Background
Disparities in cancer outcomes are often influenced by social determinants of health, including access to health insurance, financial resources, housing, transportation, and language interpretation services.1–3 Treatment completion is critical for successful cancer care and is directly linked to social determinants of health.2,4–8 Quality of life (QoL), depression symptoms, and stress also impact treatment adherence and disparities in cancer outcomes.9–11
Patient navigation (PN) helps address barriers to cancer care in vulnerable populations,12 and multidisciplinary models may improve treatment adherence.13,14 Multidisciplinary programs target multiple aspects of patient needs, requiring care coordination across multiple services, such as social services, legal aid, medical specialists, primary care, psychotherapists, language access, and transportation.13,14
A systematic review of PN found mixed evidence on treatment completion outcomes, with most studies focused on screening and diagnosis.15 In 2 randomized controlled trials (RCTs) of postdiagnosis PN, no significant differences were observed in treatment completion between the intervention and control groups.15 Fiscella et al16 examined outcomes among patients with breast and colorectal cancer at a community cancer clinic and a safety-net hospital, finding 100% treatment completion in both the intervention (multidisciplinary PN) and usual and customary care (U&C) groups at 3 months. Ell et al17 examined mostly Spanish-speaking immigrant women with breast or gynecologic cancer randomized to receive either telephone-based PN (barrier assessment with education and coaching, with follow-up based on need) or enhanced usual care (written education materials, Medicaid facilitation, and supportive services). At 12 months, treatment completion for patients with gynecologic cancer in the PN arm ranged from 84% among those receiving radiation therapy (RT; vs 87% for enhanced usual care) to 94% among those receiving chemotherapy (vs 87% for enhanced usual care).17 Further RCTs examining the impact of comprehensive multidisciplinary postdiagnosis PN on treatment completion across a variety of cancers are needed to strengthen the evidence base.
The Integrated Cancer Care Access Network (ICCAN) is a multidisciplinary program that provides PN and facilitates access to essential needs for underserved, high-need patients in New York City (NYC) safety-net hospital cancer clinics and comprehensive cancer centers.9,14,18–22 Safety-net hospitals serve patients regardless of insurance, socioeconomic, or immigration status.23 Their patients may be described as “medically underserved,” with inadequate geographic or socioeconomic access to medical care.24 Guided by Glasgow’s logic model,25 we hypothesized that ICCAN may influence predisposing, moderating, and contextual factors by addressing patients’ social and physical environments. Through one-on-one assistance in accessing multilevel social and economic support, ICCAN aims to mitigate social, environmental, and economic barriers; enhance patients’ self-efficacy and self-management; and improve treatment completion and psychosocial outcomes. To address the evidence gap on postdiagnosis PN and treatment completion, we conducted an RCT comparing outcomes between patients receiving the ICCAN intervention versus U&C.
Patients and Methods
This study followed the guidelines of the Declaration of Helsinki, with all procedures approved by the Memorial Sloan Kettering Cancer Center Institutional Review Board/Privacy Board and each study site (ClinicalTrials.gov identifier: NCT01742143). Participants provided written informed consent.
Study Design
This RCT examined the ICCAN intervention versus U&C in 152 medically underserved patients with cancer in NYC (May 2013–October 2016; trial concluded at budget’s end). Outcomes were assessed at 12 months. The study was conducted at 2 NYC safety-net cancer clinics serving large medically underserved populations (see Appendix 1 in the supplementary materials for setting details).
ICCAN Arm
The ICCAN intervention addressed social determinants of health, including essential needs (food, housing, financial support) and other socioeconomic barriers to cancer care among underserved patients. ICCAN was developed from a program that began among underserved patients in the NYC municipal hospital system in 2008 (funded by charitable grants and institutional support). Essential components of ICCAN included a comprehensive needs assessment, one-on-one Access Facilitator (AF) assistance, identification of targeted resources, help (including follow-up) applying for resources, regular follow-up to identify and address new/evolving needs, and cultural and linguistic tailoring.
At enrollment, ICCAN participants completed a comprehensive baseline paper-based needs assessment (Intake Survey) administered by a bilingual AF. See the Supplementary Materials for ICCAN Needs Assessment and AF training details (Appendix 1) and a comparison of ICCAN versus U&C services at each site (Supplementary Table S1).
