Estimating Prevalence, Incidence, and Disease-related Mortality for Patients with Epilepsy in Managed Care Organizations

Validation of ICD-10 codes for studying foreign body airway obstructions: A health administrative data cohort study

Aim

To validate a case definition for foreign body airway obstructions (FBAO) using International Classification of Diseases version 10 (ICD-10) codes to accurately identify patients in administrative health databases and improve reporting on this injury.

Methods

We identified prehospital patient encounters in Alberta, Canada between Jan 1, 2018 and Dec 31, 2021 by querying the provincial emergency medical services' (EMS) patient care records for FBAO-related presentations, EMS protocols, or treatments. We deterministically linked EMS patient encounters to data on emergency department visits and hospital admissions, which included ICD-10 codes. Two physicians independently reviewed encounters to determine true FBAO cases. We then calculated diagnostic accuracy measures (sensitivity, specificity, likelihood ratios) of various algorithms.

Results

We identified 3677 EMS patient encounters, 2121 were linked to hospital administrative databases. Of these encounters, 825 (38.9%) were true FBAO. The combination of two ICD-10 codes (T17 = foreign body in the respiratory tract or T18.0 = foreign body in the mouth) was the most specific algorithm (96.9% [95%CI 95.8-97.8%]), while the combination of all FBAO-related ICD-10 codes and R06.8 (other breathing abnormalities) was the most sensitive (75.0% [95%CI 71.9-78.0]). We identified an additional 453 (35.4%) FBAO cases not transported by EMS (due to death or transport refusal), and therefore not linked to the hospital administrative databases. Of these unlinked encounters, 44 (9.7%) cases resulted in the patient's death.

Conclusions

FBAO can be identified with reasonable accuracy using health administrative data and ICD-10 codes. All algorithms had a trade-off between sensitivity and specificity, and failed to identify a third of FBAO cases, of which 10% resulted in death.

Predictors of referral for long-term EEG monitoring for Medicare beneficiaries with drug-resistant epilepsy

OBJECTIVE

For people with drug-resistant epilepsy, the use of epilepsy surgery is low despite favorable odds of seizure freedom. To better understand surgery utilization, we explored factors associated with inpatient long-term EEG monitoring (LTM), the first step of the presurgical pathway.

METHODS

Using 2001-2018 Medicare files, we identified patients with incident drug-resistant epilepsy using validated criteria of ≥2 distinct antiseizure medication (ASM) prescriptions and ≥1 drug-resistant epilepsy encounter among patients with ≥2 years pre- and ≥1 year post-diagnosis Medicare enrollment. We used multilevel logistic regression to evaluate associations between LTM and patient, provider, and geographic factors. We then analyzed neurologist-diagnosed patients to further evaluate provider/environmental characteristics.

RESULTS

Of 12 044 patients with incident drug-resistant epilepsy diagnosis identified, 2% underwent surgery. Most (68%) were diagnosed by a neurologist. In total, 19% underwent LTM near/after drug-resistant epilepsy diagnosis; another 4% only underwent LTM much prior to diagnosis. Patient factors most strongly predicting LTM were age <65 (adjusted odds ratio 1.5 [95% confidence interval 1.3-1.8]), focal epilepsy (1.6 [1.4-1.9]), psychogenic non-epileptic spells diagnosis (1.6 [1.1-2.5]) prior hospitalization (1.7, [1.5-2]), and epilepsy center proximity (1.6 [1.3-1.9]). Additional predictors included female gender, Medicare/Medicaid non-dual eligibility, certain comorbidities, physician specialties, regional neurologist density, and prior LTM. Among neurologist-diagnosed patients, neurologist <10 years from graduation, near an epilepsy center, or epilepsy-specialized increased LTM likelihood (1.5 [1.3-1.9], 2.1 [1.8-2.5], 2.6 [2.1-3.1], respectively). In this model, 37% of variation in LTM completion near/after diagnosis was explained by individual neurologist practice and/or environment rather than measurable patient factors (intraclass correlation coefficient 0.37).

SIGNIFICANCE

A small proportion of Medicare beneficiaries with drug-resistant epilepsy completed LTM, a proxy for epilepsy surgery referral. While some patient factors and access measures predicted LTM, non-patient factors explained a sizable proportion of variance in LTM completion. To increase surgery utilization, these data suggest initiatives targeting better support of neurologist referral.

