Abstract

Background: One avenue to improve outcomes among brain tumor patients involves the mitigation of healthcare disparities. Investigating clinical differences among brain tumors across socioeconomic and demographic strata, such can aid in healthcare disparity identification and, by extension, outcome improvement.

Methods: Utilizing a racially diverse population from Hawaii, 323 cases of brain tumors (meningiomas, gliomas, schwannomas, pituitary adenomas, and metastases) were matched by age, sex, and race to 651 controls to investigate the associations between tumor type and various demographic, socioeconomic, and medical comorbidities. Tumor size at the time of diagnosis was also compared across demographic groups.

Results: At the time of diagnosis for benign meningiomas, Native Hawaiians and Pacific Islanders (NHPI; P P = 0.04) and Asians (P = 0.02), while for vestibular schwannomas, NHPI had larger tumor sizes compared to Asians (P P P P P

Conclusion: Brain tumors exhibit unique sociodemographic disparities and clinical comorbidities, which may have implications for diagnosis, treatment, and healthcare policy.

Keywords: Central nervous system, Disparities, Risk factors, Socioeconomic, Tumors

INTRODUCTION

Brain tumors impose a significant morbidity and mortality burden globally.[ 2 , 32 ] In the United States, the incidence of brain tumors is 23.79/100,000, with a median survival of 8 months to 20 years, depending on pathology.[ 38 , 67 ] Despite advances in treatment and therapies, survival rates and prognosis have not made commensurate improvements.[ 2 ] Besides conducting molecular and clinical studies, another avenue to improve outcomes involves the mitigation of healthcare disparities. Investigating epidemiologic differences between brain tumors provides a conduit to identify healthcare disparities and improve outcomes for disadvantaged populations. Establishing a comprehensive risk stratification profile among subsets of the population will not only help characterize modifiable risk factors of a disease but also identify population subsets who face barriers to treatment. To elucidate socioeconomic, demographic, and psychobiologic risk factors associated with brain tumors (i.e., gliomas, meningiomas, vestibular schwannomas, pituitary adenomas, and intracranial metastases), we conducted a retrospective case–control study within the diverse state of Hawai’i, where minorities are the plurality.

MATERIALS AND METHODS

Design and setting

The electronic medical records of a neuroscience clinic in Honolulu, Hawaii (i.e., Hawaii Pacific Neuroscience) were retrospectively searched from January 1, 2009, to January 1, 2021. The following International Classification of Diseases 9^th or 10^th editions and Clinical Modification codes (ICD-9-CM or ICD-10-CM) for patients with benign intracranial tumors were used for 2015-2021: ICD-9-CM (225.0, 225.1, 225.2, 225.3, 225.4, 225.8, and 225.9) for 2009–2014, and ICD-10-CM (D32.0, D32.1, D32.9, D33.0, D33.1, D33.2, D33.3, D33.4, D33.7, D33.9, V12.41, and Z86.011). For malignant and miscellaneous intracranial tumors, the respective codes were applied: ICD-9-CM (191.0, 191.1, 191.2, 191.3, 191.4, 191.5, 191.6, 191.7, 191.8, 191.9, 192.0, 192.1, 192.2, 192.8, and 192.9) for 2009-2014 and ICD-10-CM (D42.0, D42.1, D42.9, V10.85, and Z85.841) for 2015– 2021. The Institutional Review Board approval was obtained before the study from the University of Hawai‘i Office of Research Compliance (protocol number: 2020-01010).

Predictor and outcome variables

For cases, the data for the following variables were collected: age at diagnosis, sex, presenting symptom, history of head trauma, history of stroke, presence of gait disturbances, seizures, cognitive difficulties, dizziness, nausea or vomiting (DNV), sleep disturbances, and self-identified race (White, Black, Hispanic/Latino, Asian, Native Hawaiian or Pacific Islander [NHPI], and Native American or Alaskan Natives [NAAN]). Tumor type and dimensions were attained from pathology and imaging reports. Tumor volume and area were calculated using the established formula for a spheroid:

[ 42 , 62 , 80 ]

V = volume, r = radius (half the diameter) along the longest dimension of the tumor along the axial (r₁), coronal (r₂), and sagittal (r₃) planes.

