Patients’ Barriers to Receipt of Cancer Care, and Factors Associated with Needing More Assistance from a Patient Navigator

Abstract

Background

Racial minorities have poorer cancer survival in the United States. The purpose of this study was to better understand patients’ barriers to cancer care, and to determine which patients have a greater need for assistance from a patient navigator.

Methods

Community health workers assisted newly-diagnosed breast and colorectal cancer patients during a randomized trial of patient navigation, and collected information about patients’ barriers. Barriers to care were characterized, and were compared between non-Hispanic white and minority patients. A multivariate model was constructed of factors associated with increased log Navigation Time, a measure of patients’ need for assistance.

Results

Patients’ (n=103) most commonly-identified barriers to care included a lack of social support, insurance/financial concerns, and problems communicating with healthcare providers. Barriers differed between non-minority and minority patients, and minority patients faced a greater number of barriers (p=0.0001). In univariate analysis, log Navigation Time was associated with race/ethnicity, education, income, employment, insurance type, health literacy, marital status, language, and comorbidity. A multivariate model (R²=0.43) for log Navigation Time was created using stepwise selection, and included the following factors: minority race/ethnicity (p=0.032), non-full-time employment (p=0.0004), unmarried status (p=0.085), university center (0.0005), and months in study (p<0.0001).

Conclusions

Newly-diagnosed cancer patients’ most common barriers to care include lack of social support, insurance/financial concerns, and problems with healthcare communications. In this sample of patients, a greater need for assistance was independently associated with minority race/ethnicity and unemployment. These data may help in the design and targeting of interventions to reduce cancer health disparities.

Introduction

In the United States, cancer outcome disparities by race and/or socioeconomic status (SES) are well documented for breast cancer, colorectal cancer, prostate cancer and lung cancer.^{1, 2} Eliminating disparities is a major goal of Healthy People 2010.³ However, understanding the reasons for cancer disparities is important for the design of interventions to address the problem. While later stage at diagnosis due to screening inequities or lack of access to care contributes to disparities, disparities persist even after correcting for stage.^4–8 This finding raises the question of disparities in cancer treatment. Using cancer registry data, Gross et al documented that “processes of cancer care” after diagnosis differ between black and white cancer patients.⁹ Furthermore, these process and outcome disparities have not improved over time.⁹

Why do traditionally-disadvantaged patients receive different care for cancer in the United States? Our group has proposed a theoretical model to explain how “barriers to care” faced by minority or low SES patients can lead to worse health outcomes (Figure 1).

Figure 1. Theoretical Model of Cancer Health Disparities.

*indicates factors potentially affected by Patient Navigation-Activation)

This model shows how patients’ barriers may affect processes and outcomes of cancer care, such as patient adherence, the doctor-patient relationship, and timely receipt of care. However, empirical research studies to prospectively document the range and number of barriers faced by minority or poor cancer patients have not been performed. Instead, prior studies have focused on individual barriers. These include financial barriers,^{10, 11} social barriers,^12–15 communications barriers,^16–18 logistical barriers, and medical comorbidities.¹⁹

Patient navigation is one of the few interventions that can potentially address the multiple barriers faced by disadvantaged cancer patients. However, to design patient navigation programs and train navigators, further characterization is needed of the range and frequency of specific barriers faced by cancer patients. There is also a need to identify which patients have a greater need for the types of assistance provided by patient navigation programs.

The first aim of this study is to describe the barriers to health care faced by a diverse group of newly-diagnosed breast and colorectal cancer patients. These data were prospectively collected by community health workers who had in-depth knowledge of patients. The second aim is to compare these barriers between non-Hispanic white and minority patients. Because race/ethnicity is associated with other demographic features such as income and education, these demographic features will be also compared between non-Hispanic white and minority patients. The final aim of the study is to construct a model to predict which patients have the greatest need for patient navigation. These results may provide information that will assist in the rational design of future interventions to address cancer health disparities.

