Abstract
Purpose:
Care coordination is a strategy to reduce healthcare navigation challenges for cancer patients. The objectives of this study were to assess the association between having a cancer care coordinator (CCC) and long-term health-related quality of life (HRQoL), and to evaluate whether this association differed by level of health literacy.
Methods:
A population-based sample of survivors diagnosed with breast, prostate, or colorectal cancer in 2015 from the Iowa Cancer Registry participated in an online survey conducted in 2017–2018 (N=368). Chi-squared tests and logistic regression were used to model the association between patient characteristics and having a cancer care coordinator. Linear regression was used to model the association between patient perception of having a cancer care coordinator and post-treatment physical or mental HRQoL by differing levels of health literacy while controlling for sociodemographic and clinical factors.
Results:
Most survivors (81%) reported having one healthcare professional who coordinated their cancer care. Overall, patient perception of having a coordinator was not significantly associated with physical HRQoL (p=0.118). However, participants with low health literacy (21%) who had a coordinator had significantly higher physical HRQoL scores compared to those who did not (adjusted mean difference 5.2, p=0.010), while not so for medium (29%) or high (51%) health literacy (p=0.227, and p=0.850, respectively; test for interaction p=0.001). Mental HRQoL was not associated with having a coordinator in our analyses.
Conclusion:
Findings suggest that care coordinators improved post-treatment physical HRQoL, particularly for participants with low health literacy. Care coordinators may be beneficial to the most vulnerable patients struggling to navigate the complex healthcare system during cancer treatment. Future research should focus on the mechanisms by which care coordination may affect post-treatment HRQoL.
Keywords: Care coordination, cancer care, health literacy, quality of life, care coordinator, patient navigator
Background
Care coordination has been identified as a strategy to improve manageability of the healthcare system, particularly for patients receiving treatment for cancer which often involves a range of specialists and care settings (e.g. inpatient, outpatient, home care). The concept of care coordination has been of immense interest to the field of cancer care since the Institute of Medicine reported that cancer patients often reported poorly coordinated care [1]. Cancer care coordination is defined by the Agency for Healthcare Research and Quality as “the deliberate organization of patient care activities between two or more participants (including the patient) involved in patient’s care to facilitate the appropriate delivery of health care services” [2].
A variety of cancer care coordination interventions have been assessed (teams and committees, telehealth, etc.), and having a cancer care coordinator (CCC; one health professional coordinating cancer care) is one approach that has been used. CCCs have been associated with better perceived care coordination [3], adherence to recommended care and appropriate healthcare utilization [4–6]. However, relatively little evidence exists to support the benefits of having a CCC, on health-related quality of life (HRQoL) [4, 6, 7]. This is especially important to examine since HRQoL is one of the most important outcomes that cancer patients consider when making difficult treatment-related decisions [8–13].
One study examined the use of CCCs as part of a broader effort to examine the effects of care coordination during and after treatment on long-term HRQoL. Results showed that care coordination is associated with improved HRQoL for breast cancer patients treated at academic medical centers [7]. It is especially interesting that this effect was largest for patients with low health literacy. That study was focused on general care coordination during and after cancer treatment, rather than the specific effect of having a designated care coordinator. Prior work indicates that those with low health literacy have difficulty understanding and retaining health information, which can result in lower utilization of healthcare services and worse physical and mental health [14–17]. Since care coordination appears to benefit this vulnerable subgroup, it is possible that having one CCC, in particular, may be an important part of a patient-centered, personalized intervention [3].
Our study used a National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) registry to conduct a population-based survey of Iowa survivors of breast, prostate, and colorectal cancer. The primary objectives of this study were to determine patient characteristics associated with having a CCC, and to evaluate whether patient perception of having a CCC was positively associated with post-treatment HRQoL. Additionally, we aimed to examine whether these benefits depended on the level of a participant’s health literacy.
Methods
Study Population
The Iowa Cancer Registry (ICR) was used to identify survivors diagnosed with breast, prostate, or colorectal cancer in 2015 in Iowa. ICR is an original member of NCI’s SEER program and is consistently recognized for its high-quality data. Survivors were invited by mail to participate in two sequential online surveys as part of the SEER Rapid Response Surveillance Study (Online Resource). The sampling and survey methodology have been previously described [18]. A total of 395 people completed the initial survey, and of those, 369 completed a second survey approximately three months later. Given that this study examined long-term HRQoL after treatment, those who answered “No” to a question asking if they were cancer free and “No” to a question asking if their cancer has come back were determined to still be in treatment (n=26) and were excluded from this study. Additionally, one participant was excluded because they did not answer a question about whether they had a CCC, leaving 368 for this analysis. The University of Iowa Institutional Review Board approved the protocol for this study.
