Underserved racial/ethnic minority patients diagnosed with cancer are a vulnerable patient population, and at significant risk for inadequate food.
Abstract
Purpose:
The association between food insecurity and health-related quality of life (QOL) of racial/ethnic minority patients with cancer has not been examined. The purpose of this study is to determine the relationship between food insecurity and health-related QOL reported by racial/ethnic minority patients with cancer.
Methods:
A consecutive sample of 1,390 underserved ethnic minority patients receiving cancer care in 10 cancer clinics and hospitals in New York City participated in this study. Health-related QOL was measured by the Functional Assessment of Cancer Therapy-General (FACT-G) and food security was assessed by the US Department of Agriculture Core Food Security Module.
Results:
Of the 1,390 patients, 581 (41.8%) were classified as food secure, 571 (41.1%) with low food security, and 238 (17.1%) with very low food security. Health-related QOL decreased with each lower food security level. Patient self-reported physical, functional, social, and emotional well-being subscale scores decrease significantly with increasing food insecurity. After controlling for demographic and medical-related factors, the decreases in QOL, physical, functional, social and emotional well-being scores with increasing food insecurity remained significant.
Conclusion:
Food insecurity was associated with lower QOL in this sample of underserved racial/ethnic minority patients with cancer. Underserved ethnic minority patients diagnosed with cancer are a vulnerable patient population, at significant risk for inadequate food access and the related lower QOL.
Introduction
An estimated 15% of American households were food insecure in 2012: they were unable to acquire adequate food for one or more household members because of insufficient money and other resources (eg, transportation).1 The food insecurity prevalence for racial and ethnic minorities is substantially higher than the national average, 25% for black, non-Hispanic households and 26% for Latino households.1
Food insecurity is strongly associated with economic hardship.1 In 2012, 41% of households with incomes below the official poverty line were food insecure, compared with 7% percent of those with incomes above 185% of the poverty line.1 However, even though poverty and food insecurity are strongly related, they are distinct processes. Poverty, as defined by the US Department of Commerce, and food insecurity are both indicators of economic hardship; however, if a household is able to borrow and save or receive practical social support, poverty might not result in food insecurity.2
Simmons et al3 found a food insecurity prevalence of 17% in a predominantly white, non-Hispanic sample of patients with cancer. Gany et al4 found more than three times this prevalence (55%) in an underserved, low-income, multiethnic minority sample. Several studies have shown that racial/ethnic minority patients with cancer face significant financial strain after a cancer diagnosis.5–7 Latino,7 immigrant Chinese,6,8 and African American9,10 patients with cancer often report unmet needs for support with logistical, practical, and financial issues. The rates of food insecurity among low-income ethnic minority patients with cancer likely increase after a cancer diagnosis, because of changes in income and employment, along with increased financial strain.4
The cost of cancer care, overall, is increasing.11,12 Low-income ethnic minority patients face a double burden: compromised health coupled with added financial pressures often associated with minority status. Research conducted by Blinder et al13–15 has shown that ethnic minority patients are more likely to remain unemployed after a cancer diagnosis and to be more financially unstable than non-Hispanic whites. Food-insecure patients are more likely to be nonadherent with their prescribed treatments because of issues related to their food insecurity, such as prioritizing between affording food or medical care.6,7
The relationship between health-related quality of life (HRQOL) and food insecurity has not been examined in ethnic minority patients with cancer. Because of the high rates of food insecurity among ethnic minority individuals and the financial burden of cancer in this patient population, this study examines the relationship between food insecurity and HRQOL in ethnic minority patients. We hypothesized that the QOL scores (overall, physical, functional, social and emotional) would be negatively associated with food insecurity. In exploratory analyses, we examine whether, age, sex, income, employment status, education, cancer diagnosis, and time since diagnosis would moderate this relationship. Finally, given the different rates of food insecurity across race and ethnicity, we examine the association between food insecurity and QOL outcomes stratified by race/ethnicity.
Methods
Design and Sample
This study included patients enrolled from 2008 to 2013 in the Cancer Portal Project. The Cancer Portal Project is a prospective, longitudinal multisite cohort composed of ethnic minority patients with cancer receiving cancer treatment and patient navigation at 10 safety net cancer clinics. Of the 1,817 patients approached in the study time period, 1,390 completed the surveys. Reasons for noncompletion were primarily time related and included such issues as being called in to see the provider before completing the survey. Patients were recruited from 10 cancer clinics located in areas of New York City with high poverty rates. This study draws on cross-sectional Cancer Portal Project data on QOL and food insecurity. Patient inclusion eligibility criteria were a minimum age of 18 years; a diagnosis of cancer; immigrant or ethnic minority status; having ever received cancer treatment; and being able to speak English, Spanish, or Chinese. This study was approved by the institutional review boards of Memorial Sloan-Kettering Cancer Center and the participating institutions.