U&C Arm
Participants received their treatment facility’s U&C through clinician referral (ie, no screening or formal needs assessment). Both sites offered cancer treatment support services to patients in both the U&C and ICCAN arms as available. Site 1 provided a cancer clinic social worker, PN for appointment scheduling, and peer support groups for patients with breast and prostate cancer. Site 2 had a part-time social worker, PN for appointment scheduling, and a nutritionist. Service availability varied based on staffing levels and patterns. Both sites offered written education materials in English and Spanish. U&C services had no dedicated focus on assessing and addressing patients’ essential needs, which is an integral part of ICCAN’s PN/access facilitation program.
Participant Eligibility
Patients were eligible if they were aged 21 through 80 years, fluent in English and/or Spanish, and had stage I–III cancer of any type (expanded from only lung, breast, or colorectal in August 2013 for broader sample representation). They also had to meet one of the following criteria: (1) received chemotherapy within the past month and/or RT within the past week before study enrollment or (2) were scheduled to start chemotherapy or RT within the next 2 months. Additional criteria included planning to remain in the area for at least 12 months and not having a family member already enrolled in ICCAN. Potentially eligible patients were identified via electronic health record (EHR) and approached in-person at clinic appointments by trained study staff or referred by clinicians. Those who screened as eligible and provided consent were enrolled.
Randomization Procedures
Immediately after informed consent, study staff randomized patients to ICCAN or U&C using random permuted blocks (1:1 ratio). Randomization was unblinded and stratified by treatment site (2 sites), insurance type (private insurance vs all others), and cancer type (breast, prostate, gynecologic, blood, other).
Measured Outcomes
Primary Outcome
The treatment completion outcome (provider-reported completion of chemotherapy/RT; no other treatment types, such as hormonal, surgery, or immunotherapy, were recorded or analyzed) was abstracted from the medical record by trained staff at 12 months of participation (dichotomous variable: completed/did not complete treatment). For chemotherapy, completion was defined as receipt of the last required cycle or ≥85% relative dose intensity if treatment extended beyond 12 months or delays occurred due to toxicities. For RT, treatment completion was defined as receipt of the last required cycle; if treatment lasted beyond 12 months, completion at 12 months was used as a proxy. For combined chemotherapy and RT, treatment completion was defined as completion of both therapies. Treatment interruptions or stoppages without medical cause were considered incomplete. All 152 participants had complete chart abstractions.
Secondary Outcomes
All secondary outcomes were measured at 12 months, compared with baseline measurements, and scored using continuous scales.
QoL was assessed using the EuroQol 5-Dimension (EQ-5D) with the visual analogue scale (VAS), validated in English (Cronbach’s α=.63)26 and Spanish (α=.75).27 The VAS, resembling a thermometer, ranges from 0 (worst imaginable health) to 100 (best imaginable health),28 with a minimally important difference (MID) of 7 to 12.29
Depression symptoms were assessed using the Patient Health Questionnaire (PHQ-9), a 9-item self-report version of the Primary Care Evaluation of Mental Disorders (PRIME-MD) interview that uses the DSM-IV psychiatric diagnostic criteria for major depression, validated in English (α=.84–.89)30 and Spanish (α=.90),31 in primary care and cancer populations.32 The PHQ-9 includes questions about the frequency of experiencing little interest or pleasure in activities and feeling down, depressed, or hopeless.30 Scores of <5 indicate minimal depression symptoms, and threshold scores of 5, 10, 15, and 20 indicate mild, moderate, moderately severe, and severe depression symptoms, respectively.30 The MID for PHQ-9 scores is 3 to 5 points.33
Stress was assessed using the Modified Perceived Stress Scale (PSS-4), a 4-item version of the Perceived Stress Scale with validated versions in English34 (α=.60–.82)35 and Spanish (α=.74).36 The PSS has been conceptualized as a measure of secondary appraisal that assesses individuals’ feelings of being in control and the extent to which they believe current demands and experiences are overwhelming.34 The modified version consists of the 4 items that correlate most highly with the original 14-item scale. Probes include the frequency in the past month of feeling unable to control important things in life and of feeling confidently able to handle personal problems. Each item is scored on a scale of 0 to 4, yielding an overall score range of 0 to 16, with higher scores indicating greater stress. A MID for PSS-4 scores has not been established.37
Twelve-month follow-up assessments were conducted in person or by phone, depending on patient preference. Calls were made on weekdays, evenings, and weekends. If a patient was known to have a scheduled clinic appointment, we attempted to meet them there.
