Abstract
Background
Community economics and other social health determinants influence outcomes in oncologic patient populations. We sought to explore their impact on presentation, treatment, and survival in glioma patients.
Methods
A retrospective cohort of patients with glioma (World Health Organization grades III–IV) diagnosed between 1999 and 2017 was assembled with data abstracted from medical record review. Patient factors included race, primary care provider (PCP) identified, marital status, insurance status, and employment status. Median household income based on zip code was used to classify patients as residing in high-income communities (HICs; ie, above the median state income) or low-income communities (LICs; ie, below the median state income). The Kaplan–Meier method was used to assess overall survival (OS); Cox proportional hazards regression was used to explore associations with OS.
Results
Included were 312 patients, 73% from LICs. Survivors residing in LICs and HICs did not differ by age, sex, race, tumor grade, having a PCP, employment status, insurance, time to presentation, or baseline performance status. Median OS was 4.1 months shorter for LIC patients (19.7 vs 15.6 mo; hazard ratio [HR], 0.75; 95% CI: 0.56–0.98, P = 0.04); this difference persisted with 1-year survival of 66% for HICs versus 61% for LICs at 1 year, 34% versus 24% at 3 years, and 29% versus 17% at 5 years. Multivariable analysis controlling for age, grade, and chemotherapy treatment showed a 25% lower risk of death for HIC patients (HR, 0.75; 95% CI: 0.57–0.99, P < 0.05).
Conclusions
The economic status of a glioma patient’s community may influence survival. Future efforts should investigate potential mechanisms such as health care access, stress, treatment adherence, and social support.
Keywords: glioblastoma, socioeconomics, survival
Gliomas are responsible for 24% of brain tumors in adults and are the second most common brain malignancy in adults.1 Similar to other malignancies, research has extensively explored the biologic factors that influence outcomes for glioma cohorts such as age, extent of resection, tumor genetics, and treatment received.2–11 Generally speaking, this research has shown the benefits of surgery, radiation, and temozolomide (TMZ), with worse outcomes associated with increasing age, smaller extent of resection, and lower pretreatment performance status. Increasingly, social factors such as insurance, education status, poverty, and employment are recognized as contributors to clinical outcomes and health disparities in oncology, including patients with CNS malignancies.12
Much of the previous research on these social factors has been limited to data specific to the patient, such as the patient’s insurance status, employment status, level of education, marital status, and ethnicity. Patient-specific social factors found to negatively influence oncologic outcomes include decreased income, lower educational attainment, living in a non-urban community, being uninsured, and being unemployed.13–17 However, prior studies have also shown that social factors more broadly at the level of the community influence oncologic outcomes and include factors such as accessibility of health care centers to all local patients, ease of transportation, job availability, local support systems, and the economic conditions of the community.18–24 Both patient and community factors may influence clinical outcomes and health disparities, albeit in different ways.25,26 For example, patient-specific factors may drive outcomes through an individual’s ability to pay for health care services or the presence of comorbid disease. Community-specific factors can shape clinical outcomes by influencing a patient’s level of social engagement, exposure to environmental risks, and the ability to adhere to medical recommendations.19,20,27
In the malignant glioma population, studies have shown that patient-specific factors influence treatment and outcomes; higher estimated income, insurance coverage, having a primary care provider (PCP), receiving treatment at an academic institution, and higher level of education are associated with higher rates of treatment and increased overall survival (OS).28–32 In contrast, few studies have evaluated the influence of community-specific factors on glioma management from presentation through initial treatment. In this study, we addressed this gap in knowledge by assessing how the economic conditions of the community, as defined by US Census data, can influence the course and management of a malignant glioma population from the southeastern United States. Specifically, we focused on the relationship between community economic status (CES) and access to care, treatment received, survival, and other social determinants of health.
Methods
Patient Selection
The study was reviewed and approved by the institutional review board (#00038719) and a retrospective cohort study was conducted. Patients age ≥18 years at the time of diagnosis of a histologically confirmed high-grade glioma, World Health Organization (WHO) grade III or IV, who received primary treatment between 1999 and 2017 with available clinical and treatment data were included. Patients who did not receive primary treatment at our institution or were lost to follow-up prior to the initiation of treatment were excluded.