Procedure and Measures
Patients were approached by a Cancer Portal Project patient navigator in the waiting room before their provider visits and were administered, in their preferred languages (Spanish, English, Cantonese, or Mandarin), a needs assessment survey. The survey included sociodemographic questions, cancer history, and a set of standardized scales measuring HRQOL and food security. A detailed description of the Cancer Portal Project study methodology and recruitment procedures has been published previously.7 Sociodemographic questions included participants' age, marital status, income, education, employment status, health insurance coverage, ethnicity, language, country of birth, and years in the United States. Cancer-related questions included cancer type, stage, time since diagnosis, treatments received, and comorbidities.
Food security was measured using the 18-item US Department of Agriculture's Core Food Security Module. This instrument has been used in national surveys, and its reliability and validity for use with low-income and ethnic minority groups are well established.16 The USDA Food Security Module is the “gold standard” for measuring and determining the extent and severity of food insecurity in the United States. Several studies have supported its validity and reliability.16 The food security construct consists of four areas: quantity of food, quality of food, food concerns, and coping mechanisms used by the household to augment food supplies.16 Food security questions relate to circumstances over the 12 months preceding the survey. Households are categorized into three levels of food security: food security, low food security, and very low food security. Households in these second two categories are defined as food insecure. The Spanish version17 of this module also has excellent psychometric properties. The translation procedure for the current survey was conducted by Harrison et al17 and consisted of translations by language experts and focus groups with low-income Spanish-speaking participants of different nationalities to refine the instrument.
QOL was assessed with the Functional Assessment of Cancer Therapy-General (FACT-G). The FACT-G is a standardized instrument consisting of 27 statements rated from 0 (not at all) to 4 (very much), with higher scores indicating better HRQOL.18 Subscale scores on four dimensions (physical, social/family, emotional, and functional well-being) are generated, and an overall HRQOL score is obtained. The minimally important difference (MID) for the total FACT-G scale is 8-10.25 The MID refers to the “smallest difference in score in the domain of interest that patients perceive as important, either beneficial or harmful, and that would lead the clinician to consider a change in the patient's management.”25(p208) High reliability and good validity have been reported for the English,19 Spanish,20–22 and Chinese23 versions.
Statistical Analysis
Statistical analyses were performed using SPSS software, version 20. The sample had at least 90% power to detect differences of 5 points on the scale or subscales scores. Differences between food-secure and food-insecure patients in demographic and cancer-related variables were explored using χ2 for categorical variables and t tests to compare means on continuous variables. More than 93% of the patients had complete data for the FACT-G, and 99% had complete data for the USDA Food Security Survey Module. We followed the FACT-G scoring guidelines and the Guide to Measuring Food Insecurity to impute missing values for patients with incomplete responses. One-way analyses of variance were used to test for significant overall mean differences by food security categories. Multivariable hierarchical regression analyses assessed the effect of demographic and cancer-related medical factors (first step: age, sex, income, employment status, education, health insurance status, type of clinic [public or private], cancer diagnosis, and time since diagnosis), ethnicity and race (second step), and food security (third step) on QOL, as measured by the total scores of the FACT-G and its subscales. We also tested interactions of Latino ethnicity and black race with food security in the regression models (fourth step) to assess ethnic and racial differences in the impact of food security on quality of life and its subscales.
Results
Demographic and food security characteristics are presented in Table 1. Patients' mean age was 55 years, and the majority of patients were Latinos (45%), followed by blacks (41%), Asians (5%), and others (9%). Further, the majority of the sample were females (66%); single, divorced, separated or widowed (65%); unemployed (69%); with incomes under the national poverty level (82%), and with less than a high school degree (44%). Approximately one fifth of the sample reported having Medicaid for emergency care (22%) and being uninsured (17%). The most prevalent cancer diagnosis was breast (40%), followed by GI (17%).
Table 1.
