Sullenger_Honors_Thesis_Final.docx

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Sociodemographic Factors and Cancer Clinical Trial Enrollment Among Adolescents and Young Adults

By

Rebecca D. Sullenger

Senior Honors Thesis

Department of Health Policy and Management University of North Carolina at Chapel Hill

April 30, 2020

Approved: ____________________ Andrew B. Smitherman, Thesis Advisor

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Abstract

Purpose – Adolescents and young adults (AYAs) have experienced slower increases in cancer survival rates compared to those of other age groups. One potential explanation for this limited improvement is the low rate of AYA enrollment in cancer clinical trials. This study examines the possible association of insurance type, race, ethnicity, and marital status with AYA clinical trial enrollment in the hope that our findings will help providers to improve enrollment among AYAs. Methods – We conducted a retrospective cohort study of AYAs treated for cancer at UNC-affiliated hospitals between April 2014 and April 2019. Data were obtained through the linkage of Carolina Data Warehouse and the UNC Cancer Registry. Potential associations were tested for significance through bivariable analysis. Possible confounders were controlled for in a

multivariable analysis.

Results – Our final sample included 1,798 patients. Patients without insurance were the most likely of any group to enroll in a trial and were 1.87 times as likely as those with public

insurance to enroll [95% CI (1.25, 2.80)]. Individuals with private insurance were 1.32 times as likely as those with public insurance to enroll in cancer clinical trials, although this relationship was borderline significant. Hispanic individuals were about half as likely as non-Hispanic individuals to enroll [95% CI (0.30, 0.95)] Marital status had a borderline significant association with enrollment, with married patients being the most likely to enroll.

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Purpose

Each year about 70,000 adolescents and young adults (AYAs), or people between the ages of 15 and 39, are diagnosed with cancer in the United States (National Cancer Institute, 2018). Compared to other age groups, AYAs have generally experienced a slower increase in cancer survival rates (Barakat, Schwartz, Reilly, Deatrick, & Balis, 2014; Collins, Malvar, Hamilton, Deapen, & Freyer, 2015; Harlan et al., 2011). While this discrepancy is likely a result of multiple interacting elements, such as treatment location, insurance status, and socioeconomic factors, it is possible that the underrepresentation of AYAs in cancer clinical trials is one of the most significant contributing factors (A. Bleyer, Budd, & Montello, 2006; W. A. Bleyer et al., 1997; Collins et al., 2015). Participation in clinical trials is of particular concern because

participation can directly benefit patients through early access to novel medications and therapies as well as benefit AYAs in general by providing scientists with data to optimize treatment protocols for this age group.

In 2006, Bleyer et al. found that decreased participation in clinical treatment trials was the single factor that correlated most strongly with worse outcomes (A. Bleyer et al., 2006). Further, a 2011 study found that only 14% of AYAs diagnosed with cancer in 2006 subsequently enrolled in a clinical trial (Parsons, Harlan, Seibel, Stevens, & Keegan, 2011), in stark contrast to the estimated 60% enrollment of children under the age of 15 (A. Bleyer et al., 2006; Fern & Whelan, 2010; Weiss et al., 2015). Although a causal relationship is difficult to establish, clinical trial enrollment patterns among AYAs need to be further studied to determine the implications of this correlation.

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that treatment-related factors (cancer type, cancer stage, treatment facility type, trial availability) contribute to this discrepancy (A. Bleyer et al., 2006; Fern & Whelan, 2010). Multiple studies have shown that as AYAs age, they are less likely to receive care in pediatric hospitals from pediatric oncologists and are less likely to enroll in clinical trials (Fern & Whelan, 2010; Parsons et al., 2011). Further, teenagers and young adults who are treated outside of pediatric hospitals may not have access to appropriate trials for their age and type of tumor (Fern & Whelan, 2010).