A = area, r = radius (half the diameter) along the longest dimension of the tumor along either the axial, coronal, or sagittal plane.

The insurance and zone improvement plan code of the patient’s residence was collected as a proxy measure for median household income, in addition to the percentage or residence in a municipality below the poverty level (for all ages, 18–64 years, and 65 years and over). Such data were acquired from the United States Census Bureau, 2015–2019 American Community Survey 5-Year Estimates (http://www. census.gov). Insurance was classified as Medicare, Medicaid, private insurance, or military insurance, consistent with the criteria of the Agency of Health-care Research and Quality (Rockville, MD) for the Health-care Cost and Utilization Project (http://www.hcup-us.ahrq.gov).

The presence of the following cardiovascular risk factors was collected: type II diabetes mellitus, hypertension, atrial fibrillation/flutter, congestive heart failure (CHF), coronary artery disease or previous myocardial infarction, prosthetic valve replacement, and peripheral vascular disease. Associations between intracranial tumors and the following were also explored: autoimmune pathology, thyroid disorders, glaucoma, body mass index (BMI), obstructive sleep apnea, asthma or chronic obstructive pulmonary disease (COPD), and gastrointestinal diseases.

Social history elements collected included marital status and family histories of intracranial tumors, neurological disorders, stroke, and cancer. Self-reported smoking status (current, former, and never) was also collected. The smoking classification was based on the United States Centers for Disease Control and Prevention (CDC), National Health Interview Survey, and Adult Tobacco Use (https://www.cdc.gov/nchs/surveys.htm).

The collection of psychiatric risk factors included a history of depression and the extent of alcohol use. Depression was measured by the Patient Health Questionnaire-2 (PHQ-2) and Patient Health Questionnaire-9 (PHQ-9). The PHQ-2 and PHQ-9 are validated two-question and nine-question modules that detect and assess depression.[ 47 , 50 , 51 ] For PHQ-2, a score of 3 or greater was classified as positive, while a score of 2 or lower was classified as negative. For PHQ-9, raw scores were utilized. Alcohol consumption habit was collected using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C), a modified version of the ten question AUDIT developed by the World Health Organization (WHO).[ 10 , 19 , 49 , 72 ]. AUDIT-C cutoff scores of ≥3 for women and ≥ 4 for men were classified as positive.[ 72 ] Collected data also included prior diagnoses of psychiatric disorders other than depression, consistent with criteria from the Diagnostic and Statistical Manual of Mental Disorders 4^th or 5^th Edition.

Controls

Two to four controls were collected per case (n = 323) to maximize statistical power.[ 39 ] Using a random number generator, two sets of controls were collected from the clinic’s total patient pool from January 1, 2019, to January 1, 2021. The first set of controls consisted of 1292 patients to examine differences between the clinic’s general patient population and cases with respect to age, sex, and race. The second set of controls (n = 651) was matched to age, sex, and race to examine all other variables.

Statistical analysis

The normality of data was assessed through quantile-quantile plots and histograms to determine parametric or non-parametric analysis. For categorical variables, either Pearson’s Chi-squared test or Fisher’s exact test of independence was chosen, while for non-parametric continuous variables, the independent Wilcoxon rank-sum test was used.[ 56 , 93 ] Univariate and multivariate logistic regression with Firth’s correction was used to identify variables independently predictive of central nervous system tumors. To determine significance, all tests used an alpha level of 0.05 and were two-tailed. All calculations were performed using R Statistical Software (R Foundation for Statistical Computing, Vienna, Austria).[ 81 ]

RESULTS

All tumors

Cases of all intracranial tumors (n = 323) were compared to unmatched controls (n = 1292) [ Tables 1a - c ]. Females had significantly higher odds of tumor diagnosis than males (1.44, 95% confidence interval [CI]: 1.08, 1.92; P = 0.013). Meanwhile, Hispanics/Latinos had significantly decreased odds of having a brain tumor (0.46, 95% CI: 0.20, 0.97; P = 0.046), while NAAN had significantly increased odds (8.22, 95% CI: 0.91, 389.61; P = 0.032).