Measures
Primary Exposures – Cancer Care Coordinators and Health Literacy
Our primary exposures of interest included having a CCC and level of health literacy. To measure whether participants perceived that they had a CCC, we used answers to the following previously used question: “During your treatment, was there one health professional who COORDINATED your cancer care?” [3, 7]. Participants were given the response options “Yes”, “No”, or “Don’t Know”. Participants were categorized as “CCC” (patient perception of having a CCC) if they answered “Yes”, and they were categorized as “No/Don’t Know” if they answered “No” or “I Don’t Know.” A previously validated health literacy scale was used, which included the following three questions: 1) How often do you have problems learning about your medical condition because of difficulty understanding written information?, 2) How often do you have someone help you read hospital materials?, and 3) How often can you fill out medical forms by yourself?[19, 20] Each question had a response scale from 1 (always) to 5 (never). The items were totaled and then categorized (cut points based on prior studies) to form a measure of low, moderate, and high health literacy [3, 21, 22]. The responses to all of these questions were from the first survey.
Primary Outcome – Health-Related Quality of Life
Our primary outcome of interest was long-term HRQoL. HRQoL items were from the Patient-Reported Outcomes Measurement Information System (PROMIS) global domains for mental and physical health (from the first survey) [23]. PROMIS is a NIH initiative developed to assess important aspects of HRQoL using a set of survey questions. Validation for the measures has been performed in cancer populations [23–28]. The measures are scored using responses to eight questions regarding overall health, quality of life, and other physical and mental health concerns (Table 1). PROMIS measures were then scored by summing responses and converting to a T score, which have been normalized and calibrated against the U.S. population such that 50 represents the mean score for the general population and higher scores indicate better HRQoL [24, 25, 29]. The outcome was assessed as a continuous variable. Per prior research examining PROMIS measures in similar populations, the minimal clinically meaningful difference was defined as 3 points [26, 27, 30, 31].
Table 1.
PROMIS physical and mental health-related quality of life items
| In general, how would you rate your physical health? Excellent, Very good, Good, Fair, or Poor |
|
| To what extent are you able to carry out your everyday physical activities such as walking, climbing stairs, carrying groceries, or moving a chair? Completely, Mostly, Moderately, A little, or Not at all |
|
| In the past 7 days, how would you rate your pain on average? 0 – 10 Numerical Scale: 0 = no pain, 10 = worst pain imaginable |
|
| In the past 7 days, how would you rate your fatigue on average? Very severe, Severe, Moderate, Mild, or None |
|
| In general, how would you rate your satisfaction with your social activities and relationships? Excellent, Very good, Good, Fair, or Poor |
|
| In the past 7 days, how often have you been bothered by emotional problems such as feeling anxious, depressed or irritable? Never, Rarely, Sometimes, Often, or Always |
|
| In general, would you say your quality of life is: Excellent, Very good, Fair, or Poor |
|
| In general, how would you rate your mental health, including your mood and your ability to think? Excellent, Very good, Good, Fair, or Poor |
Sociodemographic Factors
Registry abstraction records provided age, sex, race/ethnicity, and residential zip code. The residential zip code and Rural-Urban Commuting Area (RUCA) system developed by the University of Washington were applied to define rurality [34]. Census tracts located in urbanized areas (>50,000) or those with a large proportion of residents commuting to urbanized areas were considered “urban”. Micropolitan areas (contain urbanized clusters of 10,000 – 49,999) or those with a large proportion of residents commuting to urbanized clusters were considered “large rural” and small towns (contain urbanized clusters of 2,500 – 9,999) were considered “small rural”.
Self-reported marital status was collected from the first survey (married or living with partner; single (divorced, separated, or never married); and widowed). Additional socioeconomic information was ascertained from the second survey: annual household income (less than $35,000, $35,001 – $50,000; $50,001 – $75,000; $75,001 – $100,000; greater than $100,000, and missing or prefer not to respond), employment status (employed for wages; self-employed; retired; and unemployed (out of work, unable, or homemaker) or missing), and health insurance at diagnosis (private/employer; Medicaid, Medicare, Other Gov.; and uninsured or missing).
Clinical Factors
Registry abstraction records provided cancer site, stage, surgery, radiation, and chemotherapy. A treatment summary variable was also created (surgery only; surgery and chemotherapy and radiation (CRT); surgery and radiation; surgery and chemotherapy; and other). Participants were matched to the hospital where they received most of their cancer care according to ICR records, which were categorized by NCI designation. NCI designation was based on the NCI Cancer Center online directory, and included both NCI-designated Cancer Centers and NCI-designated Comprehensive Cancer Centers [35]. A hospital must have education programs, multi-disciplinary research activities, and community outreach to obtain NCI designation. Finally, the following were calculated from responses to questions from the first survey: body mass index (BMI; underweight or normal weight; overweight; and obese), and the Charlson comorbidity index [32, 33].
