# paper.pdf ## Metadata - PDFFormatVersion=1.4 - Language=en-US - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Creator=Arbortext Advanced Print Publisher 9.1.510/W Unicode - ModDate=D:20260602150521+05'30' - Title=WNL-2025-207241 1..11 - Producer=Acrobat Distiller 7.0 (Windows) - CreationDate=D:20260602125346+05'30' - xmp:modifydate=2026-06-02T15:05:21+05:30 - xmp:metadatadate=2026-06-02T15:05:21+05:30 - xmp:createdate=2026-06-02T12:53:46+05:30 - xmp:creatortool=Arbortext Advanced Print Publisher 9.1.510/W Unicode - pdf:producer=Acrobat Distiller 7.0 (Windows) - dc:title=WNL-2025-207241 1..11 ## Contents ### Page 1 ![Figure]() RESEARCH ARTICLE OPEN ACCESS # Rural-Urban Disparities in Epilepsy Outcomes inthe United States Edward R. Bader,1,* William S. Kemball-Cook,1,* Joshua A. Benton,1 Nathaniel J. Killian,1 Alexis D. Boro,2 andEmad N. Eskandar1 Neurology® 2026;107:e218053. doi:10.1212/WNL.0000000000218053 ## Abstract ### Background and Objectives Rural residence is linked to poor access to neurologists and specialized epilepsy centers, yet itsimpact on clinical epilepsy outcomes remains unclear. To address this knowledge gap, weassessed the association between rurality and epilepsy outcomes in a nationally representativecohort in the United States. ### Methods We conducted a retrospective cohort study using the National Inpatient Sample (2016–2021),including patients with a primary diagnosis of epilepsy and recurrent seizures. The primaryexposure was patient rurality, defined using the National Center for Health Statistics UrbanRural Classification Scheme for Counties. Logistic regression models were used to study theeffect of rurality on in-hospital mortality, presenting in status epilepticus, prolonged length ofstay, nonroutine discharge, and receipt of EEG. Models were adjusted for demographic, socioeconomic, and hospital-related characteristics and Elixhauser comorbidities. Subanalysesexamined patients presenting in status epilepticus, those with private insurance, and thoseadmitted to urban teaching hospitals. ### Results A total of 841,445 epilepsy admissions were included (median age 56 years, 47.2% female).After adjusting for covariates, patients from the most rural counties experienced significantlyhigher odds of in-hospital mortality (odds ratio [OR] 1.93 [95% CI 1.56–2.39], p < 0.001),presenting in status epilepticus (OR 1.32 [95% CI 1.24–1.41], p < 0.001), and prolonged lengthof stay (OR 1.29 [95% CI 1.19–1.41], p < 0.001), relative to patients from the most urbancounties. The most rural patients also experienced lower odds of receiving EEG (OR 0.88 [95%CI 0.77–1.00], p = 0.047) and nonroutine discharge (OR 0.90 [95% CI 0.85–0.96], p = 0.001).When subanalyzing only patients with private insurance, the associations between rurality andmortality, presenting in status epilepticus, and prolonged length of stay were no longerobserved. ### Discussion Increasing rurality was associated with markedly worse epilepsy outcomes, including nearlydouble the odds of in-hospital mortality. The attenuation of these disparities among privatelyinsured patients suggests that modifiable structural barriers drive rural-urban disparities, ratherthan geography alone. Inherent to observational studies, residual confounding and limitedclinical granularity remain important considerations. These findings underscore the urgentneed for targeted public health interventions to improve outcomes for rural epilepsypopulations. CorrespondenceDr. Bader RELATED ARTICLE ![Figure]() #### Editorial Rural Health DisparitiesAre Not Inevitable Page e218166 MORE ONLINE #### Supplementary Material *These authors contributed equally to this work as co-first authors. 1Department of Neurological Surgery, Albert Einstein College of Medicine, Bronx, NY; and2Department of Neurology, Albert Einstein College of Medicine, Bronx, NY. The Article Processing Charge was funded by the authors. This is an open access article distributed under the terms of the, where it is permissible todownload and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Copyright © 2026 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. e218053(1) ### Page 2 ## Glossary HCUP = Healthcare Cost and Utilization Project; ICD-10-CM = International Classification of Diseases-Tenth EditionClinical Modification; IQR = interquartile range; NCHS = National Center for Health Statistics; NIS = National InpatientSample; OR = odds ratio. ## Introduction Epilepsy is one of the most prevalent neurologic disorders,affecting nearly 3 million adults in the United States alone.1,2Despite advancements in epilepsy diagnosis and treatment,numerous disparities in epilepsy care persist.3-5 Previousstudies have found that increasing rurality is associated withreduced access to EEG6 and epilepsy centers.7 However, theextent to which rurality affects epilepsy outcomes remainsunderexplored. It is well documented that increasing rurality is associated withhigher mortality rates and shortened life expectancy in theUnited States.8-10 Epilepsy is a complex chronic condition,often requiring specialized neurologic care,11 timely diagnosis,12 and consistent access to antiseizure medications.13,14Furthermore, delayed intervention in status epilepticus cancritically affect outcomes.15 However, for people with epilepsyresiding in rural geographic locations, these needs may be moredifficult