Table 1a:

Sociodemographic characteristics and medical comorbidities of all patients with intracranial tumors and patients with meningiomas alone: Crude odds ratios.

Table 1b:

Sociodemographic characteristics and medical comorbidities of patients with gliomas: Crude odds ratios.

Table 1c:

Sociodemographic characteristics and medical comorbidities of patients with schwannomas, acoustic neuromas, pituitary tumors, and metastatic brain tumors: Crude odds ratios.

Meningiomas

Of the analyzed meningiomas (n = 159), 81.1% were benign (n = 129) and 18.9% were malignant (n = 30) [ Table 1a ]. The median age of diagnosis was 61 years overall: 61 years for benign meningiomas and 66.5 years for malignant (P > 0.05). While there was a female predisposition for benign meningiomas (1.81, 95% CI: 1.17, 2.86; P = 0.01), this was not the case for malignant meningiomas (P > 0.05). Regarding race/ethnicity, NAAN exhibited a 12.26 (95% CI: 1.26, 118.87, P = 0.03) fold increased odds of benign meningioma diagnosis relative to other groups.

Socioeconomic variables

For benign meningiomas, patients had a 2.55 fold increased odds of having Medicare (95% CI: 1.68, 3.87; P < 0.001) and 0.47 fold decreased odds of having private insurance (95% CI: 0.30, 0.73; P < 0.001). Among malignant meningiomas, patients were at reduced odds of being from the first quartile (0.15, 95% CI: 0.0034, 0.99; P = 0.048). Meanwhile, geographically, malignant meningioma patients were at 6.36 fold greater odds of being from an urban location (95% CI: 1.81, 34.55; P = 0.001) and reduced odds of living in a suburban region (0.17, 95% CI: 0.031, 0.59; P = 0.002).

Presenting symptoms

For both benign (2.54, 95% CI: 1.47, 4.71; P < 0.001) and malignant meningiomas (6.80, 95% CI: 2.42, 19.59; P < 0.001), seizures were the most likely presentation; however, malignant meningioma patients were also more likely to present with cognitive difficulties (3.38, 95% CI: 1.23, 9.57; P = 0.014).

Medical comorbidities

In general, meningioma patients were found to have 1.86 times greater odds of a positive alcohol use screen (95% CI: 1.02, 3.29; P = 0.04). Benign meningiomas were specifically found to have increased odds of hypertension (1.54, 95% CI: 1.03, 2.31; P = 0.04), personal history of prior neoplasm (95% CI: 1.08, 2.97; P = 0.02), and family history of brain tumors (4.25, 95% CI: 1.63, 11.10; P < 0.001). Moreover, malignant meningioma patients not only had an increased odds of a history of prior neoplasm (5.15, 95% CI: 1.91, 14.08; P < 0.001) but also an 8.33 fold greater odds of head trauma history (95% CI: 1.32, 346.60; P = 0.03).

Multivariable analysis

Multivariable regression modeling was conducted to determine the best predictors of meningioma diagnosis. For benign meningiomas, variables that significantly increased the odds of diagnosis included: presentation with DNV (2.52, 95% CI: 1.25, 5.08; P = 0.01) or seizures (4.36, 95% CI: 1.78, 10.65; P = 0.001), presence of obesity class I (2.87, 95% CI: 1.08, 7.67; P = 0.04), CHF (6.64, 95% CI: 1.39, 31.73; P = 0.02), glaucomatous disease (9.80, 95% CI: 1.88, 51.06; P = 0.007), positive alcohol use screen (5.65, 95% CI: 2.38, 13.39; p < 0.001), history of stroke (3.05, 95% CI: 1.31, 7.08; P = 0.009) or neoplasm (2.26, 95% CI: 1.06, 4.81; P = 0.035), and family history of brain tumors (9.27, 95% CI: 1.84, 46.61; P = 0.007). In a best-fit model for malignant meningiomas, a presentation with seizures (8.25, 95% CI: 2.49, 27.33; P < 0.001) and a history of neoplasm (3.94, 95% CI: 1.12, 13.86; P = 0.03) were the strongest predictors of diagnosis.