Data Collection
Clinical and demographic data were obtained from the Wake Forest Baptist Comprehensive Cancer Center Registry and subsequently confirmed by medical record review. Demographic information included age, sex, race, and ethnicity. Biologic data included WHO grade as determined by histologic evaluation at the time of initial diagnosis, multifocality on initial imaging, and Karnosky performance scale (KPS) at the start of radiation. Treatment characteristics gathered for each patient included extent of surgery (eg, biopsy, resection), as defined by intraoperative documentation and confirmed when available by postoperative imaging, whether radiation was prescribed (yes vs no), radiation dose (Gy), concurrent chemotherapy (yes vs no), and adjuvant chemotherapy (yes vs no, and type).
The below social factors were collected from the medical record based on their alignment with the broad domains of social determinants outlined by the Centers for Disease Control and Prevention (CDC).33 Patient-specific social factors relating to health care included having a PCP (yes vs no) and insurance status (none, government, private, unknown). Marital status (married, widowed, divorced, separated, single, other) reflected the patient’s social and community context. The patient’s environment was reflected through the distance from the treating Comprehensive Cancer Center (CCC) and timing to presentation and treatment. Distance, in miles, from treating CCC was estimated by calculating the distance from the patient to Wake Forest Baptist Comprehensive Cancer Center using Google Maps. Data pertaining to time to presentation, surgery, and radiation were as reported in the electronic medical record. Time to presentation was categorized as the number of days from symptom onset to date of initial neurosurgical consultation (1 day‒2 weeks, 2 weeks‒1 month, and >1 month). Time to surgery was defined as the number of days from initial neurosurgical consultation to date of surgery. Time to radiation was defined as the number of days from initial neurosurgical consultation to day 1 of radiation. To estimate the economic status of each patient’s local community, each patient’s home zip code was mapped to median household income data from the most recent US Census (c. 2010), similar to other studies.34,35 Zip codes whose median household income was greater than the median state household income were defined as high income communities (HICs). Zip codes whose median household income was lower than the median state household income were defined as low income communities (LICs). Additionally, patient’s employment status (employed vs not) was collected. Due to the retrospective nature of this study, patient household income data could not be obtained.
Statistical Analysis
All data analysis was performed using Stata IC 15 (2017). Demographic, clinical, treatment, and characteristics were summarized using descriptive statistics. The primary analysis focused on CES as defined as LIC versus HIC. Differences in demographics, clinical features, treatment parameters, and other social determinants were assessed by a 2-sample t-test for normally distributed continuous measures and a chi-squared test for discrete measures. Overall survival was calculated from the time of initial histological diagnosis to death from any cause. Survival time was censored if the subject was alive at the time of last follow-up in clinic. Survival probability was estimated using the Kaplan–Meier method.36 Univariate analysis was used to assess for associations between known prognostic factors and OS (eg, age, glioma grade, extent of surgery, chemotherapy administration). Characteristics found to be associated in the univariate analysis (P < 0.10) were incorporated as covariates to construct multivariable proportional hazards regression model 1. Extent of resection was suspected to potentially mediate the relationship between CES and survival. We hypothesized that patients from LICs might present at a different timeframe compared with HICs, which may contribute to differences in extent of resection and outcome differences. To explore this potential contribution, extent of resection was included in multivariable proportional hazards regression model 2. This model was used to estimate the hazard ratio (HR) for death attributable to prognostic factors.37 Given the exploratory nature of these analyses, no adjustment for multiple testing was performed and all observed outcomes should be considered descriptive. All P-values are reported as two-sided.
Results
Patient Demographic and Cancer Characteristics
A total of 312 patients met inclusion criteria for the current study; 223 had grade IV glioma (71%) and 89 had grade III glioma (29%) (Table 1). Of these, 225 participants (72%) lived in LIC zip codes, while 87 (28%) lived in HIC zip codes. The median household income for patient zip codes in this cohort was $41 800 (interquartile range: $36 500–$48 000). Mean age was 56.5 ± 15.3 years; 61% of patients were male and 94% were white. Most patients had a PCP (82%), were insured (86%), and were married or partnered (72%); 35% were employed and 30% were retired. There were no significant differences between LIC and HIC groups for any of the characteristics except for marital status; LIC patients were more likely to be single (P = 0.01).
Table 1.