to meet. Critically, people with epilepsy may facebarriers in access to neurologists, epilepsy specialists, diagnosticservices, and surgical procedures.16-18 Nevertheless, the association between increasing rurality andepilepsy-related mortality remains unclear. Given that peoplewith epilepsy have a substantially increased risk of prematuredeath,19 it is crucial to understand the impact of rurality onepilepsy outcomes to optimize targeted public health interventions. To address this knowledge gap, we performeda nationwide retrospective cohort study to investigate therelationship between rurality and outcomes in epilepsy. ## Methods ### Standard Protocol Approvals, Registrations,and Patient Consents This study used the National Inpatient Sample (NIS), a database of inpatient hospital admissions in the United States, andpart of the Healthcare Cost and Utilization Project (HCUP).20The analyses presented here were performed in keeping withthe HCUP Data Use Agreement. The NIS is a publicly availablelimited data set containing no direct patient identifiers: therefore, as specified by the HCUP Data Use Agreement, no ethicalapproval or written informed consent is required.20 This studyfollows the Strengthening the Reporting of ObservationalStudies in Epidemiology guidelines.21 ### Data Source, Inclusion Criteria, and Covariates We performed a retrospective cohort study using the2016–2021 NIS data sets. The NIS contains data from a national sample of approximately 7 million hospital admissions annually; by applying sampling weights, the NIS can beused to provide national estimates of approximately 35 million annual admissions.20 Using International Classification ofDiseases-Tenth Edition-Clinical Modification (ICD-10-CM)codes, cases were included if they had a primary diagnosis ofepilepsy and recurrent seizures (G40.x). Patients admittedelectively or younger than 18 years were excluded (Figure 1). The primary exposure of interest was patient rurality (countyof residence), defined using the National Center for HealthStatistics (NCHS) Urban-Rural Classification Scheme forCounties.22 This classification categorizes counties based ontheir metropolitan status and population size. Counties wereassigned to an ordinal scale from 1 (most urban) to 6 (mostrural): (1) large central metropolitan, (2) large fringe metropolitan, (3) medium metropolitan, (4) small metropolitan,(5) micropolitan, and (6) noncore. The following additional variables were extracted: age, sex(dichotomous classification of male or female), race or ethnicity, primary expected payer, smoking status (ICD-10-CMcodes F17.210, F17.211, F17.213, F17.218, F17.219), Elixhauser comorbidities, hospital size (bed count), hospitalteaching status, hospital control/ownership, census region ofhospital, and median household income quartile for patients’ZIP code. In the NIS, race and ethnicity are collapsed intoa single variable with the following categories: White, Black,Hispanic, Asian or Pacific Islander, Native American, andOther. Notably, demographic data in the NIS are provided byhospitals, and therefore, exact reporting practices may vary:for example, whether variables are self-reported or providerassigned. This is particularly relevant to race/ethnicity, whereindividuals might occupy more than one category, and it is notclear under what circumstances individuals are categorized as“Other.” Furthermore, where individuals are categorized asboth Hispanic and a separate racial category by a hospital,Hispanic takes precedence in the NIS: for example, an individual categorized as both Black and Hispanic would beassigned a value of “Hispanic.” Elixhauser comorbidities,a well-validated standardized set of diagnoses used forcomorbidity adjustment, were calculated using the elixhauserStata command based on ICD-10-CM codes.23,24 ### Outcome Measures Inpatient mortality was the primary outcome. The followingadditional outcomes were analyzed: odds of presenting instatus epilepticus, prolonged length of stay (>7 days admission), nonroutine discharge (discharge to any destinationother than home or self-care; specifically, any value other than Neurology | Volume 107, Number 1 | July 14, 2026 e218053(2) ### Page 3 ![Figure]() After exclusions and application of dischargesampling weights, the final cohort was representative of 841,445 epilepsy admissions. 1 for the “DISP” NIS variable), and odds of receiving EEG(ICD-10 Procedure Coding System codes: 4A00X4Z or4A10X4Z). ### Handling of Missing Data All variables had <5% missingness and were handled usingcomplete case analysis (Figure 1).25,26 ### Statistical Analysis Statistical analyses were performed using Stata 18.0/MP(StataCorp LLC, College Station, TX). For univariate statistics, categorical variables were compared across rurality levelsusing omnibus χ2 tests while continuous variables were analyzed using univariate linear regression. Multivariable logistic regression models were built to studythe independent effect of rurality on in-hospital mortality,presenting in status epilepticus, prolonged length of stay,nonroutine discharge, and odds of receiving EEG. All modelsadjusted for age, race or ethnicity, primary expected payer,smoking status, Elixhauser comorbidities, hospital size (bedcount), hospital teaching status, hospital control/ownership,census region of hospital, median household income quartilefor