Statistical Analyses
Categorical variables were summarized using frequencies and percentages, with chi-square tests used for comparisons. Continuous variables were summarized using descriptive statistics (mean [SD]), with independent samples t tests used for comparisons. An α<.05 indicated statistical significance for all calculations.
An intention-to-treat (ITT) analysis was performed for the primary outcome of treatment completion. Based on a sample size of 150 patients per arm, we calculated 83% power to detect a difference in treatment completion rates between arms (estimating 75% U&C completion and 88% ICCAN). The statistical power was estimated by a binomial test of 2 independent proportions with a type-I error rate of 0.05, as implemented in the power.prop.test function in the R statistical language (R Core Team, 2023).
The proportion of patients who completed treatment was compared between intervention groups using a chi-square test with continuity correction. Independent samples t tests were used to compare changes from baseline to follow-up among arms for EQ-5D, PHQ-9, and PSS-4 scores between intervention conditions. Listwise deletion was used for missing data.38 Cohen’s d was calculated for effect sizes (ES; changes in each patient-reported outcome from baseline to 12 months), representing the standardized difference between 2 group means, with values of 0.2, 0.5, and 0.8 considered small, medium, and large ESs, respectively.39 All statistical analyses were conducted using SPSS Statistics, version 24 (IBM Corp.).
Results
Of the patients approached (n=285), 77 did not complete the eligibility screeners and 45 did not meet inclusion criteria (family members enrolled in ICCAN, n=6; not planning to remain in the area for a year, n=12; not currently receiving treatment, n=14; stage 4 diagnosis, n=7; and not fluent in English/Spanish, n=6). A total of 11 patients refused to participate. There were no significant differences in gender, race, ethnicity, or education between those who completed screening without enrolling (n=56) and those who enrolled. In total, 152 patients enrolled, with 76 randomized to U&C and 76 to ICCAN (Figure 1).
CONSORT flow diagram.
Abbreviation: ICCAN, Integrated Cancer Care Access Network.
Citation: Journal of the National Comprehensive Cancer Network 2025; 10.6004/jnccn.2025.7017
A total of 93 (63%) participants were female and 55 (37%) were male, with an average age of 54 years (SD, 13.15) (Table 1; missing values were excluded in the analyses). Most (n=131; 86%) had individual monthly income ≤$1,629. Participants self-identified as Black (n=64; 46%), “Other” (n=63; 46%), White (n=10; 7%), or Asian (n=2; 1%), and 76 (52%) identified as Hispanic/Latino. Regions of birth included the United States (n=62, including 14 born in Puerto Rico; 43%), Caribbean and Latin America (45%), Asia (4%), and Africa (1%). Most participants preferred English (n=93; 64%), and 36% preferred Spanish. Twelve (8%) had ≤5 years of formal education, 15 (11%) had 6 to 8 years, and 38 (27%) had 9 to 11 years; 28 (20%) had graduated from high school, and 48 (34%) had at least some college education. At study enrollment, 73 (50%) participants were employed full- or part-time, 50 (34%) were unemployed, and 17 (12%) were retired.
Patient Characteristicsa
| Characteristic | Total n (%) |
U&C Arm n (%) |
ICCAN Arm n (%) |
P Valueb |
|---|---|---|---|---|
| Total, n | 152 | 76 | 76 | |
| Gender | .865 | |||
| Male | 55 (37) | 28 (38) | 27 (36) | |
| Female | 93 (63) | 46 (62) | 47 (64) | |