Patient navigation in epilepsy care

The concept of patient navigation was first introduced in 1989 by the American Cancer Society and was first implemented in 1990 by Dr. Harold Freeman in Harlem, NY. The role of a patient navigator (PN) is to coordinate care between the care team, the patient, and their family while also providing social support. In the last 30 years, patient navigation in oncological care has expanded internationally and has been shown to significantly improve patient care experience, especially in the United States cancer care system. Like oncology care, patients who require epilepsy care face socioeconomic and healthcare system barriers and are at significant risk of morbidity and mortality if their care needs are not met. Although shortcomings in epilepsy care are longstanding, the COVID-19 pandemic has exacerbated these issues as both patients and providers have reported significant delays in care secondary to the pandemic. Prior to the pandemic, preliminary studies had shown the potential efficacy of patient navigation in improving epilepsy care. Considering the evidence that such programs are helpful for severely disadvantaged cancer patients and in enhancing epilepsy care, we believe that professional societies should support and encourage PN programs for coordinated and comprehensive care for patients with epilepsy.

The effects of a brief memory enhancement course on individuals with epilepsy

PURPOSE

The purpose of the study was to determine whether a brief memory enhancement course in persons with epilepsy (PWE) can improve cognitive abilities, quality of life, self-management, and seizure severity.

METHODS

Thirty-nine PWE completed a 1-hour memory enhancement course. This was preceded by a baseline/preintervention assessment (BA/PRE), followed by postintervention assessment (POST) at 1 & 1/2 to 3 months, and a delayed postintervention assessment evaluation (DPOST) at 4 & 1/2 to 6 months after course completion. In order to assess for retesting bias, an additional 30 PWE underwent a separate BA and PRE.

RESULTS

There was significant improvement on the Patient-Reported Outcomes Patient Information System version 2.0 Cognitive Function Abilities Subset and the Epilepsy Self-Management Scale (ESMS) on both POST and DPOST when compared with BA/PRE. Retesting bias did not occur. On ESMS subscale evaluation, significant improvement occurred on the Lifestyle Management subscale. There was no improvement in quality of life and seizure severity. There was good patient acceptability for the memory program.

CONCLUSION

A brief memory enhancement course results in sustained improvement in cognitive functioning and self-management of PWE.

Current epidemiological and etiological characteristics and treatment of seizures or epilepsy in patients with HIV infection

Seizures or epilepsy is one of the common serious complications in patients with advanced human immunodeficiency virus (HIV) infection or diagnosed with immune deficiency syndrome, with higher incidence and prevalence than in the general population. Generalized seizures are the most common type in the patients. Opportunistic infections are a stereotypical predisposing factor for seizures in HIV patients, but a variety of pathogenic factors can also be found in these patients, such as metabolic perturbation and drug-drug interactions. The diagnostic criteria for seizures in these patients are the same as those in the general population. As HIV patients with seizures need to take both antivirals and antiepileptic drugs, the risk of drug-drug interactions is greatly increased, and the side effects of drugs may also become more prominent. At present, most experience in antiepileptic drug usage has come from the general population, and there is still a lack of guidance of antiepileptic drug use in special groups such as the HIV-infected people. Unlike the old-generation drugs that involve metabolisms through CYP450, the first-line antiepileptic drugs usually bypass CYP450, thus having less drug-drug interactions. In this review, we summarize the recent research progress on the above-mentioned widely discussed topics and make a prospect on future research direction.

Using ICD-10 codes to identify elective epilepsy monitoring unit admissions from administrative billing data: A validation study