Analysis
Statistical analyses were carried out using SAS version 9.4 (SAS Institute, Cary, NC). Survey weights were calculated and used in analyses to account for the stratified sampling methodology and response rates to obtain more accurate parameter estimates. In analyses of covariates included only on the second survey, a category of “missing” was added or combined with another category. Treatments (i.e. chemotherapy, radiation and surgery) were considered individually (Yes/No) and together in the treatment summary variable, and the most appropriate was selected. For variables available from both survey and registry data (cancer site, age, sex), the registry variable was used since they are based on medical record review. All variables were considered for inclusion, but covariate selection for all multivariable models was guided by univariate analyses (p<0.05), as well as consideration of known associated factors and variables of interest (health literacy, CCC).
People who reported having a CCC were compared with those who did not by age group, sex, rurality, marital status, annual household income, employment status, health insurance at diagnosis, health literacy level, cancer site, treatment summary, and hospital NCI-designation using Wald Chi-squared tests. A multivariable model was employed to identify potential predictors of patient perception of having a CCC. In addition to variables significant in univariate analysis and variables of interest, treatment summary was forced into the multivariable logistic model estimating odds of patient perception of having a CCC because the number of treatment components (and time spent in the hospital) was hypothesized to relate to whether a person has a CCC.
Unadjusted physical and mental HRQoL score means were assessed, and ANOVA p-values were calculated for the previously listed characteristics, as well as cancer stage, BMI, and Charlson comorbidity score. Multivariate linear regression was used to evaluate the association between patient perception of having a CCC and post-treatment HRQoL, for both physical and mental HRQoL domains. In addition to the variables of interest and those significant in univariate analyses, age and Charlson comorbidity were forced into the physical model due to relevance to physical HRQoL. An interaction between having a CCC and health literacy was tested by adding the cross-product term to the HRQoL models, and additionally by performing the analysis stratified by health literacy level. Lastly, because NCI-designated cancer centers tend to be much larger than typical community hospitals where patients receive cancer care, and because patients who receive care at these NCI centers tend to be different than patients who receive care in community hospitals, we conducted a sensitivity analysis excluding those treated at an NCI-designated cancer center.
Results
The sample consisted of 142 breast cancer survivors, 88 colorectal cancer survivors, and 138 prostate cancer survivors. At the time of the survey, the study population was a mean of 2.7 years out from diagnosis. Table 2 illustrates the demographic and clinical characteristics by whether they perceived having a CCC. Race/ethnicity was excluded from Table 2 due to small cell counts. About 97% of the sample self-identified as non-Hispanic white. None of the factors examined were associated with perception of having a CCC, apart from NCI designation. Participants who were treated at NCI-designated cancer centers had lower odds of having a CCC, both before and after adjustment.
Table 2.
Characteristics of participants by cancer care coordinator (CCC), and odds of patient perception of a CCC
| N=368 | CCC (n=299)a | No/Don’t Know (n=69) | Chi square p-value | Adjusted Odds Ratio & 95% CI | |
|---|---|---|---|---|---|
| Unweighted n (weighted row%) | |||||
| Age Groupb | |||||
| Under 65 | 178 | 144 (80.7%) | 34 (19.3%) | p=0.717 | Not included in |
| 65 and Over | 190 | 155 (82.2%) | 35 (17.8%) | model | |
| Sex | |||||
| Male | 188 | 154 (82.8%) | 34 (17.2%) | p=0.532 | Not included in |