| Age, mean [SD], y | 54 [13.15] | 56 [16.17] | 53 [12.14] | .789 |
| Individual monthly income | .754 | |||
| $0 | 11 (7) | 6 (8) | 5 (7) | |
| $1–$1,629 | 131 (86) | 64 (84) | 69 (88) | |
| >$2,256 | 10 (7) | 6 (8) | 4 (5) | |
| Race | .108 | |||
| White | 10 (7) | 2 (3) | 8 (11) | |
| Black | 64 (46) | 29 (43) | 35 (49) | |
| Asian | 2 (1) | 0 (0) | 2 (3) | |
| Other | 63 (46) | 37 (54) | 26 (37) | |
| Ethnicity | .408 | |||
| Hispanic/Latino | 76 (52) | 41 (56) | 35 (48) | |
| Non-Hispanic/Latino | 70 (48) | 32 (44) | 38 (52) | |
| Region of origin | .464 | |||
| United States | 62 (43) | 28 (39) | 34 (47) | |
| Caribbean and Latin America | 64 (45) | 36 (51) | 28 (52) | |
| Africa | 1 (1) | 1 (1) | 0 (0) | |
| Asia | 6 (4) | 3 (4) | 3 (4) | |
| Other | 10 (7) | 3 (4) | 7 (10) | |
| English proficiency | .25 | |||
| English proficient | 81 (55) | 38 (52) | 43 (57) | |
| Limited English proficient | 67 (45) | 35 (48) | 32 (43) | |
| Language preference | .521 | |||
| English | 93 (64) | 44 (61) | 49 (66) | |
| Spanish | 53 (36) | 28 (39) | 25 (34) | |
| Educational attainment | .603 | |||
| None–2nd grade | 3 (2) | 3 (4) | 0 (0) | |
| 3rd–5th grade | 9 (6) | 6 (9) | 3 (4) | |
| 6th–8th grade | 15 (11) | 9 (13) | 6 (9) | |
| 9th–11th grade | 38 (27) | 17 (24) | 21 (30) | |
| High school graduate | 28 (20) | 11 (16) | 17 (24) | |
| Some college or more | 48 (34) | 24 (34) | 24 (31) | |
| Employment status (baseline) | .739 | |||
| Full-time | 45 (31) | 20 (28) | 25 (34) | |
| Part-time | 28 (19) | 7 (10) | 9 (12) | |
| Retired | 17 (12) | 11 (15) | 6 (8) | |
| Student | 1 (1) | 1 (1) | 0 (0) | |
| Unemployed | 50 (34) | 23 (32) | 26 (36) | |
| Other | 4 (3) | 3 (4) | 1 (1) | |
| Insurance coverage | .766 | |||
| Yes | 136 (92) | 69 (91) | 67 (93) | |
| No | 12 (8) | 7 (9) | 5 (7) | |
| Insurance type | .534 | |||
| Emergency Medicaid | 19 (16) | 9 (14) | 10 (17) | |
| Medicaid - HMO | 65 (53) | 35 (56) | 30 (51) | |
| Medicaid (fee for service/Medicaid only) | 12 (10) | 8 (13) | 4 (7) | |
| Medicare | 10 (8) | 4 (6) | 6 (10) | |
| Medicaid and Medicare | 10 (8) | 5 (8) | 5 (9) | |
| Private HMO/PPO | 6 (5) | 2 (3) | 4 (7) | |
| Cancer type | .941 | |||
| Breast | 80 (55) | 39 (54) | 41 (55) | |
| Gynecologicc | 25 (17) | 13 (18) | 12 (16) | |
| Prostate | 31 (21) | 15 (21) | 16 (22) | |
| Otherd | 10 (7) | 5 (7) | 5 (7) | |
| Cancer stage | .064 | |||
| I | 27 (35) | 6 (18) | 21 (48) | |
| II | 25 (32) | 16 (49) | 9 (20) | |
| III | 25 (32) | 11 (33) | 14 (32) | |
| Treatment modality | .711 | |||
| Radiotherapy | 45 (31) | 21 (28) | 24 (33) | |
| Chemotherapy | 53 (36) | 29 (39) | 24 (33) | |
| Chemotherapy and radiotherapy | 49 (33) | 24 (32) | 25 (34) | |
| Comorbid medical conditions | .897 | |||
| ≥1 | 72 (59) | 36 (58) | 36 (60) | |
| None | 50 (41) | 26 (42) | 24 (40) | |
| Study site | .879 | |||
| Site 1 | 121 (80) | 61 (80) | 60 (79) | |
| Site 2 | 31 (20) | 15 (20) | 16 (21) | |
| USDA score,e mean [SD] | .912 | |||
| Baseline (n=132) | 4 [2.674] | 4 [4.163] | 4 [3.307] | |
Abbreviations: HMO, health maintenance organization; ICCAN, Integrated Cancer Care Access Network; PPO, preferred provider organization; U&C, usual and customary care; USDA, United States Department of Agriculture Household Food Security Survey Module.
Missing values were excluded from analyses.
P values considered significant at α=.05.
Cervical, endometrial, ovarian.
Gastrointestinal cancer (n=3), lung cancer (n=3), blood cancer (n=2), other cancer (n=2).
Raw household scores ≥3 are classified as food insecure, according to USDA guidelines.