Tumor size

When tumor size was examined using three-dimensional and two-dimensional volumes, NHPI (3171.18 mm³, 95% CI: 3.67, 15286.99; P = 0.049) and Asian (219.00 mm³, 95% CI: 12.00, 668.00; P = 0.033) patients were found to have larger benign meningioma volumes at the time of diagnosis compared to Whites [ Table 2 ].

Table 2:

Tumor size at time of diagnosis in relation to patient race.

Gliomas

Of the 39 gliomas comprising the cohort, 51.3% were WHO Grades I–III gliomas (non-glioblastoma multiforme [GBM], n = 20) [ Table 1b ], and 48.7% were WHO Grade IV gliomas (GBM, n = 19). The median age of diagnosis for non-GBM patients was 47.5 years (interquartile range [IQR]: 31.5, 62.0), 12 years younger (95% CI: 2.00, 22.00; P = 0.02) than that of controls, while the median age for GBM patients was 59.0 years (IQR: 50.0, 70.5), similar to that of controls. No differences in sex were observed. Overall, Asian patients had significantly reduced odds of being diagnosed with glioma (odds ratio [OR]: 0.35, 95% CI: 0.13, 0.96; P = 0.035), while for GBM specifically, Whites had a 6.59 fold increased odds of being diagnosed (95% CI: 1.83, 30.31; P = 0.001).

Seizures (non-GBM, OR: 16.8, 95% CI: 4.88–57.8, P < 0.001; GBM, OR: 6.86, 95% CI: 2.14–22.0, P = 0.001) and cognitive difficulties (non-GBM, OR: 7.94, 95% CI: 2.54–24.9, P < 0.001; GBM, OR: 4.14, 95% CI: 1.25–13.7, P = 0.020) were the most common presenting symptoms for all gliomas. Meanwhile, glioma patients had significantly reduced odds of presenting with DNV (non-GBM, 0.08, 95% CI: 0.01–0.64, P = 0.02; GBM, 0.12, 95% CI: 0.02–0.99; P = 0.049) or sleep disturbances (0.38, 95% CI: 0.15, 0.99; P = 0.048).

Glioma patients were also found to have increased odds of class III obesity (OR: 49.68, 95% CI: 1.59, 1550; P = 0.03), as well as a family history of cancer (OR: 22.6, 95% CI: 2.40, 213; P = 0.006). In multivariable analysis, the best predictor of glioma diagnosis was cognitive difficulty (non-GBM, OR: 13300, 95% CI: 5.98– 2.94 × 10⁷, P = 0.02; GBM, OR: 61.7, 95% CI: 2.31–1650, P = 0.01).

Tumor size

NHPI patients had significantly larger tumor dimensions compared to White (22.00 mm, 95% CI: 1.00, 40.00, P = 0.04) and Asian (31.00 mm, 95% CI: 2.00, 54.00, P = 0.02) patients [ Table 2 ].

Schwannomas

In the cohort, 8.0% of the cases (n = 26) cases were schwannomas, of which 84.6% were vestibular schwannomas (n = 22) [ Table 1c ]. The median age at diagnosis was 51.0 years old (IQR: 41.5, 69.0).

Patients with schwannomas had significantly increased odds of being in the highest income quartile (5.49, 95% CI: 1.67, 18.27, P = 0.002) and from municipalities with a lower proportion of the populace below the poverty line (0.00, 95% CI: 1.57 × 10⁻⁵, 0.02; P = 0.02).

Patients with schwannomas had increased odds of glaucoma (17.07, 95% CI: 1.64, 853.14, P = 0.006), while vestibular schwannomas specifically had increased odds of having a positive alcohol screen (6.23, 95% CI: 1.69, 22.88, P = 0.006) and family history of cancer (3.89, 95% CI: 1.46, 10.36, P = 0.007). Patients with vestibular schwannomas also had decreased odds of depression/dysthymic disorder (0.13, 95% CI: 0.02, 1.03, P = 0.05) and other psychiatric disorders (0.09, 95% CI: 0.01, 0.72, P = 0.023). Meanwhile, vestibular schwannoma patients exhibited decreased odds of gastroesophageal reflux disease (GERD) (0.14, 95% CI: 0.0034, 0.94, P = 0.03).