Participants Characteristics by Food Security Category
| Characteristic | Total (N = 1,390), No. (%) | Food Security (n = 581), No. (%) | Low Food Security (n = 571), No. (%) | Very Low Food Security (n = 238), No. (%) | χ2/F (P)* |
|---|---|---|---|---|---|
| Age, years; mean (SD) | 54.7 (13.7) | 57.2 (14.5) | 52.4 (13.0) | 53.4 (12.2) | 15.20 (.01) |
| Sex | 2.47 (.29) | ||||
| Male | 479 (34.5) | 214 (36.9) | 187 (32.7) | 78 (33.1) | |
| Female | 908 (65.5) | 366 (63.1) | 384 (67.3) | 158 (66.9) | |
| Marital status† | 1.59 (.45) | ||||
| Single‡ | 891 (64.8) | 369 (64.1) | 360 (64.1) | 162 (68.4) | |
| Married or partnered | 484 (35.2) | 207 (35.9) | 202 (35.9) | 75 (31.6) | |
| Race/ethnicityठ| |||||
| Hispanic | 626 (45.1) | 241 (38.5) | 292 (46.6) | 93 (14.9) | 15.21 (.001) |
| Black | 513 (41.0) | 219 (42.7) | 193 (37.6) | 101 (19.7) | 4.70 (.10) |
| Asian | 74 (5.3) | 35 (47.3) | 25 (33.8) | 14 (18.9) | 1.73 (.42) |
| Household composition | 0.18 (.67) | ||||
| With children∥ | 531 (27.3) | 208 (25.1) | 242 (31.1) | 81 (24.0) | |
| Without children | 1413 (72.7) | 620 (74.9) | 536 (68.9) | 257 (76.0) | |
| Income level† | 37.40 (.001) | ||||
| Over federal poverty line | 247 (18.2) | 146 (25.8) | 70 (12.6) | 31 (13.4) | |
| Under federal poverty line | 1108 (81.8) | 420 (74.2) | 487 (87.4) | 201 (86.6) | |
| Education† | |||||
| Less than high school | 591 (43.6) | 244 (43.0) | 242 (43.8) | 105 (44.9) | 2.59 (.63) |
| High school graduate | 499 (36.9) | 202 (35.6) | 210 (38.0) | 87 (37.2) | |
| More than high school | 264 (19.5) | 122 (21.5) | 100 (18.1) | 42 (17.9) | |
| Employment status† | 75.96 (.001) | ||||
| Unemployed | 933 (68.5) | 326 (57.4) | 412 (73.6) | 195 (83.3) | |
| Retired | 297 (21.8) | 182 (32.0) | 86 (15.4) | 29 (12.4) | |
| Employed | 132 (9.7) | 60 (10.6) | 62 (11.0) | 10 (4.3) | |
| Health insurance status† | 47.87 (.001) | ||||
| Uninsured | 234 (17.1) | 66 (11.6) | 122 (21.6) | 46 (19.5) | |
| Medicaid for emergency care | 300 (21.9) | 95 (16.7) | 141 (24.9) | 64 (27.1) | |
| Other¶ | 837 (61.1) | 408 (71.7) | 303 (53.5) | 126 (53.4) | |
| Type of clinic | 33.50 (.001) | ||||
| Health and Hospitals Corporation | 1063 (79.8) | 417 (74.1) | 482 (87.0) | 164 (73.9) | |
| Private | 276 (20.6) | 146 (25.9) | 72 (13.0) | 58 (26.1) | |
| Cancer diagnosis† | |||||
| Breast | 561 (40.4) | 208 (35.8) | 251 (44.0) | 102 (42.9) | 8.71 (.01) |
| Prostate | 186 (13.4) | 85 (14.6) | 73 (12.8) | 28 (11.8) | 1.46 (.48) |
| GI | 238 (17.1) | 112 (19.3) | 84 (14.7) | 42 (17.7) | 4.30 (.12) |
| Head and neck | 53 (3.8) | 21 (3.6) | 25 (4.4) | 7 (3.0) | 1.04 (.60) |
| Time since diagnosis, years; mean (SD)† | 0.8 (1.3) | 0.8 (1.2) | 0.7 (1.3) | 0.9 (1.6) | 1.69 (.19) |
NOTE. Percentages may not equal 100% as a result of rounding.
Comparisons were made using the χ2 test, except for age and time since diagnosis, which used analysis of variance.
Percentages and P are not based on a total of 1,390 participants.
Single, divorced, separated, widowed.
Percentages are presented across rows, not columns.
Refers to children under 18.
Other included Medicaid, Medicare, and private health insurance.