In comparison to the treatment-related factors discussed above, limited data are available regarding the role of sociodemographic factors (patient sex, race/ethnicity, insurance status) in AYA cancer trial enrollment. One study found significant differences in enrollment rate by race and ethnicity with American Indians and Alaskan Natives having the lowest enrollment rate (Parsons et al., 2011). In contrast, other studies have not found significant differences in enrollment rates based on race and ethnicity (Collins et al., 2015; Fern & Whelan, 2010). In addition, patient gender may impact enrollment rates. Two studies found that AYA males are less likely to participate in clinical trials compared to females (Parsons et al., 2011; Zullig et al., 2016). However, another study found an insignificant difference in enrollment by gender

(Collins et al., 2015). Insurance coverage, or lack thereof, and type of insurance could also impact patient outcomes (Sanford, Beaumont, Snyder, Reichek, & Salsman, 2017). Parsons et al. (2011), found that AYAs with insurance were more likely than patients without insurance to enroll in cancer clinical trials.

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compare patient enrollment data organized by race and ethnicity to existing literature to determine if our data support previous results. Finally, we will explore the effect, if any, that marital status has on enrollment rates of AYAs in such trials. We hypothesize that patient lack of insurance, racial minority status, Hispanic ethnicity, and single or divorced status will be

associated with lower enrollment in cancer clinical trials. By analyzing these factors, we hope to expand on current literature with the intent of helping to increase enrollment rates of AYAs in clinical trials.

Methods

Study design and data source

We conducted a retrospective cohort study (Setia, 2016) to assess the association of a variety of sociodemographic factors with cancer clinical trial enrollment. Our data source was patient electronic health records from the Carolina Data Warehouse. We used data from the three University of North Carolina (UNC) affiliated cancer hospitals that upload data to the UNC Cancer Registry. These hospitals are NC Cancer Hospital, UNC Cancer Care Rockingham, and the McCreary Cancer Center at Caldwell UNC Health Care.

Participants and sampling methods

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Warehouse electronic medical record data associated with cancer-related treatment or diagnosis codes were insufficient to classify a patient as having cancer. Our rationale for this requirement is the prevalence of errors in medical coding and misleading codes. For example, a physician may record an appointment about a potentially cancerous mole as a cancer Current Procedural Terminology (CPT) code, even if the mole turns out to be benign and the patient does not have cancer. Inclusion of such patients could have led to misclassification of non-cancer patients, who are ineligible for cancer trial enrollment, potentially causing underestimation of true trial

enrollment. We used patient name, medical record number, sex, and date of birth to link patient records between the two systems. From the UNC EHR we obtained data about patient date of birth, sex, race and ethnicity, marital status, primary health insurer and enrollment status. From the cancer registry we recorded patient date of diagnosis, age at diagnosis, stage of cancer, and cancer site.

Independent variables and outcomes

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Statistical analysis

Using log-binomial models, we estimated unadjusted associations between clinical trial enrollment and social drivers such as health insurance status, patient race and ethnicity, and marital status in the form of Risk Ratios with 95 percent confidence intervals. To determine if any of the perceived associations between the observed social drivers and the primary outcome of interest were statistically significant, we also performed a chi-squared test (McHugh, 2013) for each association in our bivariable analysis. To control for potential confounders, we then used a multivariable logistic regression model to produce adjusted Risk Ratios. We controlled for insurance type, age, sex, race, ethnicity, marital status, cancer stage and cancer site. All tests were determined to be significant or not based on the predetermined value of alpha equals 0.05.