patients’ ZIP code, and rurality. We also performed subanalyses limited to the following subgroups: patients diagnosed with status epilepticus (G40x.1), patients withprivate insurance, and patients admitted to urban teachinghospitals. Because significant differences in the racial/ethnicdistribution across levels of rurality were observed on univariate analysis, we assessed for a race/ethnicity-rurality interaction through inclusion of an interaction term in theprimary mortality model. Evidence of interaction was evaluated using a global Wald test across categorical levels of race/ ethnicity and rurality, with a p value of <0.05 indicating evidence of significant interaction. Additional exploratory subanalyses were performed to examine whether epilepsyetiology modified the association between rurality and theprimary outcome of in-hospital mortality. Because the primary code G40.x does not indicate the cause of epilepsy,etiology subgroups were defined using secondary ICD-10diagnoses consistent with distinct epilepsy etiologies, including poststroke epilepsy (I69.x) and tumor-associatedepilepsy (C71.x, D33.x, D43.x, D49.6, C79.31). Rurality was analyzed both categorically (reference: firstlevel of NCHS Urban-Rural Classification) and continuously to estimate linear trend.27,28 Rurality p values for trendwere calculated by including rurality as a continuous variablein logistic regression models. The assumption of a linearrelationship between continuous rurality and log odds of theoutcome was verified using the Box-Tidwell test, with a pvalue of <0.05 indicating significant nonlinearity; where thisassumption was violated in the primary analysis for a givenoutcome, trend analyses were not reported for subanalysesof that outcome. Trend analyses assume equal spacing between levels of rurality, an assumption that may not strictlyhold; accordingly, results should be interpreted as reflectingoverall trends rather than differences between adjacentcategories. Forest plots were generated using MATLAB R2024b(MathWorks, Natick, MA). ### Data Availability In keeping with the HCUP Data Use Agreement, sharing ofthe data used in this study is not permitted. Neurology | Volume 107, Number 1 | July 14, 2026 e218053(3) ### Page 4 ## Results ### Population Characteristics The analysis provided estimates for 841,445 epilepsy admissions after exclusions (Figure 1). Table 1 lists cohort characteristics (eTable 1 provides full cohort comorbiditycharacteristics). The median age was 56 years (interquartilerange [IQR] = 39–68), with patients from the most ruralcounties older than those from the most urban counties (mostrural median [IQR] = 58 [42–70] vs most urban median[IQR] = 55 [37–67], p < 0.001). Overall, 47.2% of patientswere female, which varied across rurality levels (most rural48.3% vs most urban 45.8%, p < 0.001). Significant racial/ethnic differences were observed across rurality levels (p < 0.001). In the most rural counties, 76.5% ofpatients were White, 16.0% Black, and 3.1% Hispanic. Bycontrast, the most urban counties had lower proportions ofWhite patients (38.7%) and higher proportions of Black(36.2%) and Hispanic (17.3%) patients. Insurance coverage also varied by rurality (p < 0.001).Compared with urban areas, patients in the most ruralcounties were more likely to have Medicare (most rural 52.0%vs most urban 42.9%) and less likely to have Medicaid (mostrural 21.8% vs most urban 31.4%). Private insurance was lesscommon in rural counties (most rural 16.3% vs most urban16.9%). Moreover, patients from the most rural counties weresignificantly more likely to be in the lowest ZIP code incomequartile compared with those in the most urban counties(most rural 66.7% vs most urban 39.7%, p < 0.001). Hospital admissions predominantly occurred at urbanteaching hospitals across all rurality levels (76.6%). Amongthe most rural patients, 59.0% were admitted to urbanteaching hospitals compared with 30.4% at rural hospitals,which was lower than the 85.2% admitted to urban teachinghospitals in the most urban counties (p < 0.001). In addition,hospital admissions for the most rural counties were concentrated in the South (60.6%) and Midwest (24.6%),whereas admissions for the most urban counties were distributed across the South (30.9%), Northeast (23.5%), West(26.1%), and Midwest (19.5%) (p < 0.001). ### Rurality and Epilepsy Outcomes Multivariable logistic regression models were used to investigate the independent effect of rurality on epilepsy outcomes, with in-hospital mortality as the primary outcome.Secondary analyses evaluated the association between ruralityand status epilepticus at presentation, prolonged length ofstay, nonroutine discharge, and receipt of EEG. For in-hospital mortality (Table 2A; Figure 2A), increasingrurality was associated with higher odds of mortality (oddsratio [OR] 1.16 [95% CI 1.12–1.20], p for trend <0.001).When analyzed categorically, patients from the most ruralcounties experienced 93% higher odds of in-hospital mortality compared with patients from the most urban counties (OR1.93 [95% CI 1.56–2.39], p < 0.001). Subanalysis of patientspresenting in status epilepticus (eTable 2A) revealed a similarassociation (OR 1.14 [95% CI 1.10–1.20], p fortrend <0.001), with patients from the most rural countiesexperiencing 74% higher odds of in-hospital mortality thanpatients from the most urban counties (OR 1.74 [95% CI1.32–2.29], p < 0.001), suggesting that the increased odds ofmortality associated with rurality persist even in status epilepticus. Notably, in the subanalysis of patients with privateinsurance (eTable 3A), the association between rurality andmortality was no longer observed (OR 1.07 [95% CI0.98–1.18], p for trend = 0.127), suggesting that access toprivate insurance is associated with attenuation of the ruralurban disparities in epilepsy mortality outcomes. In the subanalysis of patients admitted to urban teaching hospitals(eTable 4A), increasing rurality continued to be associatedwith increased mortality (OR 1.17 [95% CI 1.13–1.22], p fortrend <0.001). No evidence of a significant race/ethnicityrurality interaction was observed (p = 0.530). Exploratorysubanalyses regarding epilepsy etiology (eTable 5) suggestedthat rurality was associated with increased mortality in poststroke epilepsy (OR 1.18 [95% CI 1.07–1.30], p for trend =0.001) but not tumor-associated epilepsy (OR 1.09 [95% CI0.97–1.23], p for trend = 0.136). For status epilepticus at presentation, increasing rurality wasassociated with higher odds of status epilepticus (OR 1.06[95% CI 1.05–1.07], p for trend <0.001) (Table 2B;Figure 2B). When analyzed categorically, patients from themost rural counties experienced 32% higher odds of presenting in status epilepticus compared with patients from themost urban counties (OR 1.32 [95% CI 1.24–1.41], p <0.001). In the subanalysis of patients with private insurance(eTable 3B), the association between rurality and presentingin status epilepticus was no longer observed (OR 1.27 [95%CI 0.98–1.63], p for trend = 0.068). In the subanalysis ofpatients admitted to urban teaching hospitals (eTable 4B),increasing rurality continued to be associated with higherodds of presenting in status epilepticus (OR 1.06 [95% CI1.05–1.08], p for trend <0.001). For prolonged length of stay, a test for trend was not performed because of evidence of nonlinearity. When analyzedcategorically, patients from the most rural counties experienced 29% higher odds of a prolonged stay compared withpatients from the most urban counties (OR 1.29 [95% CI1.19–1.41], p < 0.001) (Table 2C; Figure 2C). Subanalysis ofpatients presenting in status epilepticus (eTable 2B) revealeda similar association, with patients from the most ruralcounties experiencing 22% higher odds of a prolonged staycompared with patients from the most urban counties (OR1.22 [95% CI 1.07–1.39], p = 0.002). In the subanalysis ofpatients with private insurance (eTable 3C), patients from themost rural counties were no longer more likely to experiencea prolonged length of stay (OR 0.90 [95% CI 0.72–1.12], p =0.336). In the subanalysis of patients admitted to urban Neurology | Volume 107, Number 1 | July 14, 2026 e218053(4) ### Page 5 NCHS rurality level | Variable | 1 (least rural) | 2 | 3 | 4 | 5 | 6 (most rural) | Total | p Value | | - | - | - | - | - | - | - | - | - | | Cohort size, n (%) | 295,620 (35.1) | 204,750 (24.3) | 170,085 (20.2) | 71,400 (8.5) | 59,260 (7.0) | 40,330 (4.8) | 841,445 (100.0) | | Age, y, median (IQR) | 55 (37–67) | 56 (39–69) | 56 (39–68) | 56 (40–69) | 56 (40–69) | 58 (42–70) | 56 (39–68) | <0.001 | | Sex, n (%) | | Male | 160,115 (54.2) | 105,455 (51.5) | 89,350 (52.5) | 37,060 (51.9) | 31,070 (52.4) | 20,845 (51.7) | 443,895 (52.8) | <0.001 | | Female | 135,505 (45.8) | 99,295 (48.5) | 80,735 (47.5) | 34,340 (48.1) | 28,190 (47.6) | 19,485 (48.3) | 397,550 (47.2) | | Race or ethnicity, n(%) | | Asian or PacificIslander | 7,175 (2.4) | 2,995 (1.5) | 2,160 (1.3) | 445 (0.6) | 390 (0.7) | 40 (0.1) | 13,205 (1.6) | <0.001 | | Black | 106,925 (36.2) | 46,600 (22.8) | 36,210 (21.3) | 12,865 (18.0) | 8,800 (14.8) | 6,460 (16.0) | 217,860 (25.9) | | Hispanic | 51,150 (17.3) | 16,375 (8.0) | 18,870 (11.1) | 4,205 (5.9) | 2,750 (4.6) | 1,250 (3.1) | 94,600 (11.2) | | Native American | 1,075 (0.4) | 475 (0.2) | 1,425 (0.8) | 960 (1.3) | 1,230 (2.1) | 1,120 (2.8) | 6,285 (0.7) | | Other | 14,815 (5.0) | 6,050 (3.0) | 3,000 (1.8) | 1,425 (2.0) | 900 (1.5) | 590 (1.5) | 26,780 (3.2) | | White | 114,480 (38.7) | 132,255 (64.6) | 108,420 (63.7) | 51,500 (72.1) | 45,190 (76.3) | 30,870 (76.5) | 482,715 (57.4) | | Insurance payer, n(%) | | Medicare | 126,680 (42.9) | 99,010 (48.4) | 80,685 (47.4) | 35,480 (49.7) | 30,175 (50.9) | 20,970 (52.0) | 393,000 (46.7) | <0.001 | | Medicaid | 92,940 (31.4) | 43,220 (21.1) | 41,160 (24.2) | 16,475 (23.1) | 13,185 (22.2) | 8,805 (21.8) | 215,785 (25.6) | | Private insurance | 49,820 (16.9) | 44,105 (21.5) | 31,050 (18.3) | 12,230 (17.1) | 10,045 (17.0) | 6,585 (16.3) | 153,835 (18.3) | | Self-pay | 17,965 (6.1) | 11,255 (5.5) | 10,435 (6.1) | 4,075 (5.7) | 3,515 (5.9) | 2,480 (6.1) | 49,725 (5.9) | | No charge | 1,760 (0.6) | 1,385 (0.7) | 930 (0.5) | 330 (0.5) | 185 (0.3) | 115 (0.3) | 4,705 (0.6) | | Other | 6,455 (2.2) | 5,775 (2.8) | 5,825 (3.4) | 2,810 (3.9) | 2,155 (3.6) | 1,375 (3.4) | 24,395 (2.9) | | ZIP code incomequartile, n (%) | | 1 | 117,420 (39.7) | 31,500 (15.4) | 64,855 (38.1) | 28,290 (39.6) | 32,005 (54.0) | 26,920 (66.7) | 300,990 (35.8) | <0.001 | | 2 | 62,180 (21.0) | 41,690 (20.4) | 48,905 (28.8) | 26,550 (37.2) | 20,325 (34.3) | 10,725 (26.6) | 210,375 (25.0) | | 3 | 63,270 (21.4) | 57,740 (28.2) | 39,340 (23.1) | 14,080 (19.7) | 6,155 (10.4) | 2,460 (6.1) | 183,045 (21.8) | | 4 | 52,750 (17.8) | 73,820 (36.1) | 16,985 (10.0) | 2,480 (3.5) | 775 (1.3) | 225 (0.6) | 147,035 (17.5) | | Bed size of hospital,n (%) | | Small | 50,245 (17.0) | 46,110 (22.5) | 25,770 (15.2) | 14,425 (20.2) | 4,805 (8.1) | 6,180 (15.3) | 147,535 (17.5) | <0.001 | | Medium | 92,460 (31.3) | 65,585 (32.0) | 46,810 (27.5) | 21,475 (30.1) | 12,150 (20.5) | 10,550 (26.2) | 249,030 (29.6) | | Large | 152,915 (51.7) | 93,055 (45.4) | 97,505 (57.3) | 35,500 (49.7) | 42,305 (71.4) | 23,600 (58.5) | 444,880 (52.9) | | Hospital location/teaching status,n (%) | | Rural | 325 (0.1) | 735 (0.4) | 1,120 (0.7) | 970 (1.4) | 26,870 (45.3) | 12,255 (30.4) | 42,275 (5.0) | <0.001 | | Urban | 43,330 (14.7) | 47,990 (23.4) | 34,060 (20.0) | 21,240 (29.7) | 3,845 (6.5) | 4,295 (10.6) | 154,760 (18.4) | | nonteaching | | Urban teaching | 251,965 (85.2) | 156,025 (76.2) | 134,905 (79.3) | 49,190 (68.9) | 28,545 (48.2) | 23,780 (59.0) | 644,410 (76.6) | Continued Neurology | Volume 107, Number 1 | July 14, 2026 e218053(5) ### Page 6 NCHS rurality level | Variable | 1 (least rural) | 2 | 3 | 4 | 5 | 6 (most rural) | Total | p Value | | - | - | - | - | - | - | - | - | - | | Hospital ownership,n (%) | | Government, | 35,585 (12.0) | 16,120 (7.9) | 21,800 (12.8) | 8,305 (11.6) | 10,140 (17.1) | 7,275 (18.0) | 99,225 (11.8) | <0.001 | | nonfederal | | Private, not-forprofit | 201,775 (68.3) | 158,220 (77.3) | 121,555 (71.5) | 54,360 (76.1) | 41,435 (69.9) | 27,610 (68.5) | 604,955 (71.9) | | Private, investorowned | 58,260 (19.7) | 30,410 (14.9) | 26,730 (15.7) | 8,735 (12.2) | 7,685 (13.0) | 5,445 (13.5) | 137,265 (16.3) | | Census region ofhospital, n (%) | | Northeast | 69,360 (23.5) | 60,225 (29.4) | 30,390 (17.9) | 8,515 (11.9) | 7,565 (12.8) | 2,850 (7.1) | 178,905 (21.3) | <0.001 | | Midwest | 57,710 (19.5) | 42,605 (20.8) | 27,695 (16.3) | 19,220 (26.9) | 16,840 (28.4) | 9,925 (24.6) | 173,995 (20.7) | | South | 91,425 (30.9) | 81,490 (39.8) | 81,810 (48.1) | 31,825 (44.6) | 28,245 (47.7) | 24,450 (60.6) | 339,245 (40.3) | | West | 77,125 (26.1) | 20,430 (10.0) | 30,190 (17.7) | 11,840 (16.6) | 6,610 (11.2) | 3,105 (7.7) | 149,300 (17.7) | | In-hospitalmortality, n (%) | | Survived | 292,590 (99.0) | 202,420 (98.9) | 167,840 (98.7) | 70,425 (98.6) | 58,215 (98.2) | 39,665 (98.4) | 831,155 (98.8) | <0.001 | | Died | 3,030 (1.0) | 2,330 (1.1) | 2,245 (1.3) | 975 (1.4) | 1,045 (1.8) | 665 (1.6) | 10,290 (1.2) | | Status epilepticus, n(%) | | No statusepilepticus | 231,640 (78.4) | 161,040 (78.7) | 129,660 (76.2) | 55,060 (77.1) | 45,730 (77.2) | 30,845 (76.5) | 653,975 (77.7) | <0.001 | | Status epilepticus | 63,980 (21.6) | 43,710 (21.3) | 40,425 (23.8) | 16,340 (22.9) | 13,530 (22.8) | 9,485 (23.5) | 187,470 (22.3) | | Dischargedisposition, n (%) | | Routine | 172,810 (58.5) | 118,110 (57.7) | 99,665 (58.6) | 42,500 (59.5) | 34,245 (57.8) | 23,515 (58.3) | 490,845 (58.3) | 0.014 | | Nonroutine | 122,810 (41.5) | 86,640 (42.3) | 70,420 (41.4) | 28,900 (40.5) | 25,015 (42.2) | 16,815 (41.7) | 350,600 (41.7) | | Length of stay, n (%) | | ≤7 d | 257,145 (87.0) | 178,150 (87.0) | 147,385 (86.7) | 62,340 (87.3) | 51,605 (87.1) | 34,855 (86.4) | 731,480 (86.9) | 0.371 | | >7 d | 38,475 (13.0) | 26,600 (13.0) | 22,700 (13.3) | 9,060 (12.7) | 7,655 (12.9) | 5,475 (13.6) | 109,965 (13.1) | | EEG, n (%) | | No EEG | 248,790 (84.2) | 172,785 (84.4) | 148,935 (87.6) | 65,025 (91.1) | 53,045 (89.5) | 36,480 (90.5) | 725,060 (86.2) | <0.001 | | EEG | 46,830 (15.8) | 31,965 (15.6) | 21,150 (12.4) | 6,375 (8.9) | 6,215 (10.5) | 3,850 (9.5) | 116,385 (13.8) | Abbreviations: IQR = interquartile range; NCHS = National Center for Health Statistics; NIS = National Inpatient Sample.Full comorbidity data are available in eTable 1. teaching hospitals (eTable 4C), patients from the most ruralcounties continued to be more likely to have a prolongedlength of stay (OR 1.26 [95% CI 1.15–1.39], p < 0.001). For nonroutine discharge (Table 3A), increasing rurality wasassociated with lower odds of nonroutine discharge (OR 0.98[95% CI 0.97–0.99], p for trend <0.001). When analyzedcategorically, patients from the most rural counties experienced 10% lower odds of nonroutine discharge compared with patients from the most urban counties (OR 0.90[95% CI 0.85–0.96], p = 0.001). Subanalysis of patientspresenting in status epilepticus (eTable 2C) did not reveala significant trend (OR 0.98 [95% CI 0.97–1.00], p = 0.110).In the subanalysis of patients with private insurance(eTable 3D), the association between rurality and nonroutinedischarge continued to be observed (OR 0.98 [95% CI Neurology | Volume 107, Number 1 | July 14, 2026 e218053(6) ### Page 7 | A: In-hospital mortality | B: Status epilepticus at presentation | C: Prolonged length of stay | | - | - | - | | p | p | p | | NCHS rurality level | OR (95% CI) | Value | NCHS rurality level | OR (95% CI) | Value | NCHS rurality level | OR (95% CI) | Value | | 1 (least rural;reference) | 1 | 1 (least rural;reference) | 1 | 1 (least rural;reference) | 1 | | 2 1.08(0.95–1.24) | 0.249 | 2 | 1.09 | <0.001 | 2 | 1.05 | 0.037 | | (1.05–1.14) | (1.00–1.10) | | 3 1.27(1.11–1.45) | <0.001 | 3 | 1.16 | <0.001 | 3 | 1.04 | 0.137 | | (1.11–1.21) | (0.99–1.09) | | 4 1.43(1.20–1.71) | <0.001 | 4 | 1.16 | <0.001 | 4 | 1.09 | 0.011 | | (1.10–1.22) | (1.02–1.16) | | 5 2.22(1.83–2.70) | <0.001 | 5 | 1.34 | <0.001 | 5 | 1.35 | <0.001 | | (1.26–1.43) | (1.25–1.46) | | 6 (most rural) | 1.93 | <0.001 | 6 (most rural) | 1.32 | <0.001 | 6 (most rural) | 1.29 | <0.001 | | (1.56–2.39) | (1.24–1.41) | (1.19–1.41) | | Trend | 1.16 | <0.001 | Trend | 1.06 | <0.001 | Trend | N/A | N/A | | (1.12–1.20) | (1.05–1.07) | Abbreviations: NCHS = National Center for Health Statistics; OR = odds ratio. Logistic regression models of the effect of rurality on in-hospital mortality, presenting in status epilepticus, and prolonged length of stay in nationwideepilepsy admissions in the US cohort drawn from the National Inpatient Sample, years 2016–2021. The Box-Tidwell test for prolonged length of stay foundevidence of significant nonlinearity: accordingly, a trend analysis is not reported. All models adjusted for demographics, comorbidities, and hospitalcharacteristics (in Methods). 0.97–0.99], p for trend <0.001). In the subanalysis of patientsadmitted to urban teaching hospitals (eTable 4D), increasingrurality continued to be associated with lower odds of nonroutine discharge (OR 0.98 [95% CI 0.97–0.99], p for trend =0.004). For EEG, tests for trend were not performed because of evidence of nonlinearity. When analyzed categorically, patientsfrom the most rural counties experienced 12% lower odds ofreceiving EEG compared with patients from the most urbancounties (OR 0.88 [95% CI 0.77–1.00], p = 0.047). Subanalysis of patients presenting in status epilepticus(eTable 2D) revealed a similar association, with patients fromthe most rural counties experiencing 18% lower odds of receiving EEG compared with patients from the most urbancounties (OR 0.82 [95% CI 0.68–0.99], p = 0.040). In thesubanalysis of patients with private insurance (eTable 3E),patients from the most rural counties similarly had lower oddsof receiving EEG (OR 0.88 [95% CI 0.77–1.00], p = 0.047).In the subanalysis of patients admitted to urban teachinghospitals (eTable 4E), patients from the most rural countiesdid not have lower odds of receiving EEG (OR 0.89 [95% CI0.78–1.02], p = 0.098). ## Discussion In this study, we found that increasing rurality was independently associated with worse epilepsy outcomes, mostnotably with nearly double the odds of in-hospital mortality,compared with those from the most urban counties. Rurality was also linked to a higher likelihood of presenting in statusepilepticus and a prolonged hospital stay. By contrast, ruralitywas associated with lower odds of nonroutine discharge andreceipt of EEG. These associations remained when subanalyzing patients presenting in status epilepticus. Notably,the associations between mortality, presenting in status epilepticus, and prolonged length of stay were attenuated inpatients with private insurance. Several mechanisms may explain why rurality is associatedwith worse epilepsy outcomes. One key factor is limited seizure control. Because of their remote location, rural patientsmay have less frequent access to neurologists, leading tosuboptimal medication management, and medication accessitself is also more difficult, as many people with epilepsy areunable to drive and transportation barriers are frequentlycited as a reason for poor medication adherence.29,30 Accordingly, rural people with epilepsy may experience moresevere baseline seizures and an increased risk of sudden unexpected death in epilepsy.31 These challenges are furthercompounded by a scarcity of nearby hospitals, the concentration of comprehensive epilepsy centers in metropolitanregions,7 and limited access to surgical procedures.18 Increasing rurality may also negatively affect acute seizurecare. For status epilepticus, poor outcomes have previouslybeen linked to delayed hospital arrival and limited access toEEG.32-34 In our study, rurality was associated with reducedodds of receiving EEG, a gap that may contribute to diagnosticdelays and increased risk of status epilepticus–related Neurology | Volume 107, Number 1 | July 14, 2026 e218053(7) ### Page 8 ![Figure]() Forest plots of adjusted ORs and95% CIs of in-hospital mortality (A),presentation in status epilepticus(B), and prolonged length of stay(C) in nationwide epilepsy admissions in the United States, bypatients’ rurality. As rurality increased, adjusted odds of inhospital mortality (OR 1.16 [95%CI 1.12–1.20], p for trend <0.001)and status epilepticus at presentation (OR 1.06 [95% CI1.05–1.07], p for trend <0.001) increased. A trend test was not performed for length of stay becauseof evidence of significant nonlinearity. Relative to patients fromthe most urban counties, patientsfrom the most rural counties hadincreased odds of in-hospitalmortality (OR 1.93 [95% CI1.56–2.39], p < 0.001), status epilepticus at presentation (OR 1.32[95% CI 1.24–1.41], p < 0.001), andprolonged length of stay (OR 1.29[95% CI 1.19–1.41], p < 0.001). Allmodels were adjusted for age,race or ethnicity, primary expected payer, smoking status, Elixhauser comorbidities, hospitalsize (bed count), hospital teachingstatus, hospital control/ownership, census region of hospital, median household incomequartile for patients’ ZIP code, andrurality. NCHS = National Centerfor Health Statistics; OR = oddsratio. | A: Nonroutine discharge | B: Odds of receiving EEG | | - | - | | NCHS rurality level | OR (95% CI) | p Value | NCHS rurality level | OR (95% CI) | p Value | | 1 (least rural; reference) | 1 | 1 (least