| Female | 180 | 145 (80.2%) | 35 (19.8%) | model | |
| Ruralityc | |||||
| Urban | 191 | 158 (83.2%) | 33 (16.8%) | p=0.617 | Not included in |
| Large Rural | 68 | 52 (77.5%) | 16 (22.5%) | model | |
| Small Rural | 108 | 89 (81.6%) | 19 (18.4%) | ||
| Marital Statusd | |||||
| Married or Living with Partner | 302 | 249 (82.8%) | 53 (17.2%) | p=0.361 | Not included in |
| Single | 44 | 33 (74.4%) | 11 (25.6%) | model | |
| Widowed | 18 | 13 (72.9%) | 5 (27.1%) | ||
| Annual Household Income | |||||
| Less than $35,000 | 57 | 48 (84.9%) | 9 (15.1%) | p=0.614 | Not included in |
| $35,001 – $50,000 | 43 | 31 (70.5%) | 12 (29.5%) | model | |
| $50,001 – $75,000 | 67 | 57 (85.1%) | 10 (14.9%) | ||
| $75,001 – $100,000 | 55 | 46 (84.1%) | 9 (15.9%) | ||
| Greater than $100,000 | 62 | 50 (81.0%) | 12 (19.0%) | ||
| Missing or Prefer not to Respond | 84 | 67 (80.7%) | 17 (19.3%) | ||
| Employment Statuse | |||||
| Employed for Wages | 128 | 102 (79.6%) | 26 (20.4%) | p=0.885 | Not included in |
| Self-employed | 30 | 25 (84.7%) | 5 (15.3%) | model | |
| Retired | 159 | 130 (82.0%) | 29 (18.0%) | ||
| Unemployed or Missing | 51 | 42 (83.1%) | 9 (16.9%) | ||
| Health Insurance at Diagnosis | |||||
| Private/Employer | 203 | 163 (80.8%) | 40 (19.2%) | p=0.818 | Not included in |
| Medicaid, Medicare, Other Gov. | 127 | 106 (83.2%) | 21 (16.8%) | model | |
| Uninsured or Missing | 38 | 30 (79.7%) | 8 (20.3%) | ||
| Health Literacy Level | |||||
| Low (score ≤12) | 75 | 58 (76.9%) | 17 (23.1%) | p=0.233 | 1.00 (REF) |
| Medium (13≤ score ≤14) | 105 | 82 (78.7%) | 23 (21.3%) | 1.09 (0.51 – 2.33) | |
| High (score =15) | 188 | 159 (84.8%) | 29 (15.2%) | 1.74 (0.86 – 3.50) | |
| Cancer Site | |||||
| Colorectum | 88 | 69 (78.4%) | 19 (21.6%) | p=0.295 | Not included in |
| Breast | 142 | 113 (79.4%) | 29 (20.6%) | model | |
| Prostate | 138 | 117 (85.3%) | 21 (14.7%) | ||
| Treatment Summaryf | |||||
| Surgery Only | 147 | 118 (81.2%) | 29 (18.8%) | p=0.559 | 1.00 (REF) |
| Surgery and CRT | 51 | 40 (78.7%) | 11 (21.3%) | 0.79 (0.34 – 1.83) | |
| Surgery and Radiation | 60 | 47 (78.9%) | 13 (21.1%) | 0.81 (0.38 – 1.70) | |
| Surgery and Chemotherapy | 45 | 37 (79.5%) | 8 (20.5%) | 0.78 (0.31 – 1.96) | |
| Other | 65 | 57 (88.0%) | 8 (12.0%) | 1.72 (0.68 – 4.36) | |
| Hospital NCI Designation | |||||
| Not NCI-Designated | 310 | 258 (83.3%) | 52 (16.7%) | p=0.048 | 1.00 (REF) |
| NCI-Designated Cancer Center | 41 | 26 (63.7%) | 15 (36.3%) | 0.33 (0.16 – 0.70) | |
| Missing Hospital Classification | 17 | 15 (87.9%) | 2 (12.1%) | 0.92 (0.18 – 4.70) | |
All tables report unweighted frequencies and weighted proportions. Survey weights were calculated and used in analyses to account for the stratified sampling methodology and response rates. All variables in Table 2 were considered for inclusion in the model
Current age (at the time of the survey).
Rural-Urban Commuting Area (RUCA) for rurality categorization (RUCA Category B). 1 missing rurality.
Single = divorced, separated, or never married. 4 missing marital status.
Unemployed = out of work, unable, or homemaker.
CRT = chemotherapy and radiation therapy.
Overall, survivors who perceived having a CCC tended (nonsignificantly) to have higher physical HRQoL scores after adjustment (Table 3), and this relationship was modified by health literacy level. Other factors positively associated with long-term physical HRQoL on univariate analyses included higher income, insurance coverage, being employed, higher health literacy, lower stage, white race, treatment without chemotherapy, and lower BMI (Table 3). After adjustment, higher income (p=0.007), being employed (p=0.010), high health literacy (p=0.001), white race (p=0.001), non-chemotherapy treatment regimens (p=0.038), and under-normal weight BMI (p=0.002) were associated with higher physical HRQoL scores. Although not reaching statistical significance, those who perceived having a CCC appeared to have slightly higher physical HRQoL scores (multivariable-adj. means: CCC = 47.8, No/Don’t Know = 46.0; p=0.118).
Table 3.