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Figure 1: Sample Selection

E

2,675 AYAs diagnosed with invasive cancer between 04/14 and 04/19 in the UNC Electronic Medical Record system

and the UNC Cancer Registry

Excluded 64 patients that appeared in the registry twice

Excluded 466 subjects who received most of their treatment at a non-UNC hospital Excluded 347 patients who did not have an encounter with a UNC physician within

90 days of cancer diagnosis

1,798 samples for further analysis (final sample)

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Results

We identified 2,675 AYAs diagnosed with invasive cancer within the UNC health system between April 2014 and April 2019. We excluded individuals not in the registry to account for miscoding in the UNC EHR system (Figure 1). Sixty-four patients appeared in the registry twice because of two cancer diagnoses. To prevent patient duplication, we only kept medical

encounters related to the cancer with the earlier diagnosis date (n = 2,611). Further, we excluded patients who did not have an encounter with a UNC physician within 90 days of cancer diagnosis (n = 2,264). We removed subjects who received treatment at a non-UNC hospital because patients who received treatment at medical centers other than UNC may have enrolled in clinical trials at those institutions. The final study sample included 1,798 patients.

The mean age of the sample at diagnosis date was reported as 31.30 years with a standard deviation of 6.63 years. The sample was 58% female, and 91% not Hispanic. The race

distribution was 66% white, 21% black, 2% Asian and 11% other. Of the 1,798 patients, 50% were single, 44% were married, and 6% were separated or divorced at the time of diagnosis. The proportions of patients with different insurance types were not consistent across age groups. For example, the 15-19 age group was more likely to have public insurance in comparison to all the other age groups. Thus, the relationship between insurance type and enrollment may be

confounded by other variables. The demographic factors of the final sample are displayed in Table 1.

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clinical trial enrollment and pertinent covariates including sociodemographic factors. Insurance type did not appear to have a significant impact on enrollment rate in any type of trial in the bivariable chi squared analysis. Significant differences for enrollment in non-interventional trials were found within ethnicity, cancer stage, and cancer type, p<0.01, p<0.001, and p<0.001 respectively. In general, the significance levels across trial type in Table 2 are relatively consistent by category.

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Table 1: Description of Full Study Population by Insurance Type Total

n = 1798 n = 1080Private n = 377Public Militaryn = 129 n = 212Self

n % n % n % n % n %

Age at diagnosis

15-19 143 8 74 7 56 15 9 7 4 2

20-24 205 11 113 11 34 9 27 21 31 15

25-29 310 17 184 17 67 18 20 16 39 18

30-34 478 27 295 27 99 26 34 26 50 24

35-39 662 37 414 38 121 32 39 30 88 42

Sex

Male 750 42 434 40 153 41 68 53 95 45

Female 1047 58 645 60 224 60 61 47 117 55

Race

White 1131 66 773 75 182 50 80 66 96 47

Black 361 21 164 16 136 37 20 16 41 20

Asian 29 2 21 2.0 5 1 1 1 2 1

Other 198 11 70 7 42 12 21 17 65 32

Ethnicity

Not Hispanic 1565 91 978 95 334 91 111 90 142 69

Hispanic 157 9 47 5 34 9 12 10 64 31

Marital Status

Single 838 50 448 45 243 67 30 26 117 59

Married 742 44 505 51 88 24 83 71 66 33

Separated/Divorced 95 6 42 4 34 9 4 3 15 8

Cancer Stage

In situ 58 3 47 5 5 1 2 2 4 2

Local 758 44 478 46 128 36 59 48 93 46

Regional 489 28 291 28 100 28 37 30 61 30

Distant 418 24 227 22 123 35 25 20 43 21

Cancer Site

Breast 217 12 134 12 43 11 18 14 22 10

Gynecologic 234 13 134 12 50 13 9 7 41 19

Testicular 109 6 65 6. 13 3 10 8 21 10

CNS 98 5 50 5 29 8 6 5 13 6

GI 154 9 96 9 35 9 5 4 18 9

Head/Neck/Lung 91 5 50 5 20 5 12 9 9 4

Kidney/Bladder 50 3 18 2 16 4 6 5 10 5

Leukemia 167 9 89 8 51 14 16 12 11 5

Lymphoma 158 9 97 9 40 11 7 5 14 7

Sarcoma 92 5 47 4 24 6 10 8 11 5

Skin 263 15 203 19 25 7 16 12 19 9

Thyroid 139 8 80 7 24 6 12 10 22 10

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Table 2: Trial Enrollment Any trial

n = 653 (36.3%)