The prevalence of food security was 41.8, low food security 41.1%, and very low food security 17.1%. Among the Latinos, 61.5% were food insecure (low and very low food security), among the black patients 57.3% were food insecure, and 52.7% of the Asian and Pacific Islander patients were food insecure. Latino patients were more likely to be food insecure and had lower income levels than patients of other ethnicities. Further, younger, unemployed patients with income under the federal poverty line, with a breast cancer diagnosis, and with Medicaid for emergency care or no insurance reported more food insecurity than their counterparts. Sex, marital status, children living in household, education level, other cancer diagnoses (prostate, GI, head and neck), cancer stage, and time since diagnosis were not associated with food insecurity in this sample. However, sex and education were associated with poverty status. Further, the prevalence of food insecurity in municipal clinics (six of the sites) was higher than in the voluntary hospital clinics (four sites). The municipal clinics also had more younger, poor, uninsured, unemployed, and foreign-born patients, as well as more patients diagnosed with GI cancer, more recently diagnosed patients, and more patients with advanced-stage cancers.
The QOL total scale (0.85), and the dimensions—physical (0.86), social/family (0.70), emotional (0.70), and functional well-being (0.86)—showed adequate reliability, as assessed by Cronbach's alpha. In univariable analyses, patient self-reported QOL and the physical, functional, social, and emotional well-being subscale scores decrease significantly with each lower food security level (all P values < .001). Scores were significantly lower for patients in the very low food security category, except for functional well-being scores (Table 2). Table 3 shows the multivariable hierarchical regression models. In the hierarchical regression models, demographic and medical-level factors (age, sex, education, income, employment status, health insurance status, public or private type of clinic, cancer diagnosis, and time since diagnosis) account for 11% of the variance in overall QOL, 8% of physical well-being, 17% of functional well-being, 3% of social well-being, and 10% of emotional well-being. Ethnicity and race account for 1% of the variance in all QOL outcomes, except emotional well-being, in which race/ethnicity explain 4% of the variance. Food insecurity explains 2% of the variance of functional well-being, 3% of physical and social well-being, 4% of emotional, and 6% of overall QOL. The interactions of race and ethnicity with food insecurity explained only 1% of the variance for all the models; such interactions were significant for all the outcomes, except functional well-being. The final model incorporating all domains explains 18% of the variability in QOL, 20% of functional and emotional well-being, 12% of physical, and 7% of social well-being (Table 3).
Table 2.
Overall Quality of Life and Physical, Functional, Social, and Emotional Well-Being Subscales by Food Security Category
| Subscale | Total | Food Security | Low Food Security | Very Low Food Security | F |
|---|---|---|---|---|---|
| QOL total score | 71.98 (16.26) | 77.42 (15.89)* | 69.57 (14.61)* | 64.50 (16.59)* | 70.31* |
| Physical WB | 20.31 (6.22) | 21.43 (6.02)* | 20.38 (5.85)* | 17.40 (6.64)* | 37.44* |
| Functional WB | 13.39 (7.18) | 15.59 (7.51)* | 11.46 (6.41)* | 12.65 (6.61)* | 52.75* |
| Social WB | 18.32 (5.82) | 19.59 (5.34)* | 17.63 (5.70)* | 16.87 (6.60)* | 26.04* |
| Emotional WB | 19.97 (4.76) | 20.82 (4.42)* | 20.10 (4.39)* | 17.59 (5.56)* | 41.52* |
Abbreviations: QOL, quality of life; WB, well-being.
P < .001.
Table 3.