Demographic and cancer characteristics of patients with primary glioma residing in LICs and HICs
| Characteristic, n | Total (n = 312) | LIC (n = 225) | HIC (n = 87) |
|---|---|---|---|
| Age, y, mean ± SD | 56.5 ± 15.3 | 56.9 ± 15.1 | 55.6 ± 15.9 |
| Sex (% male) | 191 (61%) | 142 (63%) | 49 (56%) |
| Race | |||
| White | 292 (94%) | 209 (93%) | 83 (95%) |
| Nonwhite | 20 (6%) | 16 (7%) | 4 (5%) |
| WHO Grade | |||
| Grade III | 89 (29%) | 61 (27%) | 28 (32%) |
| Grade IV | 223 (71%) | 164 (73%) | 59 (68%) |
| Tumor Pathologic Type | |||
| Glioblastoma | 223 (72%) | 164 (73%) | 59 (69%) |
| Anaplastic astrocytoma | 54 (17%) | 38 (8%) | 16 (19%) |
| Anaplastic oligoastrocytoma | 27 (9%) | 17 (17%) | 10 (11%) |
| Anaplastic oligodendroglioma | 7 (2%) | 6 (2%) | 1 (1%) |
| PCP Status at Diagnosis | |||
| No | 33 (10%) | 24 (11%) | 9 (10%) |
| Yes | 255 (82%) | 184 (82%) | 71 (82%) |
| Unknown | 24 (8%) | 17 (7%) | 7 (8%) |
| Insurance status | |||
| None | 19 (6%) | 12 (5%) | 7 (8%) |
| Government | 110 (35%) | 85 (38%) | 25 (29%) |
| Private | 158 (51%) | 111 (49%) | 47 (54%) |
| Unknown | 25 (8%) | 17 (8%) | 8 (9%) |
| Marital status | |||
| Single | 36 (12%) | 32 (14%) | 4 (5%) |
| Married/partner | 223 (72) | 163 (72%) | 60 (70%) |
| Separated/divorced | 38 (12%) | 22 (10%) | 16 (18%) |
| Widowed | 14 (4%) | 8 (4%) | 6 (7%) |
| Employment status | |||
| Unemployed | 44 (14%) | 32 (14%) | 12 (14%) |
| Employed | 109 (35%) | 78 (35%) | 31 (36%) |
| Retired | 95 (30%) | 67 (30%) | 28 (32%) |
| Disabled | 31 (10%) | 22 (10%) | 9 (10%) |
| Student | 6 (2%) | 6 (2%) | 0 (0%) |
| Unknown | 27 (9%) | 20 (9%) | 7 (8%) |
| State of Residence | |||
| NC | 292 (94%) | 213 (95%) | 79 (91%) |
| SC, WV, TN | 14 (4%) | 9 (4%) | 5 (6%) |
| GA, TX, FL, IN | 6 (2%) | 3 (1%) | 3 (3%) |
Characteristics were not significantly different across community groups with P-values > 0.05 except for marital status (P = 0.01). State of residence consists of 3 categories based on state median household incomes. The state median household income of NC served as reference; SC, WV, TN were grouped together as their state median household income was less than that of NC while GA, TX, FL and IN were grouped together as their state median household income was greater than that of NC. P-values were calculated between the HIC and LIC utilizing chi-squared test for all variables aside from age which was calculated utilizing a 2-sample t-test.
Effect of Community Economic Status on Presenting Features
LIC patients were most likely to present within 1 day‒2 weeks (43%), while HIC patients were most likely to present ≥1 month after symptom onset (44%), though this difference was not statistically different (P = 0.10) (Table 2). The proportion of patients with KPS ≥70 (P = 0.14) and multifocal tumor (P = 0.21) was similar by group. Mean distance from the treating CCC was 65.1 ± 92.0 miles and was similar for LIC (65.5 ± 69.9) and HIC groups (64.1 ± 133.8, P = 0.90). Mean time to neurosurgical intervention was 9 ± 36 days and mean time to radiation was 41 ± 42 days. No significant differences were observed for time from initial evaluation to surgery (P = 0.39) or time from surgery to start of radiation between income groups (P = 0.60). There was no difference between patients who underwent biopsy compared with resection for the time to initial evaluation (P = 0.74), from initial evaluation to surgery (P = 0.81), or from surgery to start of radiation (P = 0.15).