Methods

Data Source

The present study utilizes data prospectively collected during a randomized trial of patient navigation conducted through the National Cancer Institute-sponsored Patient Navigation Research Program (PNRP).²⁰ Patient navigation is an intervention in which trained individuals [in this case, community health workers (CHWs)] assist cancer patients.^{21, 22} CHWs help patients with appointment reminders, coordination of care, insurance paperwork, logistical support, social support, and coaching to promote effective communication with medical providers. Consecutive cancer patients who were assigned to the patient navigation arm of the study are the patient sample for the present study.

Patients recruited to participate in the patient navigation study were newly-diagnosed breast and colorectal cancer patients. Patients were recruited from all Rochester, NY-area cancer centers and from some primary care practices. There were no exclusion criteria based upon socioeconomic status, race or insurance, but institutionalized patients and those with dementia or prior cancer were excluded. The study was IRB-approved by all participating institutions, and all participants provided written informed consent.

Data Collection

Patient information was prospectively collected by research assistants and CHWs during patient navigation activities. Patient demographic information was obtained by patient self-report. Cancer stage and treatment were abstracted from medical charts by cancer physicians. Information on barriers to care was collected by CHWs through semi-structured interviews with patients. A standardized form was used, and the types of barriers included on the form are listed in Figure 2. The category of “other” was coded when time was spent on behalf of the patients, but a specific barrier on the list was not linked to the time (for example, time for the CHW to gather information in response to a patient’s question).

Figure 2. Barriers Faced by Cancer Patients.

* “Insurance” includes health insurance problems, being uninsured, being underinsured, or having high co-payments; “Comoridity” includes medical and mental health; “System Problem” means difficulty scheduling care, poor coordination of care, or other obstacles related to providers’ practices; “Medical Communication” refers to communication problems between patients and providers; “Perceptions/Beliefs” refers to beliefs about tests or treatment that may be a barrier to accepting those tests or treatments

Study Setting

The current study takes place in Rochester, NY, where medical care is provided by one large, academic medical center and three smaller hospitals, as well as a variety of private practices providing outpatient care. The cancer treatment centers in Rochester serve all of Monroe County, and county demographics are similar to the demographics of the US population.²³

Variable Definitions

Most variables are defined in Table 1; however, detailed variable definitions are given here for selected variables. Race/Ethnicity is derived from patient self-report, and is dichotomized as non-Hispanic white (n=63) versus minority (n=40). The minority category for race/ethnicity included: black/non-Hispanic (n=25), Hispanic (10), and other (n=5).

Table 1.

Patient Characteristics

		All (n=103)	White/Non-Hispanic (n=63)	Minority (n=40)	p-value
Age at Time of Enrollment	Years [mean (SD)]	55 (12)	56 (12)	53 (13)	0.1
Gender	Number (%)	93 (90)	56 (89)	37 (93)	0.7
	Female
Diagnosis	Breast Cancer	87 (85)	52 (83)	35 (88)	0.5
	Colorectal Cancer	16 (16)	11 (18)	5 (13)
Stage at Diagnosis	0	5 (5)	1 (2)	4 (10)	0.5
	1	40 (39)	25 (40)	15 (38)
	2	26 (25)	17 (27)	9 (23)
	3	20 (19)	11 (18)	9 (23)
	4	5 (5)	4 (6)	1 (3)
	Missing	7 (7)	5 (8)	2 (3)
Insurance Type	Private	54 (52)	40 (64)	14 (35)	0.01
	Medicare	24 (23)	15 (24)	9 (23)
	Medicaid	14 (14)	2 (3)	12 (30)
	None	8 (8)	4 (6)	4 (10)
	Other	3 (3)	2 (3)	1 (3)
Education (self-reported)	8th Grade or Less	4 (4)	0 (0)	4 (10)	0.0001
	Some High School	14 (14)	2 (3)	12 (31)
	High School	23 (23)	15 (24)	8 (21)
	Diploma or Eq.
	Some College	22 (22)	17 (27)	5 (13)
	Assoc. Degree	11 (11)	8 (13)	3 (8)
	College Degree	19 (19)	14 (22)	5 (13)
	Graduate/Professional	9 (9)	7 (11)	2 (5)
Health Literacy Scale (REALM)	Mean (SD)	18.7 (4)	20.2 (1)	16.0 (6)	0.0004
Language Spoken at Home	Number (%)				0.008
	English	98 (95)	63 (100)	35 (88)
Household Income (self-reported)	less than $10,000	15 (15)	3 (5)	12 (30)	0.02
	$10,000–19,999	15 (15)	9 (14)	6 (15)
	$20,000–29,000	12 (12)	7 (11)	5 (13)
	$30,000–39,000	9 (9)	6 (10)	3 (8)
	$40,000–49,000	5 (5)	2 (3)	3 (8)
	$50,000 or greater	25 (24)	24 (38)	1 (3)
	Missing	22 (21)	12 (19)	10 (25)
Median Household Income by Zip Code (1999)	US$ [Mean (SD)]	40,968 (15,054)	46,696 (13,553)	31,864 (12,764)	<0.0001
Marital Status	# (%) Married, living as married	54 (52)	37 (59)	17 (43)	0.1
Employment	Full-Time	34 (33)	25 (40)	9 (23)	0.006
	Part-Time	11 (11)	10 (16)	1 (3)
	Not Employed	58 (56)	28 (44)	30 (75)
Charlson Comorbidity Index	Mean (SD)	1.1 (1.1)	0.9 (1.0)	1.4 (1.2)	0.03
Treatment Center	# (%) at University	56 (54)	33 (52)	23 (58)	0.6
Number of Months in Study	Mean (SD)	7.7 (4)	7.8 (4)	7.5 (4)	0.7
Number of Treatment Modalities	Categorical (1–4)				0.3