Most participants (n=136; 92%) had health insurance at enrollment. Of the 122 who reported their insurance type, 16% had Medicaid for the Treatment of an Emergency Medical Condition (Emergency Medicaid), 63% had another type of Medicaid, 8% had Medicare, 8% had both Medicaid and Medicare, and 5% had private insurance. At study enrollment, 45 (31%) participants were receiving or about to start RT alone, 53 (36%) were receiving chemotherapy alone, and 49 (33%) were receiving both RT and chemotherapy. Most patients (n=72; 59%) had ≥1 comorbid medical condition.
There were no clinically meaningful differences in the distribution of baseline demographic and medical characteristics between arms, except for a small imbalance in cancer stage.
The primary outcome of treatment completion was assessed for the original 76 patients in each arm. Most (n=129; 85%) participants completed their cancer treatment by 12 months (Table 2). The ICCAN intervention had a statistically significant effect on treatment completion (P<.05), with 70 (92%) participants in the ICCAN arm completing their cancer treatment compared with 59 (78%) in the U&C arm. The list of reasons for noncompletion of treatment (not medical or facility-related) included personal preference/events (33.4%), family-related events (28.4%), and no reason specified (13.5%). No deaths were reported among those who did not complete treatment.
Primary Outcome (Treatment Completion) After 12 Months of Trial Participation
| Primary Outcome | Overall (n=152) n (%) |
U&C Arm (n=76) n (%) |
ICCAN Arm (n=76) n (%) |
P Valuea |
|---|---|---|---|---|
| Treatment completion | .022 | |||
| Yes | 129 (85) | 59 (78) | 70 (92) | |
| No | 23 (15) | 17 (22) | 6 (8) |
Abbreviations: ICCAN, Integrated Cancer Care Access Network; U&C, usual and customary care.
P values considered significant at α≤.05.
A total of 90 patients were contacted via telephone for secondary outcomes follow-up, with no significant differences between arms or in the baseline characteristics of those reached by telephone versus not reached during the follow-up window (see Supplementary Table S2). At 12-month follow-up, the ICCAN arm showed significantly greater improvements in EQ-5D (QoL) scores (P=.001; ES, 1.47), PHQ-9 (depression symptom) scores (P=.046; ES, 1.33), and PSS-4 (stress) scores (P=.001; ES, 1.13) compared with the U&C arm (Table 3). The differences in scores from baseline to follow-up in both arms exceeded the MIDs for the EQ-5D (U&C, +12 [ES, 0.75]; ICCAN, +25 [ES, 1.47]) and PHQ-9 (U&C, –5 [ES, 1.06]; ICCAN, –6 [ES, 1.33]). The ESs for the differences in PSS-4 scores from baseline to follow-up were 0.29 in the U&C arm and 1.13 in the ICCAN arm.
Secondary Study Outcomes, Changes From Baseline at 12 Months of Trial Participationa
| Overall (n=90) Mean [SD] |
U&C Arm (n=46) Mean [SD] |
ICCAN Arm (n=44) Mean [SD] |
P Valuec | |
|---|---|---|---|---|
| EQ-5D VAS | .001 | |||
| Baseline | 64 [19.16] | 64 [17.62] | 62 [19.76] | |
| Follow-up | 81 [18.54] | 76 [14.62] | 87 [14.31] | |
| ES (95% CI)b | 0.96 (0.65–1.26) | 0.75 (0.32–1.16) | 1.47 (0.96–1.87) | |
| PHQ-9 | .046 | |||
| Baseline | 12 [5.22] | 12 [5.14] | 11 [5.39] | |
| Follow-up | 6 [4.09] | 7 [4.32] | 5 [3.61] | |
| ES (95% CI)b | 1.29 (0.93–1.59) | 1.06 (0.60–1.49) | 1.33 (0.78–1.75) | |
| PSS-4 | .001 | |||
| Baseline | 10 [3.34] | 9 [4.73] | 10 [2.79] | |
| Follow-up | 8 [4.09] | 8 [2.16] | 6 [4.32] | |
| ES (95% CI)b | 0.54 (0.23–0.83) | 0.29 (0.17–0.66) | 1.13 (0.62–1.50) | |
Abbreviations: EQ-5D, EuroQol 5-Dimension; ES, effect size; ICCAN, Integrated Cancer Care Access Network; PHQ-9, Patient Health Questionnaire-9; PSS-4, 4-item Perceived Stress Scale; U&C, usual and customary care; VAS, visual analogue scale.
