Geographic Trends and Disparities in Meningioma Mortality: A National Observational Study from 1999 to 2020 Utilizing the CDC Wonder Database

Abstract

Background

Meningiomas are benign tumors that surround the brain and spinal cord. While studies have examined disparities in meningioma outcomes nationally, the meningioma mortality rate based on urbanization level is unclear. This study utilized the Centers for Disease Control Wide-ranging Online Data for Epidemiologic Research (CDC WONDER) database to investigate meningioma mortality rates and disparities between urbanization categories.

Methods

Patients whose underlying cause of death was meningioma from 1999 to 2020 were included. Chi-squared testing determined mortality differences across urbanization groups (large central metro, large fringe metro, and medium metro). Multivariable logistic regression and adjusted odds ratios (OR) evaluated associations between death location and age, sex, race, and ethnicity. Age-adjusted meningioma mortality rates per 100,000 people with 95% confidence intervals (CIs) were reported.

Results

We analyzed a cohort of 12,274 patients. All older age groups demonstrated significantly lower odds of mortality in large central metros compared with other regions (OR: 0.54–0.69; p  < 0.05). Men had higher odds of dying in large central metro areas compared with women (OR: 1.09; p  = 0.047). Asian and African American patients had significantly higher odds of meningioma mortality in large central metros compared with their counterparts (OR: 7.53 vs. 3.42; p  < 0.001). Similarly, Hispanic/Latino ethnicity was associated with an increased likelihood of death in large central metros (OR: 6.97; p  < 0.001), but a lower likelihood in medium metros (OR: 0.77; p  = 0.029). No significant changes in mortality rates were observed based on urbanization.

Conclusion

We report that urbanization plays a significant role in meningioma mortality, with varying effects by sex, race, ethnicity, and age.

Introduction

Meningiomas, arising from the arachnoid cap cells of the meninges, are the most common primary intracranial neoplasms in adults, accounting for approximately 37% of all primary central nervous system tumors. ^{1 2} Epidemiological data from the Central Brain Tumor Registry of the United States (CBTRUS) have documented a concerning increase in annual incidence, nearly doubling from 4.52 per 100,000 population during 1998 to 2002 to 8.3 per 100,000 during 2010 to 2014, partially reflecting advancements in neuroimaging technology. ^{1 2} Despite their predominantly benign histological classification (WHO grade I, approximately 80% of cases), meningiomas can induce significant neurological morbidity and mortality through mass effect on adjacent neural structures, vascular compromise, or, in the case of higher-grade variants (WHO grades II and III), through direct parenchymal invasion. The clinical manifestations vary considerably depending on tumor location, size, and growth rate, ranging from asymptomatic incidental findings to severe neurological deficits, cognitive dysfunction, and life-threatening complications. ^{3 4 5}

The natural history and treatment outcomes of meningiomas are influenced by both patient-specific characteristics (age, sex, race/ethnicity, and socioeconomic status) and tumor-specific features (histological grade, molecular profile, location, and extent of surgical resection). Growing literature has uncovered concerning disparities in diagnostic approaches and therapeutic interventions across dimensions, including racial/ethnic background, insurance status, and geographical location. ^{6 7 8} Despite recognition of these disparities, a critical knowledge gap persists regarding potential variations in meningioma-associated mortality based on urbanization level. Understanding these location-specific differences may aid clinicians in identifying geographic risk factors, access to care issues, and health disparities that may be obscured when analyzing national data.

To address this gap, we conducted a comprehensive analysis utilizing the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) database. Our primary objective was to characterize meningioma-associated mortality rates across the urban–rural continuum and identify potential disparities based on urbanization level. Additionally, we examined temporal trends and potential interactions between urbanization and other sociodemographic factors.

Methods

Guidelines

The design and reporting of this study adhered to the strengthening the reporting of observational studies in epidemiology (STROBE) and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis + artificial intelligence (TRIPOD + AI) guidelines. ^{9 10}

Data Source and Patient Population

This study queried the CDC WONDER database for all patients whose underlying cause of death was meningioma from 1999 to 2020. Meningioma patients were identified using International Classification of Diseases-10-clinical modification diagnostic codes D32.0, D32.1, and D32.9. The rural–urban continuum codes (RUCC) were used to categorize patients by urbanicity into four groups: large central metro, large fringe metro, medium metro, small metro, and nonmetro. Data were stratified by year, age, gender, race, ethnicity, and geographical distribution. This study was deemed exempt from review by the Hospital of the University of Pennsylvania Institutional Review Board due to the utilization of a publicly available database containing de-identified data.