Video-electroencephalogram (EEG) monitoring in the epilepsy monitoring unit (EMU) is essential for managing epilepsy and seizure mimics. Evaluation of care in the EMU would benefit from a validated code set capable of identifying EMU admissions from administrative databases comprised of large, diverse cohorts. We assessed the ability of code-based queries to parse EMU admissions from administrative billing records in a large academic medical center over a four-year period, 2016-2019. We applied prespecified queries for admissions coded as follows: 1) elective, 2) receiving video-EEG monitoring, and 3) including diagnoses typically required by major US healthcare payers for EMU admission. Sensitivity (Sn), specificity (Sp), and predictive value positive/negative (PVP, PVN) were determined. Two approaches were highly effective. Incorporating epilepsy, seizure, or seizure mimic codes as the admitting diagnosis (assigned at admission; Sn 96.3%, Sp 100.0%, PVP 98.3%, and PVN 100.0%) or the principal diagnosis (assigned after discharge; Sn 94.9%, Sp 100.0%, PVP 98.8%, and PVN 100.0%) identified elective adult EMU admissions with comparable reliability (p = 0.096). The addition of surgical procedure codes further separated EMU admissions for intracranial EEG monitoring. When applied to larger, more comprehensive datasets, these code-based queries should enhance our understanding of EMU utilization and access to care on a scalable basis.

CDC-supported epilepsy surveillance and epidemiologic studies: A review of progress since 1994

To report progress, to identify gaps, and to plan epilepsy surveillance and research activities more effectively, the Centers for Disease Control and Prevention (CDC) Epilepsy Program has summarized findings from selected CDC-supported surveillance and epidemiologic studies about epilepsy from 1994 through 2019. We identified publications supported by CDC funding and publications conducted by the CDC Epilepsy Program alone or with partners. We included only epilepsy surveillance and epidemiologic studies focusing on epilepsy burden, epilepsy-related outcomes, and healthcare utilization. We describe the findings of these studies in the following order: 1)prevalence; 2)incidence; 3)epilepsy-related outcomes by selected demographic characteristics; 4)cysticercosis or neurocysticercosis (NCC); 5)traumatic brain injury (TBI); 6)comorbidity; 7)mortality; 8)access to care; 9)quality of care; and 10) cost. We have characterized these findings in relation to the scope of the first three domains of the 2012 Institute of Medicine report on epilepsy and its relevant first four recommendations. From 1994 through 2019, 76 publications on epilepsy-related epidemiologic and surveillance studies were identified. Over the past 25 years, CDC has expanded community, state, and national surveillance on epilepsy and supported epidemiologic studies by using multiple assessment methods and validated case-ascertainment criteria to identify epilepsy burden, epilepsy-related outcomes, and healthcare utilization in the general population or in population subgroups. Among identified research opportunities, studies on epilepsy incidence and risk factors, mortality, and cost are considered as important surveillance gaps. Other remaining gaps and suggested surveillance strategies are also proposed. Findings from this review may help epilepsy researchers and other stakeholders reference and prioritize future activities for epidemiologic and surveillance studies in epilepsy.

Antiepileptic Drug Treatment Patterns in Women of Childbearing Age With Epilepsy

Importance

Limited population-based data are available on antiepileptic drug (AED) treatment patterns in women of childbearing age with epilepsy; the current population risk is not clear.

Objectives

To examine the AED treatment patterns and identify differences in use of valproate sodium and topiramate by comorbidities among women of childbearing age with epilepsy.

Design, Setting, and Participants

A retrospective cohort study used a nationwide commercial database and supplemental Medicare as well as Medicaid insurance claims data to identify 46 767 women with epilepsy aged 15 to 44 years. The eligible study cohort was enrolled between January 1, 2009, and December 31, 2013. Data analysis was conducted from January 1, 2017, to February 22, 2018.

Exposures

Cases required an International Classification of Diseases, Ninth Revision, Clinical Modification-coded epilepsy diagnosis with continuous medical and pharmacy enrollment. Incident cases required a baseline of 2 or more years without an epilepsy diagnosis or AED prescription before the index date. For both incident and prevalent cases, focal and generalized epilepsy cohorts were matched by age, payer type, and enrollment period and then compared.

Main Outcomes and Measures

Antiepileptic drug treatment pattern according to seizure type and comorbidities.