Interventional trials n = 254 (14.1%)

Non-interventional trials n = 568 (31.6%)

n % p n % p n %

Primary Insurance 0.98 0.37 0.92

Private 390 36 152 14 339 31

Public 140 37 46 12 122 32

VA 46 36 19 15 38 30

Uninsured/Self Pay 77 36 37 18 69 33

Age at diagnosis 0.24 0.10 0.18

15-19 60 42 31 22 47 33

20-24 66 32 26 13 54 26

25-29 114 37 40 13 104 34

30-34 162 34 65 14 139 29

35-39 251 38 92 14 224 34

Sex 0.29 0.41 0.12

Male 262 35 100 13 222 30

Female 391 37 154 15 346 33

Race 0.21 0.47 0.19

White 415 37 160 14 369 33

Black 145 40 57 16 122 34

Asian 12 41 5 17 12 41

Other 62 31 22 11 52 26

Ethnicity <0.01 0.02 <0.01

Not Hispanic 597 38 236 15 522 33

Hispanic 42 27 13 8 35 22

Marital Status 0.90 0.35 0.91

Single 320 38 122 15 273 33

Married 275 37 118 16 242 33

Separated/Divorced 36 38 10 11 33 35

Cancer Stage <0.001 <0.001 <0.001

In situ 5 9 0 0 5 9

Local 238 31 74 10 221 29

Regional 204 42 84 17 177 36

Distant 188 45 89 21 149 36

Cancer Site <0.001 <0.001 <0.001

Breast 107 49 58 27 87 40

Cervical 52 50 19 18 51 49

CNS 31 32 8 8 30 31

Colorectal 45 47 11 12 42 44

Head/neck 29 41 16 23 27 39

Kidney 25 50 9 18 23 46

Leukemia 73 44 38 23 57 34

Lymphoma 55 35 29 18 39 25

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Sarcoma 26 28 10 11 19 21

Skin 41 16 9 3 37 14

Testicular 38 35 8 7 33 30

Thyroid 36 26 10 7 35 25

Other 72 39 25 13 66 36

Table 3: Interventional Trial Enrollment by Payer type and Covariates

n % RR

Unadjusted

95% CI RR

Adjusted

95% CI Primary Insurance

Public 46 12 Reference Reference

Private 152 14 1.15 0.85, 1.57 1.32 0.96, 1.80

VA 19 15 1.21 0.74, 1.98 1.31 0.80, 2.17

Uninsured/Self Pay 37 18 1.43 0.96, 2.13 1.87 1.25, 2.80

Age at Diagnosis

15-19 31 22 Reference Reference

20-39 223 14 0.62 0.44, 0.87 0.51 0.35, 0.73

Race

White 160 14 Reference Reference

Not White 94 14 1.00 0.79, 1.26 0.96 0.75, 1.22

Ethnicity

Not Hispanic 236 15 Reference Reference

Hispanic and Unknown 13 8 0.55 0.32, 0.94 0.54 0.30, 0.95

Marital Status

Single 122 15 Reference Reference

Married 118 16 1.09 0.87, 1.36 1.21 0.93, 1.58

Separated/Divorced 10 11 0.72 0.39, 1.33 0.67 0.35, 1.26

Cancer Stage

In Situ/Local 74 9.1 Reference Reference

Regional 84 17 1.89 1.41, 2.54 1.62 1.20, 2.18

Distant 89 21 2.35 1.77, 3.12 2.11 1.49, 2.98

Cancer Site

Breast 58 27 Reference Reference

CNS 8 8 0.31 0.15, 0.61 0.41 0.18, 0.93

GI 16 10 0.39 0.23, 0.65 0.34 0.20, 0.57

Gynecologic 38 16 0.61 0.42, 0.88 0.73 0.51, 1.06

Head/Neck/Lung 18 20 0.74 0.46, 1.18 0.67 0.43, 1.06

Heme 67 21 0.77 0.57, 1.05 0.51 0.35, 0.74

Sarcoma 10 11 0.41 0.22, 0.76 0.38 0.20, 0.71

Skin 9 3 0.13 0.06, 0.25 0.16 0.08, 0.32

Testicular 8 7 0.27 0.14, 0.55 0.26 0.13, 0.53

Thyroid 10 7 0.27 0.14, 0.51 0.22 0.10, 0.46

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Discussion

Our results expand on findings in the literature. Although insurance status, our primary sociodemographic factor of interest, did not lead to significant differences in clinical trial

enrollment in the bivariable analysis, after controlling for age, the association between uninsured status/self pay and higher trial enrollment emerged. We suspect that this difference was masked in the bivariable analysis because patients between the ages of 15 and 19 were both more likely to enroll in trials as well as have public insurance compared to older age groups. Although it was borderline statistically significant, we think it is important to mention that those with private insurance were more likely to enroll than those with public insurance. A possible explanation for this could be that individuals with public insurance such as Medicaid are of lower socioeconomic status and may not have as many resources as those with private insurance (NC Medicaid