On conducting multivariable analysis, the following variables were identified as the best predictors of vestibular schwannoma diagnosis, including being from the highest income quartile (24.88, 95% CI: 2.14, 289.14; P = 0.01), presenting with DNV (5.75, 95% CI: 1.13, 29.23; P = 0.04), and having a family history of cancer (14.66, 95% CI: 2.25, 95.30; P = 0.005).

Tumor size

NHPI patients exhibited significantly larger tumor size at the time of diagnosis (14.00, 95% CI: 1.00, 26.00, P = 0.048) compared to Asians.

Pituitary adenomas

About 6.8% of the cohort (n = 22) had pituitary adenomas, with a median age at diagnosis at 48 years (IQR: 36.50, 61.50), 11.0 years younger than controls (95% CI: 1.00, 20.00; P = 0.03) and no sex predisposition [ Table 1c ]. Whites demonstrated a reduced odds of diagnosis (0.25, 95% CI: 0.056, 0.85, P = 0.02), while NHPI had a 3.21 fold increased odds (95% CI: 0.97, 10.35, P = 0.048). Furthermore, patients with military insurance demonstrated a 12.22 fold increased odds of diagnosis (95% CI: 1.82, 138.40, P = 0.003).

Pituitary adenoma patients at diagnosis had a significantly higher median BMI, by 3.31 kg/m² (95% CI: 0.25, 6.02; P = 0.03), with obesity class I resulting in a 5.16 fold (95% CI: 1.33, 20.19; P = 0.01) greater odds of diagnosis. Meanwhile, having a history of psychiatric disorder (excluding depression) increased the odds of a pituitary adenoma diagnosis by 4.22 fold (95% CI: 1.29, 16.40; P = 0.01).

Intracranial metastases

In the tumor cohort, there were 36 patients (11.1%) with intracranial metastases [ Table 1c ]. Median age at diagnosis was 72.5 years (IQR: 60.75, 76.50), 12.64 years older than controls (95% CI: 6.00, 20.00; P = 0.0001). Patients with metastases were more likely to come from municipalities with a lower proportion of the population aged 65 and older living below the poverty line (0.004, 95% CI: 1.79 × 10^–5, 0.01; P = 0.01). The most common presenting symptom was seizures (3.72, 95% CI: 1.44, 9.64; P = 0.007). Patients were also more likely to have asthma or COPD (3.42, 95% CI: 1.42, 8.21; P = 0.006), to be former (4.45, 95% CI: 1.67, 11.87; P = 0.0030) or current smokers (5.54, 95% CI: 1.75, 17.52; P = 0.004), alcohol use disorder (5.27, 95% CI: 1.24, 22.42; P = 0.025), and family history of brain tumors (11.90, 95% CI: 1.03, 137.21; P = 0.05). The strongest predictors of intracranial metastases from diagnosis from multivariable analysis included the presenting symptom of seizures (5.23, 95% CI: 1.56, 17.50; P = 0.007), being a current smoker (12.28, 95% CI: 1.78, 84.51; P = 0.01), and having a family history of brain tumors (36.53, 95% CI: 1.90, 730.90; P = 0.02).

Tumor size

Although not statistically significant, Asian patients had significantly larger tumor sizes compared to NHPI (P = 0.05) [ Table 2 ].

DISCUSSION

Age and sex

Of the 323 tumors in the cohort, 49.2% were meningiomas (39.9% benign and 9.3% malignant), 12.1% gliomas (6.2% Grades I–III and 5.9% Grade IV), 11.1% intracranial metastases, 8.0% schwannomas (6.8% vestibular schwannomas), and 6.8% pituitary adenomas. These trends overall parallel those from the Central Brain Tumor Registry of the United States (CBTRUS), where meningiomas and gliomas were the most common primary intracranial lesions.[ 64 ]