Health literacy was measured using the Rapid Assessment of Adult Literacy in Medicine (REALM, short version).^{24, 25} The range of scores is 0 to 21 (best). Because of a large number of missing values for self-reported income, the 1999 median income for the zip code of patient residence was used in place of self-report income.²⁶ Comorbidity was self-reported based on the Charleson Comorbidity Index.²⁷

Navigation Time

CHWs recorded daily the total amount of time spent with each patient, and time spent addressing patient barriers. Time spent “just waiting” at medical offices prior to appointments was deducted from time estimates. The total time recorded by CHWs was summed to produce the variable Navigation Time, a measure of intensity of patient navigation. Because Navigation Time was not normally distributed, the variable was log-transformed (log Navigation Time) yielding a normal distribution.

Data Analysis

Patient sample characteristics and barriers were summarized with descriptive statistics. Associations between continuous variables and race/ethnicity were tested with t-tests. Dichotomous factors were tested for association with race/ethnicity using chi-square or Fisher’s exact tests, as appropriate. Categorical variables were compared between categories of race/ethnicity using Wald chi-square tests or Cochran-Mantel-Haenszel Statistics.

A series of bivariate analyses were conducted to test for associations between patient/treatment factors and the dependent variable, log Navigation Time. T-tests were used for dichotomous variables, Kruskal-Wallis tests were used for ordinal variables, and simple linear regression was used for continuous variables. All p-values were 2-sided, and statistical significance was defined as p<=0.05.

To construct a model of factors associated with increased need for patient navigation, log Navigation Time was used as the dependent variable in multiple linear regression with stepwise selection. Independent variables associated with log Navigation Time on univariate analysis were included in the stepwise selection. The criteria for selection were: p≤0.25 for model entry, and p≤0.10 to remain in model.

To interpret the results of the multivariate analysis, beta coefficients were exponentiated to return to the outcome Navigation Time. This gives a ratio of the geometric means of the non-base value over the base value.²⁸ Mean Navigation Times predicted from the model are shown in Figure 3. These were derived by multiplying the intercept value by the exp(beta) value for each factor to show how much each individual factor would be predicted to change the mean navigation time compared to having no risk factors.

Figure 3. Mean Navigation Times* from Model.

*Units for navigation time = minutes

Software programs used in this study were Microsoft Excel and SAS for Windows (version 9.1, Cary, NC).

Results

Characteristics of the Patient Sample

Characteristics of the patient sample are presented in Table 1. The majority of patients were women with early-stage breast cancer, and 40 of 103 patients in the sample (39%) were minority. The patients in the sample had lower income than the state average, particularly amongst minority patients. While age, gender, cancer type, and cancer stage were similar between white and minority patients, SES factors and comorbidity score differed significantly by race/ethnicity.