Cancer care coordinators (CCCs) and long-term physical HRQoL score unadjusted and adjusted means
| PROMIS Physical Scorea | |||||
|---|---|---|---|---|---|
| N=365 | Unadjusted Mean (SE) | ANOVA p-value | Multivariable-adj. mean, (95% CI) | p-value | |
| Cancer Care Coordinator | p=0.095 | p=0.118 | |||
| No/Don’t Know | 68 | 49.7 (1.1) | 46.0 (42.0–50.0) | ||
| CCC | 297 | 51.6 (0.5) | 47.8 (44.2–51.3) | ||
| Age Groupb | p=0.727 | p=0.752 | |||
| Under 65 | 175 | 51.4 (0.7) | 47.0 (43.5–50.6) | ||
| 65 and Over | 190 | 51.1 (0.7) | 46.7 (42.9–50.6) | ||
| Annual Household Income | p<0.001 | p=0.007 | |||
| Less than $35,000 | 56 | 48.0 (1.0) | 44.5 (40.4–48.5) | ||
| $35,001 – $50,000 | 43 | 48.6 (1.4) | 44.2 (40.0–48.5) | ||
| $50,001 – $75,000 | 67 | 52.7 (0.8) | 47.7 (43.7–51.6) | ||
| $75,001 – $100,000 | 55 | 52.8 (1.0) | 47.9 (43.7–52.0) | ||
| Greater than $100,000 | 61 | 55.6 (1.0) | 48.8 (44.8–52.9) | ||
| Prefer not to answer or Missing | 83 | 49.3 (1.1) | 48.3 (44.5–52.1) | ||
| Employment Status | p<0.001 | p=0.010 | |||
| Employed for Wages | 126 | 53.1 (0.7) | 49.1 (45.0–53.2) | ||
| Self-employed | 29 | 53.3 (1.2) | 49.0 (44.6–53.4) | ||
| Retired | 159 | 51.2 (0.6) | 47.0 (43.3–50.6) | ||
| Unemployed or Missing | 51 | 46.0 (1.7) | 42.4 (37.9–46.9) | ||
| Insurance at Diagnosis | p=0.003 | p=0.401 | |||
| Private/Employer | 200 | 51.8 (0.6) | 46.0 (42.2–49.9) | ||
| Medicaid, Medicare, or Other Gov. | 127 | 51.8 (0.7) | 47.5 (43.6–51.3) | ||
| Uninsured or Missing | 38 | 46.9 (2.0) | 47.1 (42.1–52.2) | ||
| Health Literacy Level | p<0.001 | p=0.001 | |||
| Low (score ≤12) | 75 | 48.2 (1.1) | 44.8 (40.8–48.8) | ||
| Medium (13≤ score ≤14) | 105 | 49.4 (1.0) | 46.9 (43.3–50.6) | ||
| High (score =15) | 185 | 53.5 (0.5) | 48.9 (45.1–52.7) | ||
| Race | p<0.001 | p=0.001 | |||
| White | 357 | 51.6 (0.4) | 51.1 (48.4–53.8) | ||
| Non-White | 8 | 41.5 (3.6) | 42.7 (37.1–48.2) | ||
| AJCC Stage | p=0.002 | p=0.712 | |||
| I | 143 | 52.2 (0.6) | 47.5 (43.6–51.4) | ||
| II | 119 | 52.2 (0.8) | 47.4 (43.5–51.3) | ||
| III/IV | 77 | 48.0 (1.2) | 46.1 (42.3–50.0) | ||
| Unknown | 26 | 50.3 (1.8) | 46.5 (41.5–51.5) | ||
| Treatment Summary | p=0.001 | p=0.038 | |||
| Surgery Only | 146 | 53.1 (0.7) | 48.7 (44.8–52.5) | ||
| Surgery and CRT | 50 | 48.5 (1.3) | 44.9 (40.8–49.0) | ||
| Surgery and Chemo | 44 | 48.1 (1.2) | 45.7 (41.3–50.1) | ||
| Surgery and Radiation | 60 | 51.6 (1.0) | 48.2 (44.1–52.3) | ||
| Other | 65 | 51.1 (1.3) | 47.0 (43.1–50.8) | ||
| Current BMIc | p=0.002 | p=0.002 | |||
| Under-Normal Weight (Under 24.9) | 67 | 53.2 (1.0) | 49.2 (45.1–53.2) | ||
| Overweight (25.0 – 29.9) | 135 | 52.4 (0.7) | 46.5 (42.7–50.3) | ||
| Obese (Over 30.0) | 159 | 49.5 (0.7) | 45.0 (41.4–48.5) | ||
| Charlson Comorbidity | p=0.697 | p=0.992 | |||
| Zero | 347 | 51.3 (0.5) | 46.9 (43.7–50.1) | ||
| Greater than zero | 18 | 50.5 (2.0) | 46.9 (42.2–51.5) | ||
| Hospital NCI Designation | p=0.058 | p=0.109 | |||
| NCI-Designated Cancer Center | 40 | 54.3 (1.5) | 47.8 (43.6–52.0) | ||
| Not NCI-Designated | 309 | 50.9 (0.5) | 45.2 (42.0–48.4) | ||
| Missing | 16 | 52.0 (1.8) | 47.6 (42.2–53.1) | ||
3 missing PROMIS physical score. Least square means, F tests, and p-values are from a multivariable linear model adjusted for the variables in the table.
