lack of
telecommunications; and closing of ports and airports (4). Lack of access to food and
clean water was a significant problem for residents of PR (4). Mudslides rendered many
roads in rural areas impassable, limiting relief efforts and access to medical care (4).
The severity and duration of the aftermath caused significant psychological distress in
the Puerto Rican population (5). Suicide rates rose by 29% compared to the year
before (2016) (5). Moreover, during the subsequent three months after HM made
landfall, the government psychosocial helpline received 2.4 times more calls to manage
suicidal attempts (5). However, knowledge about psychological distress and
biobehavioral factors after a natural disaster, such as HM, in cancer patients remains
limited.
There is growing evidence supporting the role of stress in cancer progression (6).
Several studies have suggested that altered psychological states, including chronic
stress or depression, may accelerate growth of existing tumors (7). Planning for short-
and long-term patient cancer care requires a holistic approach that considers the impact
of psychological distress at the biological level in the context of a natural disaster. For
example, traumatic stress, anxiety, and depression are common and persistent after a
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positive cancer diagnosis, and have been associated to alterations in circulating
markers of inflammation (e.g., decreased NK cell cytotoxicity, elevated white blood cell
counts, increased inflammatory macrophages) (7, 8). Moreover, extreme environmental
stressors could have a significant effect on cancer survivors and lead to increased
prevalence of psychological distress, such as, chronic stress and depression, that have
been associated to disease progression (6). At the physiological level, activation of the
sympathetic nervous system (SNS) or the hypothalamic-pituitary adrenal (HPA) axis
lead to the release of norepinephrine, epinephrine, and cortisol, respectively. These
have been shown to induce the release of immune and inflammatory factors (6).
Specifically, activation of SNS and HPA resulted in elevated levels of several cytokines,
infiltration of macrophages, and enhanced tumor growth (8, 9). In light of these data, it is
important to determine whether and to what extent psychological distress (caused by an
extreme environmental stressor) can contribute to altered behavioral states and
systemic cytokine levels.
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Materials and Methods
The study followed the ethical principles from the Declaration of Helsinki and was
approved by the Institutional Review Board of the Ponce Medical School Foundation.
(IRB Approval 080121-IF). We obtained written informed consent from all participants
at the time of enrollment.
Participants
This prospective longitudinal case-control study collected participants’ blood and
self-reported measurements. All measurements were obtained at the time of recruitment
and every three months for a period of one year following the landfall of Hurricane Maria
(September 20, 2017). Recruitment started four months after HM (January 2017) and
up to August 2019 is still ongoing. Recruitment took place in health care facilities
located in the southern area of PR, with the collaboration of community support groups
and the PRBB facilities at a tertiary hospital in Ponce, PR. All participants were in the
island at the time of HM and remained in PR for at least three months after the passage
of the hurricane. The inclusion criteria were: participants must be between the ages of
21 and 89, with or without current or past history of any cancer type. Subjects were
excluded if they self-reported or documented severe or uncontrolled psychiatric or
neurological conditions that preclude their participation in the study.
Natural Disaster Outcomes
The research team developed the Natural Disaster Outcomes Questionnaire,
based on their personal experiences with HM, to identify problems suffered by
participants in the aftermath of the hurricane. This survey contains 23 Likert-type
questions that explores if and to what degree participants were impacted by hurricane-
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related problems during the previous three. Total scores range from 0 to 92, with higher
scores reflecting higher impact. The questionnaire is included as Supplementary Data 1.
This questionnaire was subjected to content validation by individuals that experienced
HM and its aftermath.
Psychological Distress and Access to Health Care
Participants answered a battery of questionnaires that explored psychological
distress. Scoring and interpretation of these questionnaires followed the guidelines and
procedures reported in the literature. To gather participants’ reports of depression and
anxiety symptomatology they answered the PHQ-8 and the GAD-7 questionnaires,
respectively (10-12). Participants were asked to complete the Distress Thermometer
(psychological discomfort), the PSS (stressful situations), the PCL-5 (PTSD
symptomatology) (13-15), BRS (resilience) and the PTGI-SF (post-traumatic growth)
(16-18). Perception of social support was also assessed using the ISEL-12 (19). To
assess potential barriers that participants faced with access to health care after the
hurricane we utilized the BCQ (20, 21). To better understand the study results, the
research team inverted the scoring system so higher scores would now indicate higher
barriers.