Barriers to Cancer Care

Figure 2 shows the barriers to cancer care faced by patients. The most common were problems with medical communication, a lack of social support, and medical insurance concerns, each of which affected over half of the patients. Examples include patients’ reluctance to ask questions or share problems with the medical team, having no one to accompany them to treatments or appointments, and having difficulty completing or understanding health insurance paperwork. Other common barriers were financial problems, medical or mental health co-morbidities, and transportation. In addition, patients’ fears, perceptions or beliefs about tests or treatments, and attitudes toward providers were commonly-identified barriers to care.

Figure 2 also includes estimates of time spent by CHWs addressing various barriers, indicating that certain barriers were time-intensive, such as transportation problems, housing problems, and arranging for interpreters. Of note, these times exclude any face-to-face time between CHW and patient.

Table 2 shows the differences in barriers between non-Hispanic white and minority patients in the study. The overall number of barriers was significantly greater for minority patients (mean 7 v. 5), and certain barriers were also more common. Those included financial problems, medical comorbidities, transportation, perceptions and beliefs about tests or treatments, language barriers, and childcare issues. The total Navigation Time is also shown in Table 2 and is significantly greater for minority than for non-minority patients.

Table 2.

Barriers to Care by Race/Ethnicity

	All (n=103)	White, Non-Hispanic (n=63)	Minority (n=40)	p-value*
	No. (%)	No. (%)	No. (%)
Communication Coaching	65 (63)	38 (60)	27 (68)	0.5
Social/Practical Support	64 (62)	35 (56)	29 (73)	0.08
Insurance (uninsured, underinsured, high co-pay)	56 (54)	32 (51)	24 (60)	0.4
Financial problems	41 (40)	19 (30)	22 (55)	0.01
Medical and mental health co-morbidity	40 (39)	19 (30)	21 (53)	0.02
Fear	37 (36)	18 (29)	19 (48)	0.051
Communication concerns with medical personnel	36 (35)	19 (30)	17 (43)	0.2
Transportation	33 (32)	13 (21)	20 (50)	0.002
System problem with scheduling care	30 (29)	14 (22)	16 (40)	0.05
Perceptions/belief about tests/treatment	21 (20)	8 (13)	13 (33)	0.02
Proactive Navigation Needed	19 (18)	8 (13)	11 (28)	0.06
Language/Interpreter	9 (9)	2 (3)	7 (18)	0.03
Attitudes towards providers	9 (9)	3 (5)	6 (15)	0.09
Location of Health Care Facility	8 (8)	6 (10)	2 (5)	0.5
Patient disability	6 (6)	3 (5)	3 (8)	0.7
Housing	5 (5)	2 (3)	3 (8)	0.4
Employment Issues	5 (5)	3 (5)	2 (5)	1.0
Childcare Issues	4 (4)	0 (0)	4 (10)	0.02
Adult Care	3 (3)	1 (2)	2 (5)	0.6
Literacy	2 (2)	1 (2)	1 (3)	1.0
Number of Barriers per Patient [mean (sd)]	5.8 (3.2)	4.9 (2.7)	7.2 (3.3)	0.0001
Navigation Time [minutes, mean (sd)]	1489 (2036)	1084 (1658)	2127 (2406)	0.02

^*Chi-square tests, Fisher’s exact tests, and t-tests used as appropriate

Need for Patient Navigation

Table 3 gives the results of univariate analysis of patient and study factors that may influence the need for patient navigation as represented by log Navigation Time. Univariate analysis reveals that log Navigation Time is associated with race/ethnicity, insurance type, education, health literacy, language, income, marital status, employment, and comorbidity score. Duration of time in study, enrollment date, treatment center, and CHW ID number also were associated with log Navigation Time in univariate analysis.

Table 3.