Outcomes

The primary outcome of this study was the assessment of meningioma mortality rates and disparities between urbanization categories. Secondary outcomes were reporting mortality trends per year and the proportion of mortality per year, where the proportion of mortality was defined as the geographic distribution of meningioma deaths across urbanization categories (i.e., the percentage of total annual meningioma deaths occurring in each region).

Variables

The following variables were extracted for each eligible patient: age range (44 or younger, 45–54, 55–64, 65–74, 75–84, and 85 or older), sex, race, Hispanic ethnicity, and geographic region (large central metro, large fringe metro, medium metro, small metro, and nonmetro). Urban locations were categorized as large central metro (e.g., New York City), large fringe metro (e.g., Dallas-Fort Worth), and medium metro (e.g., Milwaukee) based on guidelines established by the National Center for Health Statistics Urban–Rural Classification Scheme. ¹¹ Specifically, urban areas were categorized into medium and small metropolitan areas, which had a population between 50,000 and 999,999, and large metropolitan areas, which had a population of greater than 1,000,000. Rural regions were defined as having fewer than 50,000 people.

Statistical Analysis

For baseline demographic comparisons, chi-square tests of independence were used to assess differences in variables across geographic areas, followed by post hoc pairwise Z-tests for proportions with Bonferroni correction when multiple comparisons were necessary. The family-wide error rate was controlled at α  = 0.05. Multivariable logistic regression and adjusted odds ratios (OR) were calculated to evaluate associations between urbanization of death location and decedent age, sex, race, and ethnicity. The p -values reported for logistic regression analyses represent unadjusted values and were not subjected to multiple comparison corrections. Age-adjusted meningioma mortality rates per 100,000 people with 95% confidence intervals (CIs), during the 1999 to 2020 time period, were reported. p  < 0.05 was considered significant for individual comparisons. All statistical analysis and the figures were conducted and generated using Python, version 3.9.6 (Python Foundation, Wilmington, Delaware).

Results

Study Population

Between 1999 and 2020, 12,274 deaths due to meningiomas were recorded ( Table 1 ). The majority (81.7%) occurred in individuals aged 65 or older. Females accounted for 63.8% of deaths, exceeding mortality in males. Regarding race, White individuals comprised 82.9% of deaths, followed by Black or African American individuals (14.6%). The vast majority (96.3%) identified as non-Hispanic/Latino, while only 3.7% were Hispanic/Latino. Demographic distributions varied significantly across geographic regions for race ( p  < 0.001) and ethnicity ( p  = 0.013) but showed no significant differences for age ( p  = 0.835) or gender ( p  = 0.333). White patients comprised of majority of deaths across all urbanization categories, ranging from 69.3% in large central metro to approximately 93% in small metro and nonmetro areas. Notably, Asian or Pacific Islander deaths were concentrated entirely in large central metro and large fringe metro areas, with no cases recorded in smaller metropolitan or nonmetropolitan regions. The proportion of Hispanic/Latino deaths decreased markedly from urban to rural areas, ranging from 8.6% in large central metro areas to only 0.4% in small metro and nonmetro regions.

Table 1. Baseline characteristics of deceased patients.