Blood processing, storage and cytokine array analyses
Blood samples were obtained at the time of recruitment and processed within
four hours of collection time to isolate serum following standard methods. Serum
samples were stored at -80°C. Serum was analyzed with R & D Proteome Profiler
Human XL Cytokine Array kits (Minneapolis, MN) according to manufacturer's
instructions. Arrays were quantified using Quick Spots Tool in Western Vision's
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HLImage++ (Version 22.0). Cytokine heatmaps were constructed in RStudio (Version
1.0.153) with the following R packages: RColorBrewer, d3heatmap and ggplot2. To
identify significant differences in cytokine patterns between cancer patients and
controls, volcano plots were constructed using MultiExperiment Viewer Software
(version 10.2). Protein-protein interaction network and gene enrichment of differentially
expressed cytokines were constructed with STRING online platform (version 11.0).
Statistical Analysis
Variable distributions were assessed with the Kolmogorov-Smirnov test to
determine the statistical approach required. With the exception of the PSS Scale,
scores for all variables were not normally distributed. We used non-parametric methods
to describe the outcomes (e.g., median and interquartile ranks) based on participants’
history of cancer. We used Spearman Rho correlations to explore associations between
variables and Mann-Whitney U tests to test group differences for the outcomes
including cytokine levels. We performed linear regression analyses to predict barriers in
access to care by cancer status, age, and time of recruitment after HM made landfall.
We used the RStudio statistical program with the following packages: nortest, psych,
olsrr, qgraph, dplyr, Hmisc, corrplot, tidyselect, ggpubr, gplots, RColorBrewer,
d3heatmap and ggplot2. All tests were 2-sided and statistical significance was defined
as p<0.05. Cytokine expression analysis were performed using expression raw values
(arbitrary units) and differences in cytokine values between cancer and non-cancer.
Cytokine distribution normality was assessed using the Shapiro-Wilk test. We performed
Mann-Whitney analyses to identify significant differences in cytokine expression
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between groups. Statistical significance threshold for cytokine analyses was established
at p<0.01.
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Results
Study Population
Here we studied the biopsychosocial effect of a natural disaster (HM) on 100
Hispanic/Latino participants (50 cancer survivors and 50 non-cancer participants). The
mean time of recruitment after the hurricane was 7.97 months (range: 4.33 – 11.27
months). Table 1 shows sociodemographic characteristics of study participants and
data obtained from psychosocial questionnaires administered to all participants. In
general, the majority of the participants were women (72%). Breast (44%) and prostate
(14%) cancers were the most prevalent cancer types among cancer survivors. The age
of cancer survivors [median (Mdn) = 58.5 (interquartile range (IQR) = 50.5-66.0)] was
significantly higher from non-cancer participants (Mdn=49.5, IQR=38.5-57.0), p<0.05.
Natural Disaster Outcomes
Participants reported being negatively affected by all items identified in the
Natural Disaster Outcomes Questionnaire (Table 1). Cancer survivors showed lower
median total scores (Mdn=17.00, IQR=8.00-32.25) than non-cancer participants
(Mdn=26.00, IQR=9.00-43.00) but this difference was not significant (p=0.146). The
most frequent problems faced by participants from both groups were no electricity
service (cancer: 68%; non-cancer: 86%) and difficulties with communication services
(cancer: 66%; non-cancer: 82%), but these rates were not significantly different
between groups. However, reports on the degree of discomfort caused by natural
disaster related outcomes were usually higher in non-cancer participants than cancer
survivors. For example, non-cancer participants reported higher rates of being affected
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by having no water service, food insecurity, difficulties accessing roads, and loss of
security for the family (p<0.05).
Psychological Distress
Cancer survivors showed higher symptomatology in several measures of
psychological distress compared to non-cancer participants (Table 1). The median
scores of depression symptomatology for cancer survivors were 7.00 (IQR=3.0-10.25)
versus 4.00 (IQR=1.75-9.00) in non-cancer participants (p=0.21). Analyses of the
severity of symptoms showed that a higher number of cancer survivors (n=14, 28%) are
distributed in the categories of moderate to severe depression compared to non-cancer
participants (n=10, 20%). Similarly, post-traumatic symptomatology scores in cancer
survivors were slightly higher than non-cancer participants (Mdn=12.00, IQR=5.00-
24.25 vs. Mdn=10.00, IQR=2.00-20.50; p=0.25). Moreover, increased severity of post-
traumatic symptomatology (scores higher than 33) were more frequently observed in
cancer survivors (cancer 18%, non-cancer 14%), who reported higher difficulties
sleeping and concentrating or feeling easily startled.