Univariate Analysis of Factors Potentially Associated with log Navigation Time

	Category	log Navigation Time [mean(SD)] or Parameter Estimate*	p-value
Age			0.3
Gender			0.9
Cancer Type			0.9
Stage at Diagnosis			0.1
No. of Treatment Modalities			0.5
Race/Ethnicity	White, non-Hispanic	6.2 (1.2)	0.002
	Minority	7.1 (1.3)
Insurance Type	None	6.8 (1.4)	0.004
	Other	6.3 (2.5)
	Medicaid	7.6 (0.9)
	Medicare	6.8 (1.2)
	Private	6.2 (1.3)
Education	8th Grade or Less	7.3 (0.2)	0.02
	Some High School	7.7 (1.1)
	High School Diploma or Equivalent	6.2 (1.0)
	Some College	6.3 (1.5)
	Assoc. Degree	6.4 (2.0)
	College Degree	6.4 (0.9)
	Graduate/Professional	6.3 (1.5)
Health Literacy Scale		−0.07	0.02
Language	English	6.5 (1.3)	0.02
	Other	7.9 (0.5)
Household Income		−0.00003	0.001
Marital Status	Married living as married	6.3 (1.4)	0.03
	Other	6.8 (1.2)
Employment	Not Employed	7.0 (1.2)	<0.0001
	Part Time	5.9 (1.1)
	Full Time	5.9 (1.3)
Comorbidity Score		+0.4	0.0009
Treatment Center	University	6.9 (1.3)	0.003
	Community	6.1 (1.3)
Number of Months in Study		+0.1	0.0004
Date of Enrollment		−0.002	0.005
CHW ID number	1	7.1 (1.5)	0.006
	2	6.7 (1.4)
	3	6.2 (1.0)
	4	7.9 (0.1)
	5	5.7 (1.2)

^*for continuous variables

To ensure that the number of months in study did not bias the univariate results, a linear regression of each variable in Table 3 against log Navigation Time was performed, correcting for the number of months in study. The distribution of statistically significant and insignificant variables did not change except for the date of enrollment, which became non-significant. All other significant variables in Table 3 remained significantly associated with log Navigation Time, after correcting for months in study (data not shown).

The final aim of the present study was to construct a model of factors associated with greater need for PN. All factors with p<=0.10 in Table 4 were included in stepwise selection with log Navigation Time as the dependent variable. The results are given in Table 4. A model including race/ethnicity, marital status, employment, treatment center and duration of time in the study predicted 43% of the variation in log Navigation Time in our patient sample. To further interpret these results, the beta coefficients were exponentiated to reflect Navigation Time. Mean Navigation Times predicted from the model are shown in Figure 3. This shows that in the model derived from our sample of patients, being unemployed increases navigation time by 157%, minority status increases it by 66% and being unmarried increases it by 46%.

Table 4.

Multivariate Model for log Navigation Time

	Level	Parameter Estimate (β)*	exp(beta)	p-value*
Race/Ethnicity	Minority	0.51	1.659	0.0321
Marital Status	Unmarried	0.38	1.461	0.085
Employment	Unemployed	0.95	2.573	0.0004
	Part-time	0.22	1.251
Treatment Center	Not at University	−0.79	0.455	0.0005
# Months in Study	Continuous	0.13	1.142	<0.0001

^*Multiple linear regression with stepwise selection. n=95. Model R² =0.43.

Parameter estimate reflects regression equation for log-transformed Navigation Time.

Discussion

The present study documents in detail the number and types of barriers to care faced by a group of newly-diagnosed cancer patients. We found that patients faced a wide variety of social and instrumental barriers. Furthermore, the number and types of barriers differed by race/ethnicity. Certain barriers required more time-intensive interventions to address. Notably, intensity of Patient Navigation was associated with race/ethnicity and other social factors, The CHWs who prospectively collected these data had in-depth knowledge of the patients; therefore, this study may provide some insights lacking when administrative data or chart review are the primary data sources.

In general, difficulty with medical communications, a lack of social support, and problems with health insurance were the most common barriers to newly-diagnosed cancer patients. The importance of patient-centered communication is well-recognized in cancer care,¹⁷ and the present study reinforces that communication difficulties affect patients of diverse social backgrounds. Insurance problems were also common, and these ranged from difficulty understanding paperwork to a complete lack of healthcare insurance. Published studies confirm the association of insurance status with cancer health disparities.²⁹ However, the existing literature may not adequately emphasize the role of social support in cancer treatment; this was found to be a common barrier in the present study. A lack of social support may affect cancer care at every level, from healthcare communications (presence of a third person), provision of transportation and assistance with financial pressures, to emotional reassurance and support for treatment adherence.