Results

Of the 46 767 patients identified, there were 8003 incident cases (mean [SD] age, 27.3 [9.4] years) and 38 764 prevalent cases (mean [SD] age, 29.7 [9.0] years). Among 3219 women in the incident epilepsy group who received AEDs for 90 days or more, 3173 (98.6%) received monotherapy as first-line treatment; among 28 239 treated prevalent cases, 18 987 (67.2%) received monotherapy. In 3544 (44.3%) incident cases and 9480 (24.5%) prevalent cases, AED treatment was not documented during 180 days or more of follow-up after diagnosis. Valproate (incident: 35 [5.81%]; prevalent: 514 [13.1%]) and phenytoin (incident: 33 [5.48%]; prevalent: 178 [4.53%]) were more commonly used for generalized epilepsy and oxcarbazepine (incident: 53 [8.03%]; prevalent: 386 [9.89%]) was more often used for focal epilepsy. Levetiracetam (incident: focal, 267 [40.5%]; generalized, 271 [45.0%]; prevalent: focal, 794 [20.3%]; generalized, 871 [22.2%]), lamotrigine (incident: focal, 123 [18.6%]; generalized, 106 [17.6%]; prevalent: focal, 968 [24.8%]; generalized, 871 [22.2%]), and topiramate (incident: focal, 102 [15.5%]; generalized, 64 [10.6%]; prevalent: focal, 499 [12.8%]; generalized, 470 [12.0%]) were leading AEDs prescribed for both focal and generalized epilepsy. Valproate was more commonly prescribed for women with comorbid headache or migraine (incident: 53 of 1251 [4.2%]; prevalent: 839 of 8046 [10.4%]), mood disorder (incident: 63 of 860 [7.3%]; prevalent: 1110 of 6995 [15.9%]), and anxiety and dissociative disorders (incident: 57 of 881 [6.5%]; prevalent: 798 of 5912 [13.5%]). Topiramate was more likely prescribed for those with comorbid headache or migraine (incident: 335 of 1251 [26.8%]; prevalent: 2322 of 8046 [28.9%]).

Conclusions and Relevance

Many women appear to be treated with valproate and topiramate despite known teratogenicity risks. Comorbidities may affect selecting certain AEDs despite their teratogenicity risks.

Estimation of the epileptogenic-zone with HFO sub-groups exhibiting various levels of epileptogenicity

High-Frequency-Oscillations (HFO) are biomarkers of the epileptogenic-zone (EZ) and thus a potential aid in guiding epilepsy-surgery. HFO are normally sub-divided according to their oscillating-frequency into Ripples (80-250 Hz) and Fast-Ripples (FR) (250-500 Hz) and are known to also occur in the non-epileptic brain. We address two challenges faced by HFO: firstly, estimating the margins of the EZ using the HFO occurrence-rate from each intracranial EEG channel; secondly, selecting HFO sub-groups with a higher probability of being purely epileptic. We propose the clustering of channels with high HFO occurrence-rates as a deterministic method to delimit the EZ. Additionally, we perform the EZ estimation using 9 sub-groups of HFO; these sub-groups are determined by their temporal and spatial coincidence with intracranial interictal-epileptic-spikes (IES) and are assumed to have varying levels of epileptogenicity. The EZ estimated with the different HFO-sub-groups are compared between themselves and with a proxy of the factually undefinable EZ, namely the resected-volume (RV). The proposed clustering method proved to be deterministic and allowed estimating the EZ for each patient and each HFO-sub-group. Those Ripples assumed to be more epileptogenic occurred in lower numbers than all Ripples but showed the highest correspondence with the RV. All FR sub-groups showed a high specificity to the RV. The proposed clustering method successfully extracted the information from the HFO occurrence-rate to estimate the EZ. The selection of more epileptogenic HFO based on their coincidence with IES proved to be of value for both Ripples and FR.

Automatic detection of high-frequency-oscillations and their sub-groups co-occurring with interictal-epileptic-spikes

OBJECTIVE

High-frequency-oscillations (HFO) and interictal-epileptic-spikes (IES) are spatial biomarkers of the epileptogenic-zone. Those HFO spatially and temporally co-occurring with IES (IES-HFO) are potentially superior biomarkers, their use is however challenged by the difficulty in detecting the low amplitude HFO riding the high-amplitude and steep-waveform of IES. We aim to develop an automatic HFO detector with an improved performance with respect to current methods and that also correctly distinguishes IES-HFO from IES occurring in isolation (isol-IES). We also aim to validate the biomarker-value of the automatic detections by utilizing them to localize a surrogate of the epileptogenic-zone.