Decreased scores at follow-up indicate improvements in PSS-4 and PHQ-9 outcomes; increased scores at follow-up indicate improvements in EQ-5D outcomes.
Cohen’s d effect size for repeated measures.
Based on change from baseline to follow-up. P values considered significant at α=.05.
Discussion
In this study of underserved English- and Spanish-speaking patients treated for cancer at 2 safety-net cancer clinics, patients randomized to a comprehensive, multidisciplinary, language-concordant PN-essential needs intervention had significantly improved 12-month treatment completion rates than those who received their institution’s U&C care. There was a sizeable difference in treatment completion proportion between the U&C (78%) and ICCAN (92%) groups. QoL, depression, and stress scores were significantly more improved in the ICCAN compared with the U&C arm, echoing the findings of the community health worker/PN study by Patel et al.40 Patients in our study’s ICCAN arm had an average of 11 contacts with AFs over 12 months, with the AFs facilitating all services themselves without hand-off to social workers or other staff.
To the best of our knowledge, there have been only 2 prior PN RCTs that examined postdiagnosis PN treatment completion outcomes, neither of which demonstrated a significant impact of PN on treatment completion.16,17 Fiscella et al’s16 3-month study may have been too short to demonstrate a difference. Ell et al’s17 study had a 12-month duration, but 8.8% of patients were excluded from the treatment completion analyses because their charts were unavailable (our ITT analysis did not exclude any patients). The treatment completion rate in the control arm of our trial (78% at 12 months) was lower than in these other 2 studies (100% at 3 months16 and ≥87% at 12 months17), indicating potentially higher treatment completion barriers in our population.41 In our prior research, 86% of underserved patients with ≥4 socioeconomic needs identified at PN intake reported that navigation services helped them attend treatment appointments at follow-up.14
A large proportion of patients were lost to follow-up for secondary outcomes. Some patients may no longer feel engaged with the study after completing treatment and/or leaving the area, making them difficult to contact. Continued follow-up attempts were discontinued when research funding ended. Ell et al17 had a 32% loss to follow-up at 12 months. Future trial designs should account for the potential of significant long-term loss to follow-up.
Although some time has passed since our trial closed, the evidence remains relevant for informing the development of programs to effectively assist our most vulnerable patients. Universal health-related social needs (HRSN) screening is gaining momentum in hospitals throughout the United States, partly propelled by inpatient quality reporting requirements.42 NYC hospitals now widely use HRSN screening technology, and new state Medicaid funding is available for HRSN screening, data collection, referrals, and services.42,43 However, self-administered screening and “light touch” resource referral approaches (eg, providing patients with resource lists) are not recommended for vulnerable patients with cancer, such as those with limited English proficiency.44 Therefore, staffing investments may be needed for one-on-one assessment, PN, and regular follow-up of the most vulnerable patients. Additional literature is still needed on the impacts of delivering such services.
This study has several limitations. The study’s sample size and power were insufficient to enable confounder or subgroup analyses, respectively, limiting generalizability. U&C patients had access to some supportive resources through their treating facility and to written materials (there is no consistent U&C in the fragmented US health care system), precluding an entirely untreated control comparison. U&C arm resource uptake was not tracked. There was no significant difference in treatment completion by stage; however, there were slightly more patients with stage I disease in the ICCAN arm and slightly more patients with stage II disease in the U&C arm, which may have introduced bias, given that stage I cancer can have a shorter treatment course. The high degree of loss to follow-up for secondary outcomes could have introduced selection bias toward patients with more favorable secondary outcomes. Future studies should include a larger patient sample, track resource uptake in all arms, and gather more detailed data on treatment noncompletion reasons.
Conclusions
Multidisciplinary, culturally, and linguistically tailored PN programs that address a range of patients’ needs can be effective in improving treatment completion and psychosocial outcomes among the most vulnerable patients with cancer, who are otherwise at elevated risk of disease progression and cancer-related mortality. These programs should be implemented at multiple levels to create a sustainable ecosystem of transdisciplinary care, supporting individual patients throughout the cancer care continuum. Our results can be used to improve treatment completion and psychosocial outcomes for underserved patients with cancer.
Acknowledgments
The authors thank Sonya J. Smyk, MA, at Memorial Sloan Kettering Cancer Center, for writing and editorial support. She was not compensated beyond her regular salary.
References
- 1.↑
Minas TZ, Kiely M, Ajao A, Ambs S. An overview of cancer health disparities: new approaches and insights and why they matter. Carcinogenesis 2021;42:2–13.