The youngest age of diagnosis was among Grades I–III gliomas at 47.5 years (IQR: 31.5, 62.0), followed by pituitary adenomas at 48.0 (36.5, 61.5), vestibular schwannomas at 51.0 (41.5, 69.0), Grade IV gliomas at 59.0 (50.0, 70.5), benign meningiomas at 61.0 (50.0, 71.0), malignant meningiomas at 66.5 (49.0, 76.0), and metastases at 72.5 (60.8, 76.5). Although gliomas, schwannomas, meningiomas, and metastases had similar values to that of national datasets, the age of diagnosis for pituitary adenomas was younger than expected.[ 35 , 37 , 65 , 73 ] The discrepancy may reflect Hawaii’s smaller Black population, who are suspected to have an older age at diagnosis for pituitary tumors.[ 55 ]

Similar to the CBTRUS results, which found a 1.25 fold higher incidence rate of primary central nervous system tumors in females (26.31/100,000; males: 21.09/100,000), females in our Hawaiian cohort had a 1.44-fold higher odds of tumor diagnosis.[ 65 ] On tumor stratification, the female predisposition in our cohort was observed only in benign meningiomas; no other tumors exhibited a male or female predilection, in contrast to some cohorts that found an increased risk of high-grade gliomas in males.[ 36 , 65 , 69 , 79 ]

Race/ethnicity

Collectively, Hispanic/Latino patients in Hawaii exhibited a significantly lower likelihood of primary intracranial tumor diagnosis than non-Hispanic patients, similar to observations in CBTRUS.[ 65 ] While no clear rationale exists for this disparity, some have suggested differences in inherited risk, although given the known socioeconomic barriers (i.e., language, health literacy, acculturation, and income) that Hispanics face, reduced access to healthcare may contribute in part.[ 30 , 66 ]

In contrast to national datasets where NAAN had the lowest incidence rates of meningioma, NAAN in Hawaii had a 12.26 fold greater odds of benign meningioma diagnosis.[ 37 , 65 , 86 ] When examining non-central nervous system cancers, NAAN typically has among the highest incidence rates; thus, the discrepancy in Hawaii for benign meningiomas may represent the improved access to health care among the indigenous population – compared to other states, Hawaii has historically had lower uninsured rates, with higher rates of cancer screening and preventive care visits.[ 13 , 57 , 71 , 86 ]

For pituitary adenomas, NHPI had the highest odds of diagnosis at 3.21 (1.03, 10.35), followed by Asians at 1.49 (0.45, 4.60), Hispanics at 0.92 (0.09, 5.02) and Whites at 0.25 (0.06, 0.85); these trends parallel a previous nationwide study that found the highest incidence among Asians and Pacific Islanders, and the lowest among Whites.[ 35 ] Similar to NAAN, while improved access to health care in Hawaii for indigenous populations may contribute to the greater likelihood of NHPI diagnoses, pituitary adenomas are associated with the risk factor of obesity, which is itself prevalent in the NHPI population.[ 7 , 88 ]

Finally, the observation of higher rates of glioma diagnosis among Whites persisted in Hawaii, with the additional finding that Asians had significantly reduced odds of glioma diagnosis.[ 29 , 66 , 74 , 87 ] Given that genome-wide association studies have identified several susceptibility loci for glioma, with literature supporting unique genetic pathways for glioma tumorigenesis, the higher odds among Whites and lower odds among Asians may be due in part to genetic predisposition.[ 22 , 28 , 58 , 78 , 94 ]

Socioeconomic variables

Of the tumors investigated, malignant meningiomas, schwannomas, pituitary adenomas, and intracranial metastases exhibited unique socioeconomic associations. Malignant meningiomas, schwannomas, and metastases were all more likely to be diagnosed in patients from households with greater median income, higher income quartiles, or municipalities with less poverty. For meningiomas, prior literature is inconclusive regarding the role of socioeconomic status: one investigation from Sweden found an increased incidence of meningiomas in women with higher socioeconomic status, while a second Swedish investigation found no association.[ 60 , 90 ] Schwannomas have also been associated with higher socioeconomic status in both Denmark and the continental United States.[ 26 , 45 , 77 ] Overall, the lower odds of intracranial tumors among patients with lower socioeconomic status are likely to result from disparities in healthcare access, in turn contributing to underdiagnosis.[ 26 , 52 ] While geographic origin from an urban environment was significantly associated with greater odds of malignant meningioma diagnosis, a prior study focusing exclusively on spinal meningiomas found a higher incidence among patients from rural communities – suggesting likely different risk factors between cerebral and spinal meningiomas.[ 36 , 37 ]