APPROACH

We developed automatic-detectors of HFO-Ripples (80-250 Hz), HFO-FastRipples (250-500 Hz) and IES based on kernelized support-vector-machines (SVM). The training of the HFO-detectors was based on visually marked HFO and the parameter optimization during this training-process promoted the correct discernment between IES-HFO and isol-IES. Both HFO-detectors were benchmarked against other published detectors using databases with both visually marked and simulated gold-standards. The IES-detector was trained with a publicly available simulated database and tested against both simulated and visually marked gold-standards. To validate the detections' biomarker-value, the detectors were run on intracranial-EEGs from 8 patients and each with durations of 2-3 days, the detections' cumulated per-channel occurrence-rate was then used to predict the seizure-onset-zone as a surrogate of the epileptogenic-zone.

MAIN RESULTS

The HFO-detectors obtained at least 21 more F1-score points than previously published algorithms at the lowest signal-to-noise-ratio. The success achieved when discerning between IES-HFO and isol-IES was comparable to that of other published algorithms. The automatically detected IES-HFO and IES-Ripples showed the best biomarker-value to localize the epileptogenic-zone.

SIGNIFICANCE

The developed detectors are publicly available and provide a reliable tool to further study HFO, IES-HFO and their value as biomarkers. The putative higher biomarker value from IES-HFO was validated on automatically-detected, long-term data.

Epilepsy Among Elderly Medicare Beneficiaries: A Validated Approach to Identify Prevalent and Incident Epilepsy

BACKGROUND

Uncertain validity of epilepsy diagnoses within health insurance claims and other large datasets have hindered efforts to study and monitor care at the population level.

OBJECTIVES

To develop and validate prediction models using longitudinal Medicare administrative data to identify patients with actual epilepsy among those with the diagnosis.

RESEARCH DESIGN, SUBJECTS, MEASURES

We used linked electronic health records and Medicare administrative data including claims to predict epilepsy status. A neurologist reviewed electronic health record data to assess epilepsy status in a stratified random sample of Medicare beneficiaries aged 65+ years between January 2012 and December 2014. We then reconstructed the full sample using inverse probability sampling weights. We developed prediction models using longitudinal Medicare data, then in a separate sample evaluated the predictive performance of each model, for example, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.

RESULTS

Of 20,945 patients in the reconstructed sample, 2.1% had confirmed epilepsy. The best-performing prediction model to identify prevalent epilepsy required epilepsy diagnoses with multiple claims at least 60 days apart, and epilepsy-specific drug claims: AUROC=0.93 [95% confidence interval (CI), 0.90-0.96], and with an 80% diagnostic threshold, sensitivity=87.8% (95% CI, 80.4%-93.2%), specificity=98.4% (95% CI, 98.2%-98.5%). A similar model also performed well in predicting incident epilepsy (k=0.79; 95% CI, 0.66-0.92).

CONCLUSIONS

Prediction models using longitudinal Medicare data perform well in predicting incident and prevalent epilepsy status accurately.

Prevalence and incidence of new-onset seizures and epilepsy in patients with human immunodeficiency virus (HIV): Systematic review and meta-analysis

BACKGROUND

The prevalence and incidence of seizures are substantially higher in patients with human immunodeficiency virus (HIV) compared with the general population and is associated with higher mortality rates. Despite this, the condition remains poorly understood, and there is variation in reported epidemiological studies. The aim of this systematic review and meta-analysis was to investigate the risk factors associated with seizures in the population with HIV, explore the source of variations, and describe management plans that can aid clinicians in the acute and long-term treatment of these patients.

METHODS

A structured electronic database search of MEDLINE, EMBASE, and Cochrane Library was conducted. Studies were included if they described clinical details of patients with HIV with seizures or epilepsy. We extracted select variables from each included study, and we estimated pooled estimates of the incidence and prevalence of seizures using random-effects meta-analysis of proportions.

RESULTS

Information on 6639 cases of patients with HIV was extracted from 9 included studies. These comprised of 2 studies from the United States of America (USA), 3 from Europe, 3 from Asia, and 1 from Africa. The pooled prevalence and incidence rate of seizures in HIV were 62 per 1000 population and 60 per 1000 population respectively. Among those who presented with new-onset seizures, 63% had seizure recurrence. At the time of first seizure, 82.3% had acquired immunodeficiency syndrome (AIDS). Factors that appeared to be linked to seizures in HIV included advanced HIV disease, opportunistic infections particularly toxoplasmosis, and metabolic derangement. Most seizures were effectively controlled by common antiepileptic drugs (AEDs).