	Total ( n  = 12,274)	Large central metro ( n  = 3,601)	Large fringe metro ( n  = 2,931)	Medium metro ( n  = 2,720)	Small metro and nonmetro ( n  = 3,022)	p -Value
Age (y; %)						0.538
44 or younger	199	86 (2.4)	50 (1.7)	52 (1.9)	11 (0.36)
45–54	646	228 (6.3)	154 (5.3)	140 (5.1)	124 (4.1)
55–64	1,393	454 (12.6)	326 (11.1)	307 (11.3)	306 (10.1)
65–74	2,491	749 (20.8)	525 (17.9)	568 (20.9)	649 (21.5)
75–84	3,978	1,128 (31.3)	971 (33.1)	875 (32.2)	1,004 (33.3)
85 or older	3,567	956 (26.5)	905 (30.9)	778 (28.6)	928 (30.7)
Gender (%)						0.333
Female	7,834	2,294 (63.7)	1,840 (62.8)	1,716 (63.1)	1,984 (65.7)
Male	4,440	1,307 (26.3)	1,091 (37.2)	1,004 (36.9)	1,038 (24.3)
Race (%)						<0.001
Asian/Pacific Islander	309	210 (5.8)	41 (1.4)	58 (2.1)	0 (0)
Black/African American	1,788	895 (24.9)	343 (11.7)	338 (12.4)	212 (7.0)
White	10,177	2,496 (69.3)	2,547 (86.9)	2,324 (85.4)	2,810 (93.0)
Ethnicity (%)						0.013
Non-Hispanic/Latino	11,814	3,293 (91.4)	2,878 (98.2)	2,634 (96.8)	3,009 (99.6)
Hispanic/Latino	460	308 (8.6)	53 (1.8)	86 (3.2)	13 (0.4)

Primary Outcomes

Large Central Metros

We reported that all older age groups had significantly lower odds of mortality in large central metros compared with other regions (odds ratio [OR]: 0.69; 95% CI: 0.49–0.97; p  = 0.031; OR: 0.56; 95% CI: 0.41–0.77; p  < 0.001; OR: 0.57; 95% CI: 0.42–0.77; p  < 0.001; OR: 0.54; 95% CI: 0.40–0.74, p  < 0.001; and OR: 0.56; 95% CI: 0.41–0.76; p  < 0.001), years 45 to 54, 55 to 64, 65 to 74, 75 to 84, 85 or older, respectively. Males exhibited higher mortality odds than females in large central metros compared with other regions (OR: 1.09; 95% CI: 1.00–1.19; p  = 0.047). Regarding racial differences, Black or African American patients had higher odds of mortality compared with White counterparts in large central metros than elsewhere (OR: 3.42; 95% CI: 3.07–3.80; p  < 0.001). Similarly, Asian or Pacific Islander patients showed significantly higher odds of death due to meningioma compared with White patients (OR: 7.53; 95% CI: 5.90–9.62; p  < 0.001). Hispanic/Latino ethnicity was associated with greater odds of mortality compared with non-Hispanic/Latino patients (OR: 6.97; 95% CI: 5.70–8.53; p  < 0.001; Table 2 ).

Table 2. Mortality in large central metro versus all others.

Variable	Coefficient	95% CI	SE	OR	95% CI	p -Value
Intercept	3.26	2.83–3.69	0.22	26.10	16.95–40.17	<0.001
Age (y)
44 or younger	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
45–54	−0.37	−0.71 to −0.03	0.17	0.69	0.49–0.97	0.031
55–64	−0.58	−0.90 to −0.26	0.16	0.56	0.41–0.77	<0.001
65–74	−0.57	−0.87 to −0.26	0.16	0.57	0.42–0.77	<0.001
75–84	−0.61	−0.92 to −0.31	0.15	0.54	0.40–0.73	<0.001
85 or older	−0.58	−0.89 to −0.28	0.16	0.56	0.41–0.76	<0.001
Gender
Female	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Male	0.09	0.00–0.17	0.04	1.09	1.00–1.19	0.047
Race
White	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Black/African American	1.23	1.12–1.33	0.05	3.42	3.07–3.80	<0.001
Asian/Pacific Islander	2.02	1.77–2.26	0.12	7.53	5.90–9.62	<0.001
Hispanic ethnicity
Non-Hispanic/Latino	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Hispanic/Latino	1.94	1.74–2.14	0.10	6.97	5.70–8.53	<0.001

Abbreviations: CI, confidence interval; OR, odds ratio; SE, standard error.