Table 2.
Effect of community economic status on glioma presenting features and treatment
| Characteristic | Total (n = 312) | LIC (n = 224) | HIC (n = 87) | P-value |
|---|---|---|---|---|
| Time to presentation (n = 290) | 0.10 | |||
| 1 day to 2 weeks | 118 (41%) | 91 (43%) | 27 (34%) | |
| 2 weeks to 1 month | 72 (25%) | 55 (26%) | 17 (22%) | |
| >1 month | 100 (34%) | 65 (31%) | 35 (44%) | |
| Time to surgery (days, mean ± SD, n = 278) | 9 ± 36 | 9 ± 41 | 8 ± 13 | 0.39 |
| Time to radiation (days, mean ± SD, n = 246) | 41 ± 42 | 40 ± 46 | 42 ± 29 | 0.60 |
| Distance from treating CCC (mean miles, mean ± SD, n = 312) | 65.1 ± 92.0 | 65.5 ± 69.9 | 64.1 ± 133.8 | 0.90 |
| Baseline KPS (n = 134) | 0.14 | |||
| KPS ≥ 70 | 98 (73%) | 66 (69%) | 32 (82%) | |
| KPS < 70 | 36 (27%) | 29 (31%) | 7 (18%) | |
| Multifocal tumor (n = 221) | 0.21 | |||
| Present | 51 (23%) | 34 (21%) | 17 (29%) | |
| Absent | 170 (77%) | 129 (79%) | 41 (71%) | |
| Biopsy only/resection (n = 312) | 28% /72% | 32% /68% | 20% /80% | 0.04 |
| Chemotherapy (% received, n = 312) | ||||
| Overall | 241 (77%) | 176 (78%) | 65 (75%) | 0.51 |
| Concurrent TMZ | 216 (69%) | 158 (70%) | 58 (67%) | 0.54 |
| Adjuvant TMZ | 189 (61%) | 132 (59%) | 57 (66%) | 0.27 |
| Radiation (% received, n = 312) | 304 (98%) | 219 (98%) | 85 (98%) | 0.97 |
Time to presentation was categorized as the number of days from symptom onset to date of initial neurosurgical consultation. Time to surgery was defined as the number of days from initial neurosurgical consultation to date of surgery. Time to radiation was defined as the number of days from initial neurosurgical consultation to day 1 of radiation. Distance from the Comprehensive Cancer Center (miles) was estimated by calculating the distance from the patient to Wake Forest Baptist Comprehensive Cancer Center. P-values were calculated utilizing a two-sample t-test for the following variables: time to surgery, time to radiation, and distance from treating CCC. P-values were calculated utilizing a chi-squared test for the following variables: time to presentation, baseline KPS, tumor focality, biopsy only/resection, chemotherapy treatment, and radiation treatment.
Effects of Community Economic Status on Treatment Received
Patients were more likely to have undergone resection (72%) than biopsy (28%) (Table 2). Biopsy was more likely to have been performed for LIC (32%) than HIC patients (20%, P = 0.04). Biopsy was significantly more common for patients with multifocal disease (biopsy/resection = 47%/53%, n = 51) compared with those without multifocal disease (20%/80%, n = 170, P < 0.001). Any use of chemotherapy (P = 0.51), concurrent TMZ (P = 0.54), and adjuvant TMZ (P = 0.27) were similar across the 2 groups. Radiation therapy was prescribed to 98% of the cohort and equally for LIC and HIC groups (P = 0.97).
Overall Survival
Median OS was 15.8 months (95% CI: 7.8–42.5 mo). Median OS was 4.1 months longer for patients from HIC compared with LIC communities (19.7 vs 15.6 mo, HR: 0.75; 95% CI: 0.56–0.98, P = 0.04, Figure 1). This difference widened over time with survival estimates for HIC compared with LIC patients of 66% vs 61% at 1 year, 45% vs 32% at 2 years, 34% vs 24% at 3 years, and 29% vs 17% at 5 years. Similar trends were observed for both WHO grade III and grade IV gliomas (Figure 1).
Figure 1.
Kaplan‒Meier analysis of (A) entire cohort, (B) grade III gliomas, and (C) grade IV gliomas presented with corresponding HR and 95% CI.