Other common barriers for all patients in the present study included financial problems, comorbidity, transportation, patients’ fears/perceptions/beliefs, attitudes toward providers, and medical system barriers. The finding that patients needed help across a range of barriers underscores the complexity of navigating cancer patients. These results suggest that patient navigators require broad training in communications coaching, providing social support, and instrumental issues including health insurance, transportation, and others.

Another finding of the present study is that minority race/ethnicity is associated with a greater number and different spectrum of barriers to cancer care. These barriers may help to explain the mechanisms of observed treatment disparities. For example, patients facing a multitude of barriers may have financial, medical or other reasons to miss appointments. They may misinterpret treatment plans or mistrust providers,^{30, 31} leading to further delays in care.³² Patients with complex barriers may even receive different treatment recommendations from providers who perceive that patients can not “handle” intensive treatment due to their situations.⁵

Consistent with prior studies, race/ethnicity is also associated in this study with lower income, more comorbid illness, less private insurance coverage, lower educational level, lower health literacy, and unemployment.^{33, 34} Given these findings and the sample size, it is difficult to disentangle the effects of low socioeconomic status from race/ethnicity. Prior research findings vary as to what proportion of observed racial disparities is explained by socioeconomic factors.^{19, 35}

Finally, this study constructed a preliminary model of factors associated with the increased need for patient navigation assistance. The final model derived from stepwise selection included: minority race/ethnicity, being unmarried, unemployment, treatment at the university cancer center, and time in the study (reflecting the duration of active cancer treatment). These factors explain 43% of the variation in log Navigation Time in this sample of patients. As discussed above, race/ethnicity is a marker for social disadvantage and may represent a constellation of social risk factors.³⁶ Nevertheless, minority status provides a useful tool for targeting interventions to help cancer patients. From a practical perspective, it means that high-need patients can possibly be identified based on a few, baseline sociodemographic factors.

While the present study provides in-depth, prospective information about the barriers confronting some newly-diagnosed cancer patients, several limitations should be recognized. The outcome variable, Navigation Time, could theoretically be affected by unmeasured factors including the personality of the patient or CHW and the navigator’s level of experience. Nevertheless, the time expenditure of the CHW for each patient is an important indicator of patient navigation intensity and cost in our program.

Furthermore, the patient sample for this study is predominantly female, due to the much larger proportion of breast cancer patients than colorectal cancer patients enrolled in the study. Because approximately 90% of the patients are female the applicability of these findings to male cancer patients is uncertain. Also, the generalizability of these findings to cancer patients across the United States cannot be assumed. It is possible that these cancer patients and/or the local health care system may have unique characteristics that make application of these data to other communities problematic.

Despite these limitations, the present study has important health services research implications. Presently, the National Cancer Institute, the American Cancer Society, and cancer centers are devoting considerable resources to the design of interventions to address the problem of cancer health disparities. Patient navigation programs and other interventions seek to “level the playing field” for traditionally underserved cancer patients, and many of these interventions have already been put in place. However, the design of these programs has relied largely on theoretical models of cancer care and on population-based analyses of risk factors. However, patient-level data on the specific barriers that cancer patients face are lacking.

More detailed knowledge of patients’ barriers to care is provided by the present study. Our results highlight the fact that cancer patients’ barriers to optimal care are more complex than insurance coverage problems or transportation to appointments. Social support, medical communications, comorbid illnesses, and fears, attitudes and beliefs also may affect timeliness and quality of care. Patient navigation programs may be particularly well-suited to address these diverse barriers, but practitioners providing patient navigation may require additional training. Also, programs may need to focus more resources on the highest-risk patients because this service is resource-intensive. The present study provides a preliminary model to target patient navigation to cancer patients likely to need more help.

Conclusions

The present study shows that newly-diagnosed cancer patients’ most common “barriers to care” include communications barriers, lack of social support, and health insurance concerns. Number and types of barriers differ significantly between non-Hispanic white and minority patients. A multivariate model that predicts high need for patient navigation in our patient sample includes minority race/ethnicity, unemployment and unmarried status. These data may help in the design and targeting of future interventions to reduce cancer health disparities.