Large Fringe Metros

In large fringe metro areas, Black or African American and Asian or Pacific Islander patients had significantly lower odds of mortality compared with White patients (OR: 0.70; 95% CI: 0.62–0.79; p  < 0.001; OR: 0.45; 95% CI: 0.32–0.62; p  < 0.001, respectively). Hispanic/Latino individuals demonstrated significantly lower odds of mortality compared with non-Hispanic/Latino individuals (OR: 0.38; 95% CI: 0.28–0.50; p  < 0.001; Table 3 ).

Table 3. Mortality in large fringe metro versus all others.

Variable	Coefficient	95% CI	SE	OR	95% CI	p -Value
Intercept	−0.99	−1.31 to −0.66	0.17	0.37	0.27–0.52	<0.001
Age (y)
44 or younger	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
45–54	−0.08	−0.45 to 0.29	0.19	0.93	0.64–1.34	0.689
55–64	−0.07	−0.42 to 0.27	0.18	0.93	0.66–1.31	0.677
65–74	−0.26	−0.59 to 0.08	0.17	0.77	0.55–1.08	0.134
75–84	−0.07	−0.40 to 0.26	0.17	0.93	0.67–1.30	0.681
85 or older	−0.04	−0.37 to 0.29	0.17	0.96	0.69–1.34	0.803
Gender
Female	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Male	0.05	−0.04 to 0.14	0.04	1.05	0.96–1.15	0.269
Race
White	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Black/African American	−0.36	−0.49 to −0.23	0.06	0.70	0.62–0.79	<0.001
Asian/Pacific Islander	−0.81	−1.14 to −0.47	0.17	0.45	0.32–0.62	<0.001
Hispanic ethnicity
Non-Hispanic/Latino	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Hispanic/Latino	−0.98	−1.27 to −0.69	0.15	0.38	0.28–0.50	<0.001

Abbreviations: CI, confidence interval; OR, odds ratio; SE, standard error.

Medium Metros

In medium metro areas, Black or African American individuals showed lower odds of mortality compared with White patients (OR: 0.77; 95% CI: 0.68–0.88; p  < 0.05). Hispanic/Latino individuals had significantly lower odds of mortality compared with non-Hispanic/Latino individuals (OR: 0.77; 95% CI: 0.60–0.97; p  = 0.029; Table 4 ).

Table 4. Mortality in medium metro versus all others.

Variable	Coefficient	95% CI	SE	OR	95% CI	p -Value
Intercept	−0.98	−1.30 to −0.66	0.16	0.38	0.27–0.52	<0.001
Age (y)
44 or younger	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
45–54	−0.25	−0.61 to 0.12	0.19	0.78	0.54–1.13	0.190
55–64	−0.22	−0.56 to 0.12	0.17	0.80	0.57–1.13	0.207
65–74	−0.19	−0.52 to 0.14	0.17	0.82	0.59–1.15	0.251
75–84	−0.25	−0.57 to 0.08	0.17	0.78	0.56–1.08	0.135
85 or older	−0.27	−0.60 to 0.06	0.17	0.76	0.55–1.06	0.104
Gender
Female	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Male	0.03	−0.04 to 0.14	0.04	1.05	0.96–1.15	0.269
Race
White	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Black/African American	−0.26	−0.39 to −0.13	0.07	0.77	0.68–0.88	<0.001
Asian/Pacific Islander	−0.26	−0.55 to 0.03	0.15	0.77	0.58–1.03	0.082
Hispanic ethnicity
Non-Hispanic/Latino	Ref.	Ref.	Ref.	Ref.	Ref.	Ref.
Hispanic/Latino	−0.27	−0.51 to −0.03	0.12	0.77	0.60–0.97	0.029

Abbreviations: CI, confidence interval; OR, odds ratio; SE, standard error.

Secondary Outcomes

The largest proportion of meningioma-related deaths (30–35%) occurred consistently in large central metro areas throughout the study period. Large and medium fringe metro areas each accounted for approximately 20 to 25% of deaths, with medium metro regions showing greater fluctuations. Small metro and micropolitan (nonmetro) areas maintained a consistent proportion of 5 to 10% of deaths, despite some variations. Noncore (nonmetro) areas consistently had the lowest proportion of deaths, with a notable decrease observed from 2012 to 2019 ( Supplementary Table S1 , available in the online version only).