The increased odds of brain tumors overall among Medicare patients may be due to an older age of diagnosis for brain tumors.[ 61 ] Only for pituitary adenomas were there increased odds of diagnosis among those with military insurance. This association may be due to mandatory visual-optical readiness testing of military personnel, which would likely allow for earlier detection of visual field deficits from pituitary adenomas.[ 18 , 84 ]

Furthermore, among gliomas, the odds of diagnosis were significantly higher among married patients, likely yielding from spouses more likely to appreciate cognitive changes in a patient.[ 48 ]

Presenting symptoms

In the overall brain tumor cohort, seizures were the most common presenting symptom, a finding consistent with prior studies.[ 3 , 5 ] When stratified by tumor type, seizures were the most common in benign meningiomas, all types of gliomas, and metastases. Cognitive difficulty was more commonly observed among malignant meningiomas and gliomas, likely arising from the infiltrative nature of such tumors.[ 17 , 31 ] The inclusion of cognitive difficulty, with seizures, as a common presenting symptom distinguishing malignant from benign meningiomas, could aid in narrowing the differential before histopathologic diagnosis. Vestibular schwannomas were the only tumor type within our cohort that presented with increased odds of dizziness, nausea, and vomiting; while consistent with the literature, progressive/sudden onset hearing loss was reported as more common, although the proportion of patients presenting with hearing loss as the most common presenting symptom has decreased over time.[ 33 , 54 , 89 ]

Medical comorbidities

In the entire cohort, brain tumors overall were associated with decreased odds of GERD and increased odds of hypertension, traumatic brain injury, obesity, diverticular disease, and a prior history of cancer. However, after multivariable analysis, only the association with obesity and prior history of cancer remained significant among the general cohort.

On stratification, benign meningiomas exhibited a positive association with hypertension and obesity, findings consistent with another study in a multiethnic cohort, potentially linking metabolic syndrome with an increased risk of meningioma.[ 59 ] Yet, the relationship inverted for malignant meningiomas, where patients were more likely to be underweight, likely representing cachexia. Obesity and pre-obesity were also found to be associated with gliomas, pituitary adenomas, and metastases, with clinical observations consistent with data suggesting that adipocyte cytokines may promote tumor growth.[ 1 , 4 , 6 , 75 ]

Asthma was associated with reduced odds of glioblastoma diagnosis, a trend paralleling that of pediatric brain tumors, where T-cell mediated disorders result in reduced tumor frequency.[ 21 , 41 , 44 , 83 , 91 ] The protective mechanism of asthma is suspected to be secondary to T-cell decorin-mediated microglial inhibition, which ultimately reduces glioma formation.[ 21 ]

The increased odds of glaucoma among schwannomas overall, as well as reduced odds of GERD among vestibular schwannomas and pituitary adenomas, have not been described in the literature. Of note, sensorineural hearing loss has been linked to an increased incidence of glaucoma; however, the etiology of the association remains unelucidated, and a link with schwannomas is unclear.

Psychiatric risk factors and social history

Our cohort did exhibit a correlation between a positive AUDIT-C screen with intracranial tumor diagnosis, contrary to prior studies that found no association between alcohol consumption and brain tumor risk.[ 9 , 11 , 25 , 34 , 76 ] A link with positive AUDIT-C was found in benign meningiomas and vestibular schwannomas, with alcohol use disorder being more likely in those with intracranial metastases. Although alcohol use has been linked to other cancers, positive AUDIT-C screens may also represent the use of alcohol as a coping mechanism for a recent tumor diagnosis.[ 24 ]

Smoking history was found only to be associated with intracranial metastases, most likely accounted for by the strong association between smoking and non-intracranial cancers.[ 82 ] Consistent with previous investigations, no association between meningiomas and smoking was observed in our cohort.[ 14 , 46 , 59 , 63 ]

Pituitary adenomas were the only tumor type to exhibit a positive correlation with psychiatric history.[ 70 , 85 , 92 ] The psychiatric disorders experienced by those with pituitary adenomas may be due to the pituitary tumor itself, treatment, or hormonal changes in the hypothalamic-pituitary axis – regardless, primary treating clinicians should have heightened awareness that such a patient population may require ancillary psychiatric care.[ 85 ] Unlike pituitary adenomas, vestibular schwannomas exhibited decreased odds of depression and other psychiatric disorders.