Both groups revealed comparable scores on measures of anxiety
symptomatology that were not significantly different (cancer: Mdn=4.50, IQR=2.00-7.50;
non-cancer: Mdn=4.00, IQR=1.75-9.75), emotional distress (cancer: Mdn=4.00,
IQR=1.00-7.00; non-cancer: Mdn=4.00, IQR=1.00-6.75), and perceived stress (cancer:
Mdn=16.00, IQR=11.00-20.00; non-cancer: Mdn=16.00, IQR=9.50-18.50). Scores on
resilience (cancer: Mdn=3.33, IQR=2.83-4.33; non-cancer: Mdn=3.33, IQR=2.83-3.83),
post-traumatic growth (cancer: Mdn=40.00, IQR=28.75-45.00; non-cancer: Mdn=39.50,
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IQR=33.75-44.25) and perception in social support (cancer: Mdn=9.50, IQR=4.75-
16.25; non-cancer: Mdn=7.00, IQR=2.75-14.00) were also not significantly different.
Access to Health Care
Cancer survivors reported higher barriers in access to care compared to non-
cancer participants (Table 1). Cancer survivors had higher median scores on the total
scale (cancer: Mdn=10.26, IQR=3.21-30.13 vs non-cancer: Mdn=6.41, IQR=1.28-18.43)
as well as in the Skills subscale (cancer: Mdn=4.69, IQR=0.00-18.75 vs. non-cancer:
Mdn=0.00, IQR=0.00-6.25), but these differences did not reach statistical significance
(p=0.08). The median score on the Marginalization subscale was greater in cancer
survivors compared to non-cancer participants (Mdn=9.00, IQR=0.00-24.43 vs.
Mdn=2.27, IQR=0.00-17.05, respectively) although not significant (p=0.07).
Next, we explored the associations between participants’ scores on various
psychosocial questionnaires used in this study for each group, using the Spearman’s
Rho correlation test. Figure 1 is a visual representation of a correlation network with
significant associations (p<0.05) in cancer (A) and non-cancer participants (B),
generated using qgraph in RStudio 1.1.463. Both groups displayed a direct correlation
between scores of depression, anxiety, post-traumatic symptoms, distress, and
perceived stress. Resilience scores were inversely related to these measures in all
participants, except for depression, which showed no association. Resilience was also
negatively associated with barriers to care (skills subscale), but only in cancer survivors.
All measures of barriers in access to care were directly inter-associated in both groups,
but reports of such barriers by cancer survivors showed significant positive associations
with scores on anxiety, perceived stress, and post-traumatic symptoms. Interestingly,
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the perceived social support scores in cancer survivors revealed a positive relationship
with depression, anxiety, post-traumatic symptoms, distress, and perceived stress. In
summary, the psychosocial variables exhibited stronger associations among them in
cancer survivors than in non-cancer participants.
Linear regression analyses confirmed a significant association between barriers
in access to care and cancer status (Supplementary Table 1;
β
= 7.28, 95% CI: 0.58-
13.97, p=0.03). Being a cancer survivor increased the barriers to access to care score
by 7.28 points. However, after controlling for age and time of recruitment after the
hurricane (
β
= 7.01, 95% CI: -0.59-14.08, p=0.06) the model failed to show a significant
association between barriers in access to care and cancer status (Supplemental Table
1). Only when we adjusted our model to account for recruitment time after the
hurricane, the association became significant. Specifically, being a cancer survivor
increased barriers in access to care score by 7.38 after controlling for time of
recruitment (Supplemental Table 1;
β
= 7.38, 95% CI: 0.75-14.00, p=0.03). This model
revealed that cancer status and time of recruitment explained 7.4% of the scores in
barriers in access to care. The regression formula was: Barriers score = 23.04 +
7.38*cancer status – 1.47*recruitment time. The predicted score of barriers in access to
care is 28.85 when an individual has cancer and if he or she was recruited in the month
following the hurricane.
Serum Cytokine Analyses
Serum was obtained from 40 cancer survivors and 35 non-cancer participants.