- 2.↑
Zavala VA, Bracci PM, Carethers JM, et al. Cancer health disparities in racial/ethnic minorities in the United States. Br J Cancer 2021;124:315–332.
- 3.↑
Genoff MC, Zaballa A, Gany F, et al. Navigating language barriers: a systematic review of patient navigators' impact on cancer screening for limited English proficient patients. J Gen Intern Med 2016;31:426–434.
- 4.↑
Thompson B, Hohl SD, Molina Y, et al. Breast cancer disparities among women in underserved communities in the USA. Curr Breast Cancer Rep 2018;10:131–141.
- 5.↑
Bandera EV, Lee VS, Rodriguez-Rodriguez L, et al. Racial/ethnic disparities in ovarian cancer treatment and survival. Clin Cancer Res 2016;22:5909–5914.
- 6.↑
Ahmed S, Ahmad I, Zhu T, et al. Early discontinuation but not the timing of adjuvant therapy affects survival of patients with high-risk colorectal cancer: a population-based study. Dis Colon Rectum 2010;53:1432–1438.
- 7.↑
Hershman D, McBride R, Jacobson JS, et al. Racial disparities in treatment and survival among women with early-stage breast cancer. J Clin Oncol 2005;23:6639–6646.
- 8.↑
Hershman DL, Kushi LH, Shao T, et al. Early discontinuation and nonadherence to adjuvant hormonal therapy in a cohort of 8,769 early-stage breast cancer patients. J Clin Oncol 2010;28:4120–4128.
- 9.↑
Costas-Muniz R, Leng J, Diamond L, et al. Psychosocial correlates of appointment keeping in immigrant cancer patients. J Psychosoc Oncol 2015;33:107–123.
- 10.↑
Hui SK, Miller SM, Wen KY, et al. Psychosocial barriers to follow-up adherence after an abnormal cervical cytology test result among low- income, inner-city women. J Prim Care Community Health 2014;5:234–241.
- 11.↑
Arrieta O, Angulo LP, Núñez-Valencia C, et al. Association of depression and anxiety on quality of life, treatment adherence, and prognosis in patients with advanced non-small cell lung cancer. Ann Surg Oncol 2013;20:1941–1948.
- 12.↑
Wells KJ, Battaglia TA, Dudley DJ, et al. Patient navigation: state of the art or is it science? Cancer 2008;113:1999–2010.
- 13.↑
Castaldi M, Safadjou S, Elrafei T, McNelis J. A multidisciplinary patient navigation program improves compliance with adjuvant breast cancer therapy in a public hospital. Am J Med Qual 2017;32:406–413.
- 14.↑
Gany F, Ramirez J, Nierodzick ML, et al. Cancer portal project: a multidisciplinary approach to cancer care among Hispanic patients. J Oncol Pract 2011;7:31–38.
- 15.↑
Bernardo BM, Zhang X, Beverly Hery CM, et al. The efficacy and cost- effectiveness of patient navigation programs across the cancer continuum: a systematic review. Cancer 2019;125:2747–2761.
- 16.↑
Fiscella K, Whitley E, Hendren S, et al. Patient navigation for breast and colorectal cancer treatment: a randomized trial. Cancer Epidemiol Biomarkers Prev 2012;21:1673–1681.
- 17.↑
Ell K, Vourlekis B, Xie B, et al. Cancer treatment adherence among low- income women with breast or gynecologic cancer: a randomized controlled trial of patient navigation. Cancer 2009;115:4606–4615.
- 18.↑
Ayash C, Costas-Muñiz R, Badreddine D, et al. An investigation of unmet socio-economic needs among Arab American breast cancer patients compared with other immigrant and migrant patients. J Community Health 2018;43:89–95.
- 19.↑
Costas R, Gany F. Depressive symptoms in a sample of Afro-Caribbean and Latino immigrant cancer patients: a comparative analysis. Support Care Cancer 2013;21:2461–2468.
- 20.↑
Costas-Muniz R, Leng J, Aragones A, et al. Association of socioeconomic and practical unmet needs with self-reported nonadherence to cancer treatment appointments in low-income Latino and Black cancer patients. Ethn Health 2016;21:118–128.
- 21.↑
Costas-Muniz R, Sen R, Leng J, et al. Cancer stage knowledge and desire for information: mismatch in Latino cancer patients? J Cancer Educ 2013;28:458–465.