Large central metros demonstrated a crude mortality rate of 0.15 to 0.20 per 100,000 population, with a gradual decrease observed from 1999 to 2010. Large fringe metro areas had slightly higher rates (0.20–0.25), while medium metro areas showed even higher rates (0.20–0.30). Small metro and nonmetro areas exhibited markedly higher crude mortality rates compared with more urbanized regions. Both small metro and micropolitan areas had rates ranging from 0.25 to 0.35, while noncore areas consistently showed the highest crude mortality rates (approximately 0.30–0.40), with pronounced fluctuations across several years ( Supplementary Table S2 , available in the online version only; Fig. 1 ).

Fig. 1.

This figure shows the mortality trends per year and the proportion of mortality per year. ( A ) Large central metro; ( B ) large fringe metro; ( C ) medium metro; ( D ) small metro; ( E ) micropolitan (nonmetro); ( F ) noncore (nonmetro).

Discussion

To the best of our knowledge, this was the first study to primarily investigate the meningioma mortality rates and disparities between urbanization categories. Secondary outcomes included reporting mortality trends per year and the proportion of mortality per year. The analysis of over 12,000 deaths demonstrates that while older adults and females represent the majority of meningioma mortality cases nationwide, there are striking differences in mortality odds across urban–rural settings. Notably, large central metropolitan areas showed lower mortality odds for older age groups but significantly higher odds for racial and ethnic minorities compared with their white counterparts. This pattern notably reversed in large fringe and medium metropolitan areas, where Black or African American, Asian or Pacific Islander, and Hispanic/Latino patients experienced lower odds of mortality. Additionally, the crude mortality rates followed a distinct geographic gradient, with more rural areas consistently demonstrating higher mortality rates than urban centers throughout the study period. These findings suggest that geographic location significantly influences meningioma mortality risk and highlight potentially concerning disparities in healthcare delivery, access, or quality across the urban–rural continuum.

The existing literature has documented disparities in both access to meningioma treatment and outcomes based on various patient-specific factors. Jackson et al, in their retrospective cohort study, demonstrated that minority patients are more likely to present with severe symptoms, require extended perioperative hospitalization, and incur higher hospitalization costs, suggesting a delayed presentation to healthcare services. ⁶ Lei et al conducted a meta-analysis revealing significant disparities in outcomes for Black patients and those of lower socioeconomic status undergoing meningioma resection. ¹² Additionally, Pugazenthi et al examined socioeconomic status at the county level and its relationship to meningioma incidence, treatment approaches, and survival outcomes in the United States. ¹³ Their findings indicated that higher socioeconomic status at the county level correlates with increased meningioma incidence, greater likelihood of surgical intervention, and improved survival rates. Particularly relevant to our study, they noted that metropolitan areas demonstrated a higher overall incidence of meningioma compared with nonmetropolitan regions. However, at the time of our analysis, no studies had comprehensively investigated the association between meningioma mortality trends and urbanization levels.

The predominance of meningioma mortality among females (63.8%) is consistent with established epidemiological patterns, reflecting the well-documented 2:1 female-to-male incidence ratio in meningioma diagnosis. This sex-based disparity has been attributed to hormonal influences, particularly the expression of progesterone receptors and estrogen receptors, which may modulate tumor growth and aggression. ¹⁴ However, our finding that males exhibited 9% higher mortality odds in large central metros than in other regions suggests urban-specific factors affecting male outcomes. Prior research documented that males present with higher-grade meningiomas at diagnosis, potentially reflecting delayed care-seeking behavior. ^{14 15} Additionally, studies investigating molecular analyses demonstrated that TERT promoter mutations, associated with a more aggressive clinical course and higher mortality in meningioma patients, are significantly more common in male patients, potentially contributing to our observed sex-based geographic differences in mortality. ^{16 17}