Figure 2a shows a heatmap visualization of cytokine expression by group. Figure 2b-c
shows cytokines that were significantly upregulated in the cancer survivor group when
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compared to non-cancer participants. The most significantly upregulated cytokines were
CD31, BDNF, TFF3, Serpin E-1, Vitamin D BP, VCAM-1, Osteopontin, Chitinase 3 like
1, MMP-9 and MIF (Figure 2c). To determine possible protein-protein association
networks we data-mined the STRING V11 (22). Figure 2d depicts molecular
relationships, interactions, and pathway associations between cytokines that were
significantly upregulated in cancer survivors compared to non-cancer participants. To
understand biological processes that could be modulated by these cytokines, we
performed pathway enrichment analyses using STRING v11. Table 2 depicts KEGG
pathway enrichment data (23-25) based on significantly upregulated cytokines identified
in cancer survivors compared to non-cancer participants. These analyses revealed
significantly enriched pathways that included leukocyte migration, PI3K-AKT and TNF
signaling pathways, cell and focal adhesion, Ras and MAPK signaling, among other
pathways (Supplemental Table 2).
Furthermore, we explored if there was a correlation between the top ten
significantly upregulated cytokines and psychosocial measures using the Spearman’s
Rho correlation procedure. Figure 3 illustrates a visual representation of a correlation
network with significant associations done using corrplot in RStudio 1.1.463. First, we
evaluated the whole cohort and found that most cytokines had a significant positive
correlation with perceived social support (Figure 3a and Supplemental Figure 1a).
When the cohort was divided into groups our data show that this effect was only
observed in the non-cancer group (Figure 3b-c and Supplemental Figure 1b-c).
Interestingly, in the cancer survivor group, BDNF, MMP9 and Osteopontin, all known to
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promote disease progression (26-28), had significant positive correlations with several
measures related to barriers in access to care (Figure 3c and Supplemental Figure 1c).
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Discussion
Our data show that cancer survivors that faced HM and its aftermath suffered
from lack of access to care leading to increased health disparities among Puerto Rican
cancer patients. In fact, not only were cancer survivors presented with more barriers in
accessing medical care, but our research also revealed direct relationships with anxiety,
perceived stress, and post-traumatic symptomatology that were only observed in cancer
survivors. Linear regression analysis confirmed that being a cancer survivor predicted
more barriers to receiving health care along with closeness to the events related to a
natural disaster (such as HM). Moreover, several inflammatory cytokines were found to
be significantly upregulated in cancer survivors while pathway enrichment analyses
showed that these were associated with activation of tumor-promoting pathways such
as those mediated by MAPK, PI3K-AKT, Ras and TNF. Further, we uncovered a
positive correlation between several cytokines and perceived social support in cancer
survivors. Also, in cancer survivors BDNF, MMP9 and Osteopontin (all associated to
pro-tumoral processes) levels were associated with lack of access to care.
As expected, the effects of HM in PR infrastructure affected cancer survivors as
well as non-cancer participants. In general, participant's responses in most of the
psychosocial measures suggest that both groups were affected equally. Although
psychological distress symptoms in the total sample showed a negative and significant
relationship with resilience, group analysis revealed that the association of resilience
with symptomatology of depression in the group of cancer survivors was not significant.
These findings show that compared to non-cancer participants, cancer survivors report
greater severity in the symptoms of depression and greater resiliency. This observation
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is unexpected, but it can be explained by patients’ tendency to conceal emotional
distress to protect loved ones from worrying. Also, in cancer survivors, social support
was positively correlated with distress. This association could be explained by Hispanic
cultural values that emphasize supportive family relationships where families are
prioritized before individual’s well-being. On the other hand, the survivor‘s resiliency
was negatively related to barriers in access to care. Exacerbations in such barriers after
the hurricane for a prolonged time can put them at risk of worsening prognoses, both
physically and psychologically.
Our data identified several cytokines that have been associated with
inflammatory processes, biobehavioral factors and cancer biology (6, 8, 29). For
example, CD31 has been associated to angiogenic processes and cancer progression
that have been shown to be promoted by chronic stress in preclinical models of cancer
(6, 30). Moreover, BDNF was recently shown to be involved in cancer progression and
to be modulated by chronic stress and activation of the SNS (26). Our data also
identified several signaling pathways that were enriched by cytokines found to be
significantly induced. These included PI3K-Akt (31, 32), MAPK (6), Ras and TNF (6, 8);
signaling nodes that are well-known to play key roles in cancer biology. Finally,
induction of BDNF and MMP9 was associated with barriers to access to care, in itself a
potential source of distress. This is an important observation as these cytokines have
been reported to play key roles in biobehavioral effects on cancer (26, 27). They have
also been associated with metastases, cancer progression and activation of the SNS
(26, 27). These data suggest that psychosocial outcomes in the aftermath of a natural
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disaster could potentially play a role in cancer biology by promoting tumor-associated
processes.