Hierarchical Regression Analyses of Food Insecurity and Race/Ethnicity Predicting Overall Quality of Life and Physical, Functional, Social, and Emotional Well-Being Subscales
| Measure | R2 | Change in R2 | F Change | Beta |
|---|---|---|---|---|
| Quality of life | ||||
| 1. Demographic/medical characteristics | .11 | 0.11 | 13.13* | |
| 2. Race/ethnicity | .12 | 0.01 | 5.88† | |
| Latino ethnicity | −.01 | |||
| Black race | .09‡ | |||
| 3. Food insecurity | .17 | 0.06 | 78.33* | −.25* |
| 4. Interactions | .18 | 0.01 | 4.37† | |
| Latino ethnicity × food insecurity | .17† | |||
| Black race × food insecurity | .19† | |||
| Physical well-being | ||||
| 1. Demographic/medical characteristics | .08 | 0.08 | 9.06* | |
| 2. Race/ethnicity | .09 | 0.01 | 5.89† | |
| Latino ethnicity | .08‡ | |||
| Black race | .14* | |||
| 3. Food insecurity | .11 | 0.03 | 32.35* | −.17* |
| 4. Interactions | .12 | 0.01 | 3.81‡ | |
| Latino ethnicity × food insecurity | .06 | |||
| Black race × food insecurity | .16‡ | |||
| Functional well-being | ||||
| 1. Demographic/medical characteristics | .17 | 0.17 | 22.10* | |
| 2. Race/ethnicity | .18 | 0.01 | 8.81* | |
| Latino ethnicity | −.17* | |||
| Black race | −.09‡ | |||
| 3. Food insecurity | .20 | 0.02 | 27.85* | −.15* |
| 4. Interactions | .20 | 0.01 | 1.26 | |
| Latino ethnicity × food insecurity | .08 | |||
| Black race × food insecurity | .01 | |||
| Social well-being | ||||
| 1. Demographic/medical characteristics | .03 | 0.03 | 3.62* | |
| 2. Race/ethnicity | .04 | 0.01 | 1.71 | |
| Latino ethnicity | −.07 | |||
| Black race | −.03 | |||
| 3. Food insecurity | .06 | 0.03 | 29.56* | −.16* |
| 4. Interactions | .07 | 0.01 | 3.61‡ | |
| Latino ethnicity × food insecurity | .17‡ | |||
| Black race × food insecurity | .18† | |||
| Emotional well-being | ||||
| 1. Demographic/medical characteristics | .10 | 0.10 | 12.31* | |
| 2. Race/ethnicity | .14 | 0.04 | 28.04* | |
| Latino ethnicity | .21* | |||
| Black race | .31* | |||
| 3. Food insecurity | .19 | 0.04 | 60.41* | −.22* |
| 4. Interactions | .20 | 0.01 | 5.00† | |
| Latino ethnicity × food insecurity | .18† | |||
| Black race × food insecurity | .20† |
NOTE. Demographics and medical characteristics included in the first step were: age, gender, education, income, employment status, health insurance status, type of clinic (public v private), cancer diagnosis, and time since diagnosis.
P < .001.
P < .01.
P < .05.
Discussion
In this sample of low-income ethnic minority patients with cancer attending safety net cancer clinics, food insecurity status was associated with lower general HRQOL, and with worse physical, functional, social, and emotional well-being. The food insecurity rate (58.2%) in this multiethnic cancer sample is noticeably high, higher than the national rates for low-income individuals (in 2012, 41% percent of households with incomes below the official poverty line were food insecure).1 We found a significant negative relationship between food insecurity and QOL in these patients. On average, this multiethnic sample scored approximately 9 points below the normative mean (80.9) for patients with cancer reported by the developers of the FACT-G scale.24 Patients with low food security scored 6 points below the food-secure patients and 10 points below the normative mean. Patients with very low food security scored 11 points below the food-secure patients and 15 points below the normative mean in the total QOL scale. The developers of the quality of life scale report that the FACT-G MID for the total scale is 8-10.25 The difference in FACT-G scores between food-secure patients and patients with very low food security was within the MID range, suggesting that an important and perceivable gap in QOL exists between them. Food insecurity was associated with lower quality of life and well-being after controlling for patients' ethnicity/race, age, sex, education level, income level, employment status, health insurance status, type of cancer care clinic, cancer diagnosis, and time since diagnosis. Latino ethnicity and Black race were also associated with QOL, after controlling for other demographic and medical factors. However, the interaction between race and ethnicity with food insecurity explained only a small proportion of the variance, suggesting that the relationship between food insecurity and QOL is slightly stronger for Black and Latino patients compared with individuals of other ethnicities or races. Underserved ethnic minority patients with cancer are at significant risk of not acquiring adequate food, because they are financially unstable, have competing financial priorities, and have limited access to food resources. This lack of access to adequate food may have a clinical impact on patients' QOL, which would worsen as food insecurity worsens into hunger. Food insecurity affects an increasing number of individuals in the United States, and patients with cancer are at increased risk as a result of changes in employment status and income.4 However, the impact of food insecurity on the health outcomes of patients with cancer and other chronically ill patients has been overlooked by health professionals and is an understudied determinant of health status. There is a critical need to raise awareness among the medical community and policy makers about the pronounced and detrimental prevalence of food insecurity among our most underserved, impoverished, and disenfranchised patients.