- 22.↑
Gany F, Lee T, Ramirez J, et al. Do our patients have enough to eat?: Food insecurity among urban low-income cancer patients. J Health Care Poor Underserved 2014;25:1153–1168.
- 23.↑
Chatterjee P, Sommers BD, Maddox KEJ. Essential but undefined – reimagining how policymakers identify safety-net hospitals. N Engl J Med 2020;383:2593–2595.
- 24.↑
National Center for Advancing Translational Sciences (NCATS). Underserved group. Accessed January 28, 2025. Available at: https://toolkit.ncats.nih.gov/glossary/underserved-group/
- 25.↑
Glasgow RE. Perceived barriers to self-management and preventive behaviors. Accessed January 28, 2025. Available at: https://cancercontrol.cancer.gov/brp/research/constructs/perceived-barriers
- 26.↑
Khanna R, Jariwala K, Bentley JP. Psychometric properties of the EuroQol Five Dimensional Questionnaire (EQ-5D-3L) in caregivers of autistic children. Qual Life Res 2013;22:2909–2920.
- 27.↑
Fernández-de-Las-Peñas C, Rodríguez-Jiménez J, Moro-López-Menchero P, et al. Psychometric properties of the Spanish version of the EuroQol-5D-5L in previously hospitalized COVID-19 survivors with long COVID. Sci Rep 2022;12:12605.
- 28.↑
EuroQol Group. EuroQol—a new facility for the measurement of health-related quality of life. Health Policy 1990;16:199–208.
- 29.↑
Pickard AS, Neary MP, Cella D. Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer. Health Qual Life Outcomes 2007;5:70.
- 30.↑
Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606–613.
- 31.↑
Errazuriz A, Beltrán R, Torres R, Passi-Solar A. The validity and reliability of the PHQ-9 and PHQ-2 on screening for major depression in Spanish speaking immigrants in Chile. Int J Environ Res Public Health 2022;19:13975.
- 32.↑
Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA 1999;282:1737–1744.
- 33.↑
Kroenke K, Stump TE, Chen CX, et al. Minimally important differences and severity thresholds are estimated for the PROMIS depression scales from three randomized clinical trials. J Affect Disord 2020;266:100–108.
- 34.↑
van Eck M, Nicolson NA, Berkhof J. Effects of stressful daily events on mood states: relationship to global perceived stress. J Pers Soc Psychol 1998;75:1572–1585.
- 35.↑
Lee EH. Review of the psychometric evidence of the Perceived Stress Scale. Asian Nurs Res (Korean Soc Nurs Sci) 2012;6:121–127.
- 36.↑
Remor E. Psychometric properties of a European Spanish version of the Perceived Stress Scale (PSS). Span J Psychol 2006;9:86–93.
- 37.↑
Patterson S, Trupin L, Hartogensis W, et al. Perceived stress and prediction of worse disease activity and symptoms in a multiracial, multiethnic systemic lupus erythematosus cohort. Arthritis Care Res (Hoboken) 2023;75:1681–1689.
- 39.↑
Cohen J. Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Lawrence Erlbaum; 1988.
- 40.↑
Patel MI, Kapphahn K, Wood E, et al. Effect of a community health worker-led intervention among low-income and minoritized patients with cancer. J Clin Oncol 2024;42:518–528.
- 41.↑
Henning Santiago AL. NY has the richest, poorest, smallest, most unequal congressional districts. Accessed April 6, 2022. Available at: https://www.cityandstateny.com/politics/2019/12/ny-has-the-richest-poorest-smallestmost-unequal-congressional-districts/176658
- 42.↑
Greater New York Hospital Association. Health-Related Social Needs Coding and Referral Guide. Accessed March 3, 2025. Available at: https://www.gnyha.org/wp-content/uploads/2022/12/SDH-Coding-Referral-Guide-2024.pdf
- 43.↑
Findhelp. Findhelp expands work with NYC providers and health systems prior to launch of the NY State 1115 Medicaid waiver. Accessed May 30, 2024. Available at: https://www.prnewswire.com/news-releases/findhelp-expands-work-with-nyc-providers-and-health-systems-prior-to-launch-of-the-ny-state-1115-medicaid-waiver-301968799.html
- 44.↑
Wethington SL, Rositch AF, Yu R, et al. Integrating social needs screening and resource referral into standard ambulatory oncology care: a quality improvement project. JCO Oncol Pract 2024;20:566–571.