Study Limitations
Even though our findings provide new light of how a natural disaster can affect
cancer survivors, we acknowledge that our results are limited by not controlling for type
of cancer, or between subjects with active disease vs. survivors, cancer stage, age, sex,
and other comorbidities. Our analyses were also limited by not knowing the previous
psychological history of participants. We acknowledge that the psychometric properties
of our newly developed Natural Disaster Outcomes Questionnaire needs further
evaluation. Our cohort did not include a cancer group that was not exposed to a natural
disaster, so we cannot conclude that the biological differences seen were due to a
cancer diagnosis and/or the hurricane.
Clinical Implications of the Study
We consider that cancer survivors can benefit from positive or appropriate social
support that in the case of patients can help reduce negative psychosocial comorbidities
and increase medical adherence. Several studies have highlighted the importance of
social support networks and how this support can help overcome a number of common
barriers to treatment, leading to better adjustment to the cancer diagnosis (33, 34). We
propose that it is of paramount importance to identify factors that promote and influence
resilience and well-being among Hispanic/Latinos facing a chronic and/or terminal
illness following natural disasters. Communities and relevant groups could help by
pinpointing other factors that may exacerbate cancer survivors perceived stress after a
natural disaster.
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Conclusions
Our findings support changes in public policy that includes plans to ensure prompt
access to treatment and specialists for cancer survivors and mitigate any barriers to
care in the aftermath of a hurricane. These processes will lead to better care and
promote population well-being in the face of natural disasters as it is essential for
stakeholders to consider the psychological needs of cancer survivors.
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Acknowledgements
We would like to acknowledge support from Hospital San Lucas, the PHSU/MCC U54
Partnership’s PRBB staff, Drs. Zuleika Diaz and Hector Velez, Rayo de Luz y
Esperanza and Esfuerzate y Se Valiente support groups.
Data Sharing
The data that support the findings of this study are available from the corresponding
author upon reasonable request.
Author Contributions
MR-R, IF, HJ, EMC, GNA-P analyzed data, performed statistical analyses and wrote the
manuscript. RH, ZR, NT, ET, AM collected psychosocial data, biological samples and
analyzed data. DA, LM, CBC-E and JP-M analyzed biological samples and performed
statistical analyses. All authors read and approved the final manuscript.
Figure Legends
Figure 1. Correlation network of psychosocial assessments in (A) cancer
and (B) non-cancer group. Only significant correlations are shown as determined by
qgraph. Green lines: positive correlations; red lines: negative correlations. Line
thickness shows the strength of the correlation. All comparisons shown are statistically
significant. P < 0.05. brr: barriers to care – total; bsk: barriers to care – skills; bmr:
barriers to care – marginalization; bxp: barriers to care – expectations; bkn: barriers to
care – knowledge and beliefs; bpr: barriers to care – pragmatics; dep: depressive
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symptomatology; anx: anxiety symptomatology; pts: post-traumatic stress disorder
symptoms; dst: distress; prc: perceived stress; rsl: resilience; grw: post-traumatic
growth; scs: perceived social support; otc: natural disaster outcomes.
Figure 2. Serum cytokine expression in cancer and non-cancer
participants. (A) Heatmap depicting cytokine expression among cancer and non-
cancer participants. (B) Individual cytokine differences between cancer and non-cancer
participants. Higher (red) to lower (green) differences among groups. (C) Volcano plot
depicting cytokine changes (x-axis) and p-values (statistical significance was
established as p < 0.01; y-axis). (D) String diagram depicting molecular relationships,
interactions, and pathway associations between significantly upregulated cytokines.
Figure 3. Correlation of top significantly expressed cytokines with
psychosocial measurements. Top differentially expressed cytokines (by P-value)
were subjected to Spearman correlation analyses to identify associations with
psychosocial measurements among (A) whole cohort, (B) non-cancer participants and
(C) cancer group. White squares = no significant association; Blue circles = significant
positive association; Red = significant negative associations. Color intensity reflects
stronger associations as determined by the correlation coefficient value (Y-axis). P <
0.05. bknow: barriers to care – knowledge and belief; bmargin: barriers to care –
marginalization; barriers: barriers to care - total; bexpect: barriers to care –
expectations; bskills: barriers to care – skills; bprag: barriers to care: pragmatics; ssp:
social support; pss: perceived stress; dss: distress; ptsd: post-traumatic stress
syndrome symptoms; dep: depressive symptomatology; anx: anxiety symptoms; res:
resilience; growth: post-traumatic growth; outcome: natural disaster outcomes.