Current age (at the time of the survey).
4 missing BMI.
Among participants with low health literacy, perception of having a care coordinator was significantly associated with higher long-term physical HRQoL (mean difference=5.2, p=0.010), while among participants with medium and high health literacy, the association was not significant (p=0.227, and p=0.850, respectively). Figure 1 displays the long-term physical HRQoL scores by CCC vs. no CCC stratified by health literacy status. The test for interaction between CCC and health literacy level was statistically significant at p=0.001.
Figure 1.
Physical HRQoL and cancer care coordinator (CCC) stratified by health literacy level
Association between the presence of a cancer care coordinator (CCC) and physical health-related quality of life (HRQoL) stratified by health literacy. Adjusted physical HRQoL score means are graphed by health literacy level. Least square means, F tests, and p-values are from a multivariable linear model adjusted for age, income, insurance, employment, stage, race, treatment, BMI, Charlson comorbidity, and NCI-designation of treatment hospital. Test for interaction between CCC and health literacy level, p= 0.001.
Univariable and multivariable-adjusted mental HRQoL means are displayed in Table 4. Univariate analysis showed income, employment, insurance, health literacy, race, chemotherapy, and NCI-designation of treatment hospital were associated with mental HRQoL. After adjustment, higher income (p=0.002), high health literacy (p=0.011), white race (p=0.022), and treatment at an NCI-designated cancer center (p=0.038) were associated with higher mental HRQoL scores. A test for interaction between patient perception of having a CCC and health literacy level was not statistically significant (p=0.072). Patient perception of having a CCC was not associated with long-term mental HRQoL (p=0.436).
Table 4.
Cancer care coordinators (CCCs) and long-term mental HRQoL score unadjusted and adjusted means
| PROMIS Mental Scorea | |||||
|---|---|---|---|---|---|
| N=365 | Unadjusted Mean (SE) | ANOVA p-value | Multivariable-adj. mean, (95% CI) | p-value | |
| Cancer Care Coordinator | p=0.431 | p=0.436 | |||
| No/Don’t Know | 68 | 51.4 (1.1) | 49.3 (46.2–52.4) | ||
| CCC | 297 | 52.3 (0.5) | 50.2 (47.3–53.0) | ||
| Annual Household Income | p<0.001 | p=0.002 | |||
| Less than $35,000 | 56 | 48.8 (1.0) | 46.9 (43.5–50.4) | ||
| $35,001 – $50,000 | 43 | 50.0 (1.3) | 47.8 (44.1–51.5) | ||
| $50,001 – $75,000 | 67 | 54.0 (0.9) | 51.1 (47.8–54.4) | ||
| $75,001 – $100,000 | 55 | 54.4 (0.9) | 51.8 (48.7–54.8) | ||
| Greater than $100,000 | 61 | 55.7 (1.2) | 52.0 (48.5–51.8) | ||
| Prefer not to answer or Missing | 83 | 49.7 (1.0) | 48.7 (43.5–50.4) | ||
| Employment Status | p<0.001 | p=0.185 | |||
| Employed for Wages | 126 | 53.4 (0.8) | 51.1 (48.0–54.1) | ||
| Self-employed | 29 | 53.6 (1.3) | 51.2 (47.3–55.0) | ||
| Retired | 159 | 52.3 (0.6) | 49.8 (46.8–52.8) | ||
| Unemployed or Missing | 51 | 47.7 (1.4) | 46.7 (42.6–50.9) | ||
| Health Insurance at Diagnosis | p=0.046 | p=0.189 | |||
| Private/Employer | 200 | 52.3 (0.6) | 48.6 (45.7–51.5) | ||
| Medicaid, Medicare, or Other Gov. | 127 | 52.8 (0.7) | 50.2 (47.4–53.1) | ||
| Uninsured or Missing | 38 | 49.1 (1.7) | 50.3 (45.6–55.0) | ||
| Health Literacy Level | p<0.001 | p=0.011 | |||
| Low (score ≤12) | 75 | 49.9 (1.0) | 48.4 (45.0–51.7) | ||
| Medium (13≤ score ≤14) | 105 | 50.4 (0.7) | 49.3 (46.5–52.0) | ||
| High (score =15) | 185 | 54.0 (0.6) | 51.5 (48.5–54.5) | ||
| Race | p=0.014 | p=0.022 | |||
| White | 357 | 52.3 (0.4) | 51.7 (49.6–53.9) | ||
| Non-White | 8 | 46.4 (2.7) | 47.7 (43.6–51.7) | ||
| Chemotherapy | p=0.024 | p=0.099 | |||
| No | 264 | 52.7 (0.5) | 50.5 (47.8–53.3) | ||
| Yes | 101 | 50.4 (0.9) | 48.9 (45.8–51.9) | ||
| Hospital NCI Designation | p=0.022 | p=0.038 | |||
| Not NCI-Designated | 309 | 51.7 (0.5) | 48.8 (46.7–50.9) | ||
| NCI-Designated Cancer Center | 40 | 55.7 (1.4) | 52.3 (49.0–55.5) | ||
| Missing | 16 | 51.6 (2.1) | 48.1 (43.2–53.0) | ||
3 missing PROMIS mental score. Least square means, F tests, and p-values are from a multivariable linear model adjusted for the variables in the table.