Results of both univariate and multivariable Cox regression models of overall survival are summarized in Table 3. Univariate analysis showed a significantly greater risk of death associated with higher WHO grade, older age, lack of chemotherapy, and biopsy versus resection. No difference in risk of death was seen with the univariate analysis for radiation treatment (HR, 0.61; 95% CI: 0.27–1.38, P = 0.24), having a PCP (HR, 0.96; 95% CI: 0.70–1.32, P = 0.81), employment status (HR, 0.97; 95% CI: 0.90–1.05, P = 0.46), sex (HR, 0.86; 95% CI: 0.67–1.11, P = 0.24), race (HR, 1.33; 95% CI: 0.93–1.90, P = 0.12), or distance from the treating CCC (HR, 1.00; 95% CI: 0.99–1.00, P = 0.77). Multivariable model 1 included potential confounding demographic and clinical variables as determined by a significant univariate relationship, defined as P < 0.10. When controlling for age, grade, and chemotherapy, HIC patients had significantly improved survival (HR, 0.75; 95% CI: 0.57–0.99 P = 0.05). Extent of resection was the only variable significantly associated with both CES and OS and was hypothesized to mediate the effect on survival. When incorporated into Model 2, the magnitude of the hazard remained similar (HR, 0.80) and precision of the estimate widened (95% CI: 0.61–1.07). Across all multivariable models, higher grade, older age, chemotherapy, and extent of resection contributed significantly to worsened OS (P < 0.01).
Table 3.
Univariate and multivariate Cox regression models for the effect of CES on OS in glioma patients
| Characteristic | Univariate HR, 95% CI N = 312 | P-value | Multivariable 1 HR, 95% CI N = 312 | P-value | Multivariable 2 HR, 95% CI N = 312 | P-value |
|---|---|---|---|---|---|---|
| HIC vs LIC | 0.75 (0.56–0.98) | 0.038 | 0.75 (0.57–0.99) | 0.046 | 0.80 (0.61–1.07) | 0.129 |
| WHO grade IV vs III | 3.74 (2.74–5.10) | <0.001 | 2.64 (1.90–3.69) | <0.001 | 3.24 (2.31–3.55) | <0.001 |
| Age, y | 1.04 (1.03–1.05) | <0.001 | 1.04 (1.03–1.05) | <0.001 | 1.04 (1.02–1.05) | <0.001 |
| Chemotherapy (received vs none) | 0.73 (0.55–0.97) | 0.031 | 0.58 (0.44–0.78) | <0.001 | 0.55 (0.42–0.74) | <0.001 |
| Extent of resection (biopsy vs resection) | 1.82 (1.40–2.37) | <0.001 | 2.45 (1.86–3.23) | <0.001 |
Discussion
In this study, we assessed the role of community economic status on presentation, treatment receipt, and overall survival in malignant glioma patients, while exploring associations with patient-specific socioeconomic factors. This study suggests that the economic status of the community in which a patient resides contributes to subsequent outcomes. Specifically, survival for patients residing in lower income communities was worse and appeared to widen over time after diagnosis.
Compared with other malignancies, there have been few studies that assess the influence of social determinants of health (SDHs) on patients with primary gliomas.28–31 The CDC divides SDH broadly into 5 major categories,33 including: (1) economic stability, (2) education, (3) social and community context, (4) health and health care, and (5) neighborhood. Prior studies of SDHs in glioma have primarily focused on patient-specific factors, including patient insurance status, marital status, PCP status, patient income, education level, and treatment location.28–31,38
In this study, we were able to obtain data on 2 broad categories of SDHs, economic stability and the health care system. We focused on characterizing both patient-specific and community-level features of these social determinants. The patient-specific SDHs included PCP status (eg, health care system), insurance status (eg, health care system), employment status (eg, economic stability), and marital status (eg, community and social context). Community-level SDHs included distance of each zip code from the treating CCC (eg, health care system) and the community economic status (eg, economic stability). Similar to other studies, community was defined by zip codes as these data were readily available.34,35 We recognize that zip codes are administrative household groupings of varying geographic areas and may have considerable household heterogeneity. Several definitions of “community” exist and numerous factors contribute to and define local communities.