Exploring the impact of food insecurity and other indicators of deprivation and poverty on patient outcomes and access to health care and services is warranted. Health care providers and clinicians can help address food insecurity issues. They can help educate patients on how to choose nutritious foods in a cost-effective way; refer patients to patient navigators and case managers to assist them with their financial needs; refer patients to community-based agencies that administer food assistance; and pressure administrators to provide case management and financial aid, as well as to hire culturally competent patient navigators who can address these issues. Immigrant and racial/ethnic minority patients with cancer are at higher risk of facing economic deprivation, and many of them require special assistance to facilitate their food security. Social assistance programs that are medically and culturally appropriate should be developed to meet their nutritional needs. The development of medically tailored food pantries, co-located with cancer clinics, can provide economic relief and might even improve adjustment after a cancer diagnosis and QOL in this population. This link between food insecurity and low QOL can help guide future health policy and the development of sustainable programs that address socioeconomic determinants of health, and thus the resultant QOL, among chronically ill patients. Future studies should explore the impact of food insecurity on other health and quality of care outcomes. Also, it is necessary to design and study interventions aimed at improving food security and other socioeconomic factors, and to test their effectiveness in improving psychosocial, medical and quality of care outcomes.
There are some limitations to this study. First, it is possible that other factors, not included in this study, might be related to food insecurity and its relationship to health-related QOL. Predictors examined in our multivariable analyses accounted for only 18% of the variance in the FACT-G scores, with the rest of the variance possibly explained by other, unexamined factors. Some examples of factors that might be associated with both QOL and food insecurity are cancer stage and body mass index. An additional limitation of this study is that our reliance on self-report data for medical information precluded the inclusion of cancer stage in the analyses. In addition, given the cross-sectional design of this study, these results do not allow for causal inferences. Future research with longitudinal designs and conceptual contextual models is necessary for a deeper understanding of the causal pathway of food insecurity and HRQOL in ethnic minority patients. Data were based on a cross-sectional survey that used retrospective measures of food security, an additional limitation. Our findings are therefore subject to the recall and reporting biases inherent in this approach. Further, the Food Security Survey includes questions that relate to circumstances over the 12 months preceding the survey. Some of the patients had been recently diagnosed, and their responses might reflect their circumstances before the cancer diagnosis. Future studies with longitudinal designs can shed light on the stability or emergence of food insecurity after a cancer diagnosis. The recruitment was limited to participants who speak English, Spanish, and/or Chinese. Further, future studies should conduct stratified analyses for different ethnic and racial groups because the prevalence of low and very low food security may vary across the ethnic/racial groups. Different ethnic and racial groups are exposed to different risk factors (ie, immigration status, language issues, insurance status, cultural factors influencing food selection and intake, rates of poverty, discrimination, etc) that might be associated with the rates of food insecurity and QOL. Future studies should continue disaggregating groups and examining within-group differences.
Acknowledgment
Supported by the New York Community Trust, the New York State Health Foundation, the Laurie Tisch Illumination Fund, and by National Cancer Institute Grants No. U54-13778804-S2 (City College of New York-Memorial Sloan Kettering Cancer Center Partnership) and T32CA009461 (Institutional Training Grant). The contents of this article are solely the responsibility of the authors and do not necessarily represent the views of the awarding agencies. Presented in part at the 35th Annual Meeting and Scientific Sessions of the Society of Behavioral Medicine, Philadelphia, PA, April 23-26, 2014.
Authors' Disclosures of Potential Conflicts of Interest
The authors indicated no potential conflict of interest.
Author Contributions
Conception and design: Francesca Gany, Jennifer Leng, Nicole Roberts, Rosario Costas-Muniz
Financial support: Francesca Gany, Nicole Roberts
Administrative support: Julia Ramirez, Serena Phillips, Nicole Roberts
Provision of study materials or patients: Julia Ramirez, Serena Phillips, Rosario Costas-Muniz
Collection and assembly of data: Francesca Gany, Jennifer Leng, Julia Ramirez, Serena Phillips, Nicole Roberts, Rosario Costas-Muniz
Data analysis and interpretation: Francesca Gany, Jennifer Leng, Abraham Aragones, Nicole Roberts, Mohammed Imran Mujawar, Rosario Costas-Muniz
Manuscript writing: All authors
Final approval of manuscript: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
Health-Related Quality of Life of Food-Insecure Ethnic Minority Patients With Cancer
The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or jop.ascopubs.org/site//misc/ifc.xhtml.
Francesca Gany
No relationship to disclose
Jennifer Leng
No relationship to disclose
Julia Ramirez
No relationship to disclose
Serena Phillips
No relationship to disclose
Abraham Aragones
No relationship to disclose
Nicole Roberts
No relationship to disclose
Mohammed Imran Mujawar
No relationship to disclose
Rosario Costas-Muniz
No relationship to disclose
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