The sensitivity analysis that excluded individuals treated at an NCI-designated facility yielded the same pattern of results as the main physical HRQoL results, both overall (mean difference=1.58, p=0.222) and regarding the interaction between patient perception of having a CCC and health literacy level (p=0.016). The same pattern of results for physical HRQoL were observed for those who were treated at NCI-designated cancer centers (mean difference=3.47, p=0.425). However, given the small number of people in this group (n=40), we were unable to evaluate the relationship by health literacy level.
Discussion
While no patient-level characteristics were associated with having a CCC, survivors who were treated at NCI-designated cancer centers were less likely to perceive having a CCC. Survivors with low health literacy had higher physical HRQoL scores when they had a CCC during treatment. This effect was not observed for those with higher health literacy. The mean physical HRQoL score for those with low health literacy had a 5-point increase when the patient perceived having a CCC, which reflects a clinically significant difference [26, 27, 30, 31]. No associations were found between patient perception of having a CCC and mental HRQoL. In this study, most (81%) respondents reporting having had one healthcare professional coordinating their cancer care.
Our study adds to the existing evidence that having a CCC may be more beneficial to those with lower health literacy, and specifically that this may be associated with better HRQoL later in life. Similar to previous work, our study found an association between high health literacy and higher HRQoL [15–17]. Other studies have also found similar relationships between health literacy and having a CCC. One prior study found that an association between “care coordination” (a measure that combined Survivorship Care Plans and having one CCC) and quality of life was strongest for people who had lower health literacy in a population of breast cancer patients being treated at academic medical centers [7]. Health literacy has been referred to as the “neglected, final pathway to a high-quality health care” by the Institute of Medicine [36]. Patients with low health literacy often have a more difficult time navigating the healthcare system, where they can have difficulty understanding treatment options, getting their questions answered, and self-monitoring their own care [14, 15]. Thus, it is not surprising that patients with low health literacy have been found to use health resources less efficiently, and have poorer health and HRQoL outcomes [16, 22, 37].
If individuals with low health literacy may be particularly likely to benefit from having a CCC, it is a concern that they were somewhat less likely to report having a CCC. The present findings indicate that prioritizing CCCs for patients with lower health literacy may be an effective strategy, particularly at facilities that may have limited personnel to do so. Health literacy screening for patients may be useful for providers to target coordination efforts to those with more need. The validated health literacy scale used for our study was merely three survey questions, which could lend itself well to use as a screening tool for health literacy in clinical settings. Furthermore, there is a one-item health literacy screening tool that has been used in clinical practice and may serve as a potential alternative to questions we used in our survey [38].
Interestingly, our findings suggested that those being treated at NCI-designated cancer centers were less likely to perceive having a CCC. This could be a function of the way many larger cancer centers facilitate cancer care, using more of a team approach to coordinate care across clinics and specialties, as opposed to designating a single person to do so. The effectiveness of having a single CCC vs. a team fulfilling that role is unknown, but our study focused on the association between the perception of having a CCC to coordinate all care with HRQoL. Given that NCI-designated cancer centers are inherently larger and more complex facilities, it is possible they cannot designate a single person to coordinate all aspects of care. We did, however, consider the potential of misclassification, and therefore performed the sensitivity analysis without individuals treated at an NCI-designated facility, which confirmed the overall findings.
Our primary exposure of interest was based on self-report that assessed participants’ perceptions of whether someone was coordinating their care, rather than if they were assigned a CCC by their provider (e.g. a patient navigator). Our study did not obtain the type of health-care professional that “coordinated care”, nor did we evaluate what specific activities they performed. If our findings represent a true causal relationship, possible mechanisms could involve healthcare professionals who manage appointments, answer questions, and otherwise ensure that a patient’s health care needs are met, which may lead to more appropriate utilization of care, better adherence to care, better patient satisfaction, and so on [4, 6, 39]. All of this would contribute to higher post-treatment HRQoL [3, 4, 7].