The age distribution of meningioma deaths, with 61.5% occurring in individuals aged 65 or older, reflects both the increasing incidence of meningioma with age and age-related challenges in management. We highlighted that all older age groups demonstrated significantly lower odds of mortality in large central metros, revealing an urban advantage for geriatric patients. This may be explained by several factors, such as the availability of specialized geriatric neurosurgical assessments that better stratify operative risk, access to advanced stereotactic radiosurgery techniques that offer noninvasive management options for frail elderly patients, and better management of comorbidities through multidisciplinary approaches. Literature has demonstrated that comprehensive geriatric assessment prior to neurosurgical intervention significantly improved outcomes in elderly brain tumor patients. ¹⁸ Furthermore, increased hospital volume is associated with reduced mortality for elderly patients undergoing surgery, with high-volume centers concentrated predominantly in urban areas. ¹⁹

The racial disparities observed across different urban contexts present nuanced patterns that show how healthcare systems function differently for various racial groups depending on geographical settings. In large central metros, Black or African American patients experienced 3.42 times higher odds of mortality compared with White patients, a disparity largely consistent with known structural inequities in urban healthcare delivery. Previous studies documented that Black patients with brain tumors were significantly less likely to receive care at high-volume neurosurgical centers despite geographic proximity. ^{20 21} This disparity is particularly consequential as hospital case volume significantly predicts outcomes for complex cranial procedures. ^{21 22 23 24 25 26}

The strikingly high mortality odds for Asian or Pacific Islander patients in large central metros represent one of the most extreme disparities in our findings. This population, often aggregated in research despite substantial heterogeneity, faces unique barriers in urban healthcare systems. Asian Americans, particularly those with limited English proficiency, experience significant delays in specialty referrals within complex healthcare systems. ^{27 28} Similarly, Manuel et al found that language barriers were associated with extended length of stay, higher hospitalization cost, and discharge to skilled care in neurosurgical patients who undergo craniotomy for brain tumors, with significant implications for recurrence and mortality. ²⁹ The inadequate provision of culturally and linguistically appropriate services in complex urban healthcare systems may explain these disparities. Interestingly, we observed a reversal of racial disparities in less urbanized settings. In large fringe metro areas, Black or African American patients had 30% lower odds of mortality compared with White patients, with similar patterns observed in medium metro areas. This geographical reversal may be explained by healthcare system factors, including differential care-seeking patterns where White patients may more frequently travel from outlying areas to urban academic centers for specialized neurosurgical care, potentially concentrating higher-risk White patients in urban mortality statistics while local Black patients receive care within more uniform regional healthcare systems. ³⁰ Relatedly, continuity of care, more common in less fragmented healthcare systems, significantly improves outcomes for patients with medical conditions requiring multidisciplinary management. ^{31 32} Asian and Pacific Islander patients also demonstrated this geographical reversal, with 55% lower mortality odds in large fringe metros compared with White patients. This dramatic shift likely reflects differences in social integration, counseling, and healthcare navigation resources. ^{33 34}

Hispanic/Latino patients demonstrated perhaps the most dramatic geographical variation in outcomes, with nearly sevenfold higher mortality odds in large central metros, but significantly lower mortality odds in large fringe metros and medium metros. This extreme disparity underscores how urbanicity shapes healthcare experiences for ethnic minorities. In urban centers, Hispanic patients, particularly recent immigrants, face substantial barriers, including language concordance issues, inadequate interpreter services, and culturally insensitive care models. ^{35 36 37} It has been reported that Hispanic patients with limited English proficiency experienced significantly higher rates of adverse events during hospital care compared with English-speaking patients. ^{38 39} Furthermore, Pandey et al found that language barriers significantly impact comprehension of treatment plans and follow-up care instructions, with direct implications for treatment adherence and clinical outcomes. ³⁶ The dramatically improved outcomes for Hispanic patients in suburban and medium metro settings likely reflect several factors: (1) better integration of language services in healthcare systems serving more stable Hispanic communities versus recent immigrants, (2) higher rates of established primary care relationships facilitating appropriate specialist referrals, and (3) potentially higher rates of health insurance coverage. There is substantial variation in insurance coverage and healthcare continuity for Hispanic populations across different geographic contexts, with implications for specialty care access and outcomes.