Poorer economic stability within a community could contribute to worse outcomes in a patient for several reasons. In this study, we did not see that patients from lower-income communities were more likely to present late, have a delay to radiation, or poorer performance status at baseline. Tumors were not more likely to be of higher grade or multifocal. Radiation and chemotherapy were prescribed similarly. We did find that patients from LICs were more likely to have undergone biopsy as opposed to resection, potentially as a result of surgical accessibility, burden of comorbid medical conditions, tumor location in eloquent areas, access to care, or other factors.39–41 Additionally, the results of multivariable model 2 show that greater extent of resection may partially explain the effect of CES on survival as CES only trended toward significance while the known prognostic factors maintained the strength of their relationship throughout both the univariate and multivariate analyses. We hypothesized that differences may exist between communities in access to care (eg, access to primary care, availability of tertiary care center referrals, recognition of early symptoms) for patients residing in LIC versus HIC communities and that this may influence surgical options at presentation and further influence the survival outcomes. The association between CES and extent of resection is likely complex, and additional prospectively gathered data on access, severity of presenting symptoms, time to presentation, and specific patient and tumor features need to be explored.42–44
The association between CES and survival appeared to widen over the first 2 years after diagnosis and then remained stable throughout the entirety of over 10 years of follow-up in this cohort. This pattern of divergence points to a stronger influence of CES outside of the initial treatment and management stage. Patients whose tumors grow rapidly despite treatment are likely to experience poor outcomes irrespective of their community context. Patients who complete their initial therapy, are required to undergo regular routine surveillance, and seek to reintegrate into their local network may experience a greater impact from their community. This has been suggested previously in studies analyzing the influence of community engagement and social support in influencing outcomes and re-admission rates in both acute and chronic diseases.45–49
This retrospective study of a currently available convenience sample does have important limitations. We were not able to gather data on patient-specific household income, which would have allowed us to distinguish the impact of individual- and community- level socioeconomic status. In addition, due to the retrospective nature of this study over a long period of time, data on itemized KPS scores at treatment start, gross tumor volume by initial neuroimaging at diagnosis, tumor genomics, and molecular classification (eg, isocitrate dehydrogenase [IDH], O6-methylguanine-DNA methyltransferase [MGMT]) were not reliably available for the entire cohort. In the 85 patients with available MGMT methylation and IDH mutation data, there was no significant difference in results of these molecular studies for LIC versus HIC patient groups (P = 0.35 and P = 0.41). This study was limited by a relatively small sample size with a total of 312 patients included in the overall analysis and only 87 patients meeting criteria for the HIC group. Post hoc power analysis indicated that we had 80% power to detect an HR of 0.70 for the HIC group, assuming a two-sided test at an alpha of 0.10. Data on salvage therapy was not collected. Other patient level variables that were not available in the dataset included patient comorbidities, treatment toxicity/adherence, and the ability to adhere to routine follow-up. It should also be noted that anaplastic astrocytomas, anaplastic oligodendrogliomas, and oligoastrocytomas were included in this analysis and could have impacted the survival data given the better prognosis these gliomas confer compared with glioblastomas.
Overall, our study is one of the first within the primary glioma population to show the influence of community-specific factors on clinical outcomes and treatment. Patients from LICs were more likely to have undergone only biopsy compared with surgical resection and had worse OS compared with those from HICs. The association between CES and survival appeared to widen, emphasizing the potential importance of community support and resources outside the initial presentation and management phase of neuro-oncologic care. In the future, studies should work to characterize specific community-level factors impacting outcomes after initial treatment, such as local support structures and ability to adhere to follow-up care recommendations. Identifying and augmenting these potential community facilitators could improve clinical outcomes in glioma patients, especially among those residing in economically disadvantaged communities.
Funding
Research reported in this publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant no. KL2TR001421 and the Wake Forest Baptist Comprehensive Cancer Center (P30CA012197, PI: Pasche).
Conflict of interest statement. Dr Roy E. Strowd MD has received grant support from the American Society of Clinical Oncology Conquer Cancer Foundation, Southeastern Brain Tumor Foundation, National Institutes of Health, and JAZZ Pharmaceuticals unrelated to this study. Dr Strowd has served as a consultant to Innocrin Therapeutics, Monteris Medical, and Novocure. Dr Strowd receives support to serve on the editorial board of Neurology. No other author reports a conflict of interest.
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