Division of Health Benefits, 2020). Factors influencing chance of enrollment for patients with public insurance could include transportation barriers (Syed, Gerber, & Sharp, 2013). Thus, providers could connect patients with public insurance to hospital social support and ensure reliable and affordable transit to and from the hospital to potentially increase their enrollment.

We were surprised that uninsured individuals were found to be the most likely to enroll in clinical trials out of any group in the multivariable analysis. While this result does not replicate prior literature in which patients with insurance were most likely to enroll (Parsons et al., 2011), we think this information is worth further evaluation. One possible explanation for this

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them to pay for healthcare (Cho, Danis, & Grady, 2018). This brings up ethical questions about informed consent and enrolling uninsured patients on clinical trials. Providers should be vigilant in consenting uninsured patients to make sure they are not unduly influenced by cost of care.

While we did not see a significant difference in AYA enrollment among the five age ranges, when we split the groups up between 15 to 19 and 20 to 39 the difference emerged. The younger AYAs were significantly more likely to enroll in trials than older age groups, which we anticipated as similar results were found in previous studies (Fern & Whelan, 2010; Parsons et al., 2011). Our findings also contribute to the mixed information from the literature about the significance of sex (Collins et al., 2015; Parsons et al., 2011; Zullig et al., 2016), race, and ethnicity (Collins et al., 2015; Fern & Whelan, 2010; Parsons et al., 2011) in trial enrollment among AYAs . We found that sex, race and marital status were not significant contributors to patient enrollment in clinical trials, although marital status was borderline significant, with married individuals more likely to enroll than single individuals. Our data also showed a strongly significant association between ethnicity and clinical trial enrollment, with not Hispanic patients being about twice as likely as Hispanic patients to enroll. Further, despite uninsured individuals having the highest enrollment rates, this association remained present even though a greater proportion of Hispanic patients were uninsured compared to not Hispanic patients.

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mind from the beginning as well as ensuring adequate employment of medically literate

translators. Patients should be assured that their participation in such trials may help them on an individual level as well as help future patients from all ethnic backgrounds.

These findings should be considered within the limitations of this study. By excluding patients not in the Cancer Registry, we are limiting our cohort to specific hospitals because only three hospitals in the UNC system upload their cancer data to the Cancer Registry. This focus could distort our findings if the patients who we excluded were treated at smaller and more rural hospitals, where fewer clinical trials are available. Further, all of our data came from patients who received care at academic medical centers. Thus, our conclusions may not be representative of patient populations who receive care at community or private oncology centers. Additionally, we are using electronic patient data that was collected for reasons other than to answer our research questions.

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