rural; reference) | 1 | | 2 | 0.96 (0.93–1.00) | 0.028 | 2 | 0.98 (0.91–1.06) | 0.691 | | 3 | 0.97 (0.94–1.01) | 0.112 | 3 | 0.80 (0.71–0.89) | <0.001 | | 4 | 0.89 (0.85–0.94) | <0.001 | 4 | 0.69 (0.61–0.77) | <0.001 | | 5 | 0.94 (0.89–0.99) | 0.023 | 5 | 1.02 (0.90–1.14) | 0.782 | | 6 (most rural) | 0.90 (0.85–0.96) | 0.001 | 6 (most rural) | 0.88 (0.77–1.00) | 0.047 | | Trend | 0.98 (0.97–0.99) | <0.001 | Trend | N/A | N/A | Abbreviations: NCHS = National Center for Health Statistics; OR = odds ratio.The Box-Tidwell test for receipt of EEG found evidence of significant nonlinearity: accordingly, a trend analysis is not reported. All models adjusted fordemographics, comorbidities, and hospital characteristics (in Methods). Neurology | Volume 107, Number 1 | July 14, 2026 e218053(8) ### Page 9 mortality. Previous work has also demonstrated that longertime to treatment is associated with prolonged status epilepticus.35 Given that many patients with epilepsy do notreceive prehospital treatment36 and rural patients face longertransport times, strengthening emergency medical services tofacilitate earlier seizure recognition and timely medicationdelivery may be important for improving epilepsy outcomes. In the subanalysis of those patients with private insurance, theassociations between rurality and in-hospital mortality, presenting in status epilepticus, and prolonged length of staywere no longer observed. Previous studies have shown thatadults with active epilepsy are more likely to be insured underMedicaid and less likely to have private insurance.37,38 Inaddition, the availability of epilepsy centers did not influenceaccess to specialized care in those with private insurance,39and patients with private insurance are more likely to experience shorter wait times and to undergo epilepsy surgery.40Taken together, these findings suggest that private insurancemay mitigate some of the structural barriers faced by ruralpatients, thereby attenuating the observed rural-urban disparities in epilepsy outcomes. Increasing rurality was also associated with a prolonged lengthof stay. Rural patients may experience delayed access to care,extending hospitalization. These patients may also presentlater in the disease course (e.g., in status epilepticus or uncontrolled seizures), further prolonging their stay. Despitegreater odds of prolonged length of stay, rural patients hadlower odds of nonroutine discharge, which may reflect limitedavailability of postacute care facilities in rural areas,41 resultingin more patients being discharged home rather than to rehabilitation or skilled nursing facilities. A higher proportion ofrural patients being uninsured or underinsured may also underlie this association,42 as these patients have less access torehabilitation facilities. Our findings of worse epilepsy outcomes with increasing rurality mirror those in a recent study, which found that EEGwas less likely to be performed in rural hospitals amongpatients presenting in status epilepticus.6 Notably, usinghospital classification as a proxy for patient rurality maymisclassify patients because many rural residents receive carein urban hospitals, as was observed in our cohort. While weobserved that rural patients admitted to urban teaching hospitals were not more likely to have a nonroutine discharge orreceive EEG than their most urban counterparts, we nevertheless found that they continued to exhibit increased odds ofmortality, presenting in status epilepticus, and a prolongedlength of stay: this suggests that the deleterious effects ofrurality persist regardless of the hospital type patients areadmitted to. A 2021 systematic review found only a smallnumber of studies investigating rurality and epilepsy, withinconsistent reporting of outcomes and with overall mixedresults.43 Of the 3 studies exploring epilepsy outcomes in theUnited States, each was conducted at the state level and included varying definitions of rurality, limiting comparability.44-46 Future studies should use standardizedrurality measures to accurately quantify disparities in epilepsycare. Exploratory subanalyses examining epilepsy etiologysuggested that rural-urban disparities in mortality were present among patients with poststroke epilepsy, but not amongthose with tumor-associated epilepsy. The absence of a similarassociation in tumor-associated epilepsy may reflect thegenerally poor prognosis associated with intracranial malignancies, potentially masking the effect of rurality on mortality.However, the NIS lacks detailed etiology data, which preventsdefinitive etiologic classification. These analyses are, therefore, exploratory and should be interpreted cautiously, particularly given the limited sample sizes within etiologicsubgroups. Broadly, our findings should be interpreted within the contextof a nationwide increase in demand for neurologic carealongside a growing shortage of neurologists.47 On average, 1in 5 Medicare recipients must travel 50 or more miles toreceive neurologic care,48 a challenge exacerbated by theclosure of 153 rural hospitals since 2010.49 As a result, peoplewith epilepsy residing in rural counties may be disproportionately affected by accessibility barriers. Ultimately, there isa critical need for targeted interventions to improve epilepsyoutcomes in rural populations. To expand access, some providers have reported success using