The secondary outcome data reveal a consistent gradient of increasing crude mortality rates with decreasing urbanicity. While large central metros demonstrated crude mortality rates of 0.15 to 0.20 per 100,000 population, noncore areas consistently showed the highest rates (approximately 0.30–0.40 per 100,000). This gradient reflects fundamental disparities in neurosurgical infrastructure, as there is significant maldistribution of neurosurgical providers, with rural areas having approximately one-fifth of the per capita neurosurgical workforce of urban centers. These workforce limitations directly impact treatment options. Additionally, access to neurosurgical care significantly predicts outcomes for intracranial tumors, with delayed intervention associated with poorer prognosis. ^{40 41}

The technological gap in rural settings further compounds these disparities. Advanced neuroimaging techniques critical for meningioma diagnosis and monitoring, such as functional MRI and perfusion studies, are substantially less available in rural facilities. ^{42 43 44 45} Similarly, access to stereotactic radiosurgery, a cornerstone of modern management for surgically challenging meningiomas, is concentrated in urban academic centers. ^{46 47 48} Riggs et al found significant associations between distance to specialty care centers and utilization rates of advanced treatment modalities for brain tumors, with rural patients less likely to receive state-of-the-art interventions despite meeting clinical criteria. ⁴⁹ Additionally, the pronounced fluctuations in mortality rates in noncore areas reflect the vulnerability of rural healthcare systems to provider turnover and infrastructure changes. Rural neurosurgical coverage is often dependent on individual providers, with the departure of a single neurosurgeon creating immediate access gaps. ^{50 51 52} Furthermore, the financial instability of rural hospitals during the study period, with numerous rural hospital closures between 2010 and 2019, disrupted established referral networks. ⁵³

Limitations

The study must be interpreted within the context of its limitations. First, the retrospective design of this study introduces potential selection and information biases that may have influenced our results. Second, the CDC WONDER database provides only aggregated data, which significantly limits our ability to control for crucial confounding variables related to the cause of death, including comorbidities, socioeconomic factors, and healthcare access disparities. Third, statistical power constraints necessitated combining certain age ranges, potentially obscuring age-specific trends that might have clinical relevance. Fourth, we were unable to include small and nonmetro population groups in our analysis due to insufficient sample sizes, creating a notable gap in our understanding of these potentially vulnerable communities. Fifth, there may be confounding by treatment location versus residence. The CDC WONDER database classifies deaths by the geographic location where death occurred, not the patient's place of residence. This creates important interpretive challenges, as patients who travel from rural or suburban areas to receive specialized neurosurgical care at large urban academic centers would be categorized as urban deaths regardless of their actual residence or initial treatment location. This geographic misclassification may be particularly pronounced for certain demographic groups, as patients with greater financial resources, insurance coverage, or social capital may be more likely to seek care at distant tertiary centers. Consequently, our observed geographic mortality patterns may reflect a mixture of true residence-based disparities and differential healthcare-seeking behaviors across demographic groups, limiting our ability to distinguish between inherent geographic risk factors and treatment access patterns. Future studies should consider more comprehensive multiple comparison adjustments, such as residence data, tumor characteristics, socioeconomic indicators, and treatment details, when examining numerous subgroup analyses simultaneously. Finally, our exclusive reliance on CDC WONDER data, while providing a standardized national perspective, restricts generalizability to populations not adequately represented in this database and limits comparisons with findings from alternative data sources.

Conclusion

This analysis of meningioma-associated mortality across the urban–rural continuum reveals pronounced geographical and demographic disparities that challenge simplistic narratives about healthcare access. While rural areas exhibit higher crude mortality rates, the impact of urbanization is significantly modulated by demographic factors. In large central metros, racial and ethnic minorities faced substantially higher mortality odds compared with White patients, yet these disparities diminished or reversed in less urbanized settings. These patterns suggest that the concentration of neurosurgical specialists and advanced treatment facilities in urban centers fails to translate into equitable outcomes for all populations. Addressing these disparities will require targeted interventions that consider how geographical and demographic factors interact, including culturally appropriate navigation services in urban settings, expanded telemedicine infrastructure for rural areas, and standardized treatment protocols to mitigate inconsistencies in care quality affecting vulnerable populations. Future studies should investigate more correlational reasons, such as healthcare policy and socioeconomic factors, for the trends observed in this study. Although highlighting the current trends in meningioma mortality is important, exploring preventative methods to improve the mortality rate will benefit patients.