Of note, we did not find that patient perception of having a CCC was associated with post-treatment mental HRQoL. Mental HRQoL may be more likely associated with other initiatives during treatment, for example, cancer survivor support groups or self-management programs for psychosocial concerns accessed via mobile apps may help with mental HRQoL [40–42]. The fact that the significant associations in this study were only observed for physical HRQoL may be indicative of the mechanisms by which having a CCC could be related to HRQoL. For example, it may mean that the way in which CCC’s help patients (i.e., managing appointments, improving adherence and appropriate utilization of care, etc.) plays a more important role in pathways related to physical HRQoL than those of mental HRQoL.
Our results further suggest that may be particularly beneficial to those with low health literacy who already find it more difficult to navigate the complex world of cancer care [3, 7, 43]. It is possible that some of these CCCs were actually cancer patient navigators, and thus were trained to help resolve barriers to care, encourage adherence to care recommendations during treatment, and emphasize importance of follow-up care [4, 6, 39, 44]. Studies have found other tasks of patient navigators to include reducing the time between diagnosis and treatment via reminders and answering questions (e.g. easing worries regarding insurance) [39]. Other tasks included providing education to improve knowledge and attitudes, advocating for services to fill service gaps, arranging transportation, mapping out locations for cancer services, helping with insurance paperwork, addressing cultural appropriateness, and providing emotional support [39]. Investigation of effective patient navigation is a growing field of research and programs have been shown to improve patient outcomes, particularly to address health-disparities (low-income, uninsured, limited English proficiency etc.) [39, 45, 46].
A limitation of this study was that the online mode of survey administration may have led to a sample of cancer survivors who have better access to, and capability of using, the internet compared to the overall population of cancer survivors [18]. Of note, respondents and non-respondents did not differ in rurality [18]. This study used a survey for patients to recall whether they had a CCC and to collect current HRQoL. It is possible that a person’s current HRQoL could have influenced their perception of having had a CCC or that some unmeasured confounding factor influenced both CCC and HRQoL. We hypothesize that if respondents and non-respondents differed, it would likely be the case that respondents would have higher health literacy, more often recall having a CCC, and report higher HRQoL than non-respondents, rather than the other way around. This would potentially bias our analyses toward the null, yet we still detected a relationship between having a CCC and HRQoL that differed by health literacy. Furthermore, the variables found to be associated with HRQoL in our population mirror those found in previous studies, including health literacy, race, insurance status, cancer stage, employment, and income [37, 47–50]. Additionally, the HRQoL estimates in this study are consistent with those found for 2 to 3-year cancer survivors in a previous large-scale population-based study using the PROMIS physical and mental HRQoL scales, providing confidence for our HRQoL measurement [51].
The results of the study address a gap in the literature and have useful implications for patient care. Our results complement those of previous studies [3, 7], but our study also expands to a wider variety of cancer types, as both previous studies focused solely on breast cancer patients. The population-based sample was also an advantage, as prior studies recruited participants from hospitals. Investigators in the field have called for more research focusing on care coordination, and the role of health literacy has just begun to emerge in the literature [3, 7]. Additionally, the patient-reported nature of the data allowed for us to evaluate whether participants perceived having a CCC, rather than information on if the hospital considers them to have had a CCC.
Future studies should attempt to determine the mechanisms by which CCCs are associated with HRQoL, and whether these associations depend on the type of healthcare professional serving as the coordinator. Researchers should also investigate resources or services addressing psychosocial issues for cancer survivors, which could improve mental HRQoL. It will also be important to thoroughly evaluate the role of coordination throughout the cancer care continuum, including care coordination and survivorship care planning that takes place after cancer treatment [6, 39, 43].
In conclusion, our findings suggested that the patient perception of having a CCC during cancer care was associated with better long-term physical HRQoL, specifically for participants with low health literacy. This suggests that CCCs may be particularly useful to patients who are already struggling to navigate the complex healthcare system during cancer treatment. Prioritizing designated CCCs for patients with lower health literacy may be an effective strategy for targeting care coordination resources to the patients most likely to benefit from them.
Supplementary Material
Funding:
This work was supported under TORFP 2016-07 (Surveillance, Epidemiology and End Results Rapid Response Surveillance Study) HHSN261201300020I/HHSN26100014. This work was also supported by the University of Iowa Holden Comprehensive Cancer Center, which is funded in part by NIH/ NCI P30 CA086862.
Footnotes
Conflicts of Interest: None of the authors have any conflicts of interests to declare.
Code Availability: The code used for analysis are available from the corresponding author upon reasonable request.
Ethics Approval: The questionnaire and research protocol were approved by the University of Iowa Institutional Review Board, with a waiver of documentation of consent.
Consent to Participate: Patients were provided a letter containing the elements of consent. Given that this was an online survey, a waiver of documentation of consent was obtained from our Institutional Review Board. Participants had to click on an ‘Agree’ button to consent and enroll in the study.
Availability of Data and Material:
Our data are not deposited in publicly available repositories. However, the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Our data are not deposited in publicly available repositories. However, the datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.