telehealth visits for epilepsy care,50 which may improve seizure control andoutcomes. Other important strategies include expandingrapid or conventional EEG availability in rural hospitals,51increasing epilepsy-specific training among providers,52 andfacilitating medication access through mail-order pharmacyservices. Expanding access to comprehensive epilepsy centers7 and epilepsy surgery18 for rural populations may furtherimprove outcomes. Future research should identify whichrural subpopulations are at highest risk, examining factorssuch as insurance type and access to specialized epilepsyservices. Our study has several limitations. Inherent to any observational study design is the potential for latent confounding, andthere were several factors we were unable to control for in ouranalyses. Notably, although we were able to adjust for presence of status epilepticus, we were unable to adjust for otherseizure severity measures such as seizure frequency, subtype,and duration. In addition, we could not account for emergency medical services availability, travel time/time to presentation, and geographic distance to hospitals and epilepsycenters.48 Future studies should incorporate more preciseseverity and accessibility measures. Second, the NIS onlyincludes patients admitted to hospitals in the United States,limiting generalizability to other populations and countrieswith different health care systems. We also note that, owing tothe structure of the NIS’s sex and race/ethnicity variables,values are provided by hospitals and, therefore, exact reporting practices may vary; in addition, race/ethnicity data in theNIS are collapsed into 1 variable, limiting the ability to disambiguate racial/ethnic differences in this study. Notably, our Neurology | Volume 107, Number 1 | July 14, 2026 e218053(9) ### Page 10 cohort (2016–2021) largely predates the widespread expansion of telehealth services after the coronavirus disease2019 pandemic: it may be that the adoption of telehealthfor epilepsy care can help mitigate some of the observedrural-urban disparities by addressing transportation barriers and the shortage of local epileptologists.53,54 Third,our analysis was limited to hospitalizations, preventingassessment of longitudinal outcomes such as sudden unexpected death in epilepsy, status epilepticus prevalence, orrehospitalization rates. It is also possible that a subset ofrural patients may be entirely unable to access inpatientcare—for example, because of socioeconomic barriers orinadequate transportation—representing a particularly vulnerable population that our study cannot capture. Consequently, the absence of outpatient outcome data may lead toan underestimation of the true magnitude of rural-urbandisparities in epilepsy. Finally, because the NIS does notsupport patient-level linkage, it is possible that patients maycontribute multiple hospitalizations, such as in the case ofa rural-to-urban transfer. Nevertheless, regarding our primary outcome of in-hospital mortality, such double countingwould likely result in an underestimate of rural-urban mortality differences because transferred patients have necessarily survived their initial hospitalization. Moreover, ourfindings were broadly unchanged in subanalyses restricted toadmissions to urban teaching hospitals, suggesting that ruralto-urban transfers do not meaningfully drive the observedrural-urban disparities. Among patients with epilepsy, rural residence was independently associated with higher in-hospital mortality,greater likelihood of status epilepticus at presentation, andhigher odds of prolonged length of stay. Reduced access toEEG may also contribute to poor outcomes. These findingshighlight an important health care disparity, with significantpublic health implications. Strengthening rural health infrastructure for people with epilepsy is required, includingexpanding telehealth services, improving EEG availability,increasing access to comprehensive epilepsy centers and epilepsy surgery, and enhancing epilepsy training among healthcare providers and emergency medical services. ### Author Contributions E.R. Bader: drafting/revision of the manuscript for content,including medical writing for content; major role in the acquisition of data; study concept or design; statistical analyses;analysis or interpretation of data. W.S. Kemball-Cook:drafting/revision of the manuscript for content, includingmedical writing for content; analysis or interpretation of data.J. Benton: drafting/revision of the manuscript for content,including medical writing for content; analysis or interpretation of data. N.J. Killian: analysis or interpretation ofdata. A. Boro: drafting/revision of the manuscript for content,including medical writing for content; analysis or interpretation of data. E.N. Eskandar: drafting/revision of themanuscript for content, including medical writing for content;analysis or interpretation of data. ### Study Funding E.R. Bader was supported by the Einstein-Montefiore Clinicaland Translational Science Award Hub (1UM1TR004400;MPIs: Keller, Kim) and the Predoctoral T32 at Albert Einstein College of Medicine (T32TR004537; MPIs: Marantz,Hosgood). ### Disclosure The authors report no relevant disclosures. Go to for full disclosures. ### Publication History Received by Neurology® December 17, 2025. 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