Family history

Family history of cancer is an established risk factor for brain tumors, consistent with our overall cohort, as well as when stratified among gliomas and vestibular schwannomas.[ 15 , 16 , 32 , 95 ] For benign meningiomas specifically, as in our cohort, two prior investigations suggest that having a first degree relative with a meningioma significantly increases the odds of diagnosis, as does a personal history of cancer.[ 23 , 53 ]

Tumor size at diagnosis associated with race

Several racial disparities were highlighted when examining tumor size at the time of diagnosis [ Table 3 ]. For benign meningiomas, Asians, NHPI, and Hispanics exhibited tumors nearly two-fold larger than Whites with the difference between NHPI and Whites are statically significant. While the finding among NHPI is unique, prior surveillance data from the United States corroborate our data, demonstrating that Black, Hispanic, and Asian populations often present with larger tumors than Whites.[ 8 ] Among gliomas, NHPI presented with significantly larger tumors than both Whites and Asians, while Asians had smaller gliomas than Whites; such differences may account the lower mortality rates among Asian glioma patients, provided the correlation between larger tumor size and worse survival.[ 12 , 20 , 43 , 68 ] For vestibular schwannomas, both Asians and NHPI had smaller tumors relative to Whites. Overall, these large tumor sizes among NHPI demonstrate a delay to diagnosis and, thus, carry important implications toward survival and outcome. [ 20 , 40 ] The results are likely due to the documented disparities in healthcare access that NHPI faces.[ 27 ]

Table 3:

Summary of associated variables stratified by tumor type.

Limitations

Although this exploratory study identified several novel correlations, the findings should be considered in the context of several limitations. First, the study is retrospective and relies on accurate documentation by healthcare workers. The reliance on ICD codes for case ascertainment leads to susceptibility to administrative errors in data entry, meaning that cases could be inadvertently undetected. Furthermore, certain social history and psychiatric risk factors may be susceptible to recall bias or patient’s reluctance to disclose due to stigmatization of mental health. Finally, a limited sample size may have decreased statistical power.

CONCLUSION

This study identified several sociodemographic differences in intracranial tumors, which, in turn, may have implications for diagnosis, treatment, and healthcare policy. For benign meningiomas, gliomas, and vestibular schwannomas, NHPI presented with significantly larger tumor volumes at diagnosis than Whites and/or Asians. There were greater odds of diagnosis of benign meningiomas among NAAN, increased odds of diagnosis of gliomas among Whites (reduced among Asians), and increased odds of pituitary adenomas among NHPI (reduced among Whites). Affluence was associated with a diagnosis of malignant meningioma, vestibular schwannoma, and intracranial metastases. Hence, among brain tumors, there are key healthcare disparities that may implicate survival outcomes being linked to a patient’s sociodemographic background.

Authors’ contributions

All authors contributed equally to the development of this project and manuscript.

Availability of data and material

Data supporting this study can be made available upon reasonable request. Further data can be found in Supplementary Table 1 (number of patients stratified by tumor dimensions, sociodemographics, and medical comorbidities) and Supplementary Tables 2a - c (multivariable logistic regression).

Code availability

Code supporting this study can be made available on reasonable request.

Ethics approval

The Institutional Review Board approval was obtained before the study from the University of Hawaii, Office of Research Compliance (protocol number: 2020-01010).

Declaration of patient consent

Patient’s consent was not required as there are no patients in this study.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The authors confirm that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

Supplementary material:

https://doi.org/10.25259/SNI_190_2024

Disclaimer

The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Journal or its management. The information contained in this article should not be considered to be medical advice; patients should consult their own physicians for advice as to their specific medical needs.

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