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Table 1. Clinical, demographical and psychosocial measurements from study participants.
P-value
Age (mean, range) 0.001
Sex (male/female) 0.184
Race
Type of cancer
Breast
Prostate
Endometrial
Cervix
Lung
Other areas
Assessments Mean (SD) Median (IQR) % Mean (SD) Median (IQR) % P-value
Natural disaster outcomes 21.08 (16.90) 17.00 (8.00-
32.25) 96.0 28.96 (23.57) 26.00 (9.00-
43.00) 98.0 0.147
No water 1.24 (1.65) 0.00 (0.00-3.00) 44.0 2.06 (1.70) 2.00 (0.00-4.00) 68.0 0.009
Food insecurity 0.76 (1.33) 0.00 (0.00-1.00) 38.0 1.44 (1.53) 1.00 (0.00-3.00) 54.0 0.003
Difficulties accessing roads 0.98 (1.39) 0.00 (0.00-2.00) 42.0 1.60 (1.54) 1.50 (0.00-3.00) 62.0 0.025
Family insecurity 0.28 (0.76) 0.00 (0.00-0.00) 16.0 0.86 (1.34) 0.00 (0.00-1.25) 38.0 0.012
Depression symptoms 7.18 (5.70) 7.00 (3.0-10.25) 5.92 (5.61) 4.00 (1.75-9.00) 0.207
Moderate to severe (n, %) 14 (28%) 10 (20%)
Anxiety symptoms 5.96 (5.12) 4.50 (2.00-7.50) 5.60 (5.11) 4.00 (1.75-9.75) 0.63
Moderate to severe (n, %) 11 (22%) 12 (24%)
Post-traumatic stress
symptoms
‡
16.82 (16.03) 12.00 (5.00-
24.25) 14.69 (16.60) 10.00 (2.00-
20.50) 0.247
Scores >33 (cut-off) (n, %) 9 (18%) 7 (14%)
Emotional distress
†#
3.94 (3.12) 4.00 (1.00-7.00) 3.98 (3.10) 4.00 (1.00-6.75) 0.941
High lev els of distress
†#
(n, %) 7 (15%) 7 (15%)
Perceived stress (PS)
‡
15.52 (7.11) 16.00 (11.00-
20.00) 14.76 (7.55) 16.00 (9.50-
18.50) 0.539
Moderate to high PS
‡
(n, %) 32 (64%) 31 (63%)
Res ilience 3.53 (0.89) 3.33 (2.83-4.33) 3.42 (0.70) 3.33 (2.83-3.83) 0.612
High resilience (n, %) 13 (26%) 7 (14%)
Post-traumatic growth 35.00 (13.12) 40.00 (28.75-
45.00) 37.48 (9.88) 39.50 (33.75-
44.25) 0.725
Great/very great growth (n, %) 32 (64%) 37 (74%)
Perceived social support 10.90 (7.87) 9.50 (4.75-16.25) 8.80 (7.69) 7.00 (2.75-14.00) 0.158
Barriers in access to care
†§
18.75 (19.94) 10.26 (3.21-
30.13) 30.6 11.47 (12.33) 6.41 (1.28-18.43) 14.6 0.082
Skills 10.69 (14.06) 4.69 (0.00-18.75) 18.0 4.94 (9.39) 0.00 (0.00-6.25) 2.0 0.075
Marginalization
‡
16.86 (21.74) 9.09 (0.00-24.43) 24.0 9.69 (13.16) 2.27 (0.00-17.05) 18.4 0.074
Expectations
§
24.34 (31.97) 7.14 (0.00-41.07) 30.6 16.64 (21.93) 8.93 (0.00-23.21) 24.0 0.349
Knowledge and beliefs 18.75 (28.60) 6.25 (0-26.56) 28.0 11.63 (18.52) 0.00 (0.00-18.75) 18.0 0.384
Pragmatics
†
24.06 (21.62) 16.67 (4.86-
38.89) 44.0 17.59 (17.12) 11.11 (0.69-
29.86) 35.4 0.125
26 (52%)
Characteristics Cancer (n=50) Non-Cancer
7 (14%)
8 (16%)
57 (25-87)
11/39
Hispanic (50/50)
n (%)
4 (8%)
3 (6%)
2 (4%)
‡Non-Cancer, n=49; †Non-Cancer, n=48; §Cancer, n=49; #Cancer, n=48
17/33
Hispanic (50/50)
48 (22-73)
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