CONCLUSIONS

The prevalence and incidence of seizures and epilepsy in the population with HIV are substantially higher than the general population. Our results suggest that advanced HIV and opportunistic infections are associated with the majority of the seizures. Early initiation of highly active antiretroviral therapy (HAART), prophylactic use of cotrimoxazole (trimethoprim-sulfamethoxazole) and routine electroencephalogram (EEG) in patients with HIV may reduce seizure incidence and frequency and help in early diagnosis of nonconvulsive seizures in this population. We recommend long-term seizure management with AED, and for patients on HAART, enzyme-inducing AED should be avoided when possible.

Automated Source Estimation of Scalp EEG Epileptic Activity Using eLORETA Kurtosis Analysis

OBJECTIVES

eLORETA (exact low-resolution brain electromagnetic tomography) is a technique created by Pascual-Marqui et al. [Int J Psychophysiol. 1994 Oct; 18(1): 49-65] for the 3-dimensional representation of current source density in the brain by electroencephalography (EEG) data. Kurtosis analysis allows for the identification of spiky activity in the brain. In this study, we focused on the evaluation of the reliability of eLORETA kurtosis analysis. For this purpose, the results of eLORETA kurtosis source localization of paroxysmal activity in EEG were compared with those of eLORETA current source density (CSD) analysis of EEG data in 3 epilepsy patients with partial seizures.

METHODS

EEG was measured using a digital EEG system with 19 channels. We set the bandpass filter at traditional frequency band settings (1-4, 4-8, 8-15, 15-30, and 30-60 Hz) and 5-10 and 20-70 Hz and performed eLORETA kurtosis to compare the source localization of paroxysmal activity with that of visual interpretation of EEG data and CSD analysis of eLORETA in focal epilepsy patients.

RESULTS

The eLORETA kurtosis analysis of EEG data preprocessed by bandpass filtering from 20 to 70 Hz and traditional frequency band settings did not show any discrete paroxysmal source activity compatible with the results of CSD analysis of eLORETA. In all 3 cases, eLORETA kurtosis analysis filtered at 5-10 Hz showed paroxysmal activities in the theta band, which were all consistent with the visual inspection results and the CSD analysis results.

DISCUSSION

Our findings suggested that eLORETA kurtosis analysis of EEG data might be useful for the identification of spiky paroxysmal activity sources in epilepsy patients. Since EEG is widely used in the clinical practice of epilepsy, eLORETA kurtosis analysis is a promising method that can be applied to epileptic activity mapping.

Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.

Medicare claims can identify post-stroke epilepsy

OBJECTIVE

There have been no validated Medicare claims-based algorithms available to identify epilepsy by discrete etiology of stroke (e.g., post-stroke epilepsy, PSE) in community-dwelling elderly individuals, despite the increasing availability of large datasets. Our objective was to validate algorithms that detect which patients have true PSE.

METHODS

We linked electronic health records (EHR) to Medicare claims from a Medicare Pioneer Accountable Care Organization (ACO) to identify PSE. A neurologist reviewed 01/2012-12/2014 EHR data from a stratified sample of Medicare patients aged 65+ years to adjudicate a reference-standard to develop an algorithm for identifying patients with PSE. Patient sampling strata included those with: A) epilepsy-related claims diagnosis (n = 534 [all]); B) no diagnosis but neurologist visit (n = 500 [randomly sampled from 4346]); C) all others (n = 500 [randomly sampled from 16,065]). We reconstructed the full sample using inverse probability sampling weights; then used half to derive algorithms and assess performance, and the remainder to confirm performance. We evaluated predictive performance across several measures, e.g., specificity, sensitivity, negative and positive predictive values (NPV, PPV). We selected our best performing algorithms based on the greatest specificity and sensitivity.

RESULTS

Of 20,943 patients in the reconstructed sample, 13.6% of patients with epilepsy had reference-standard PSE diagnosis, which represents a 3-year overall prevalence of 0.28% or 28/10,000, and a prevalence within the subpopulation with stroke of 3%. The best algorithm included three conditions: (a) at least one cerebrovascular claim AND one epilepsy-specific anticonvulsant OR (b) at least one cerebrovascular claim AND one electroencephalography claim (specificity 100.0% [95% CI 99.9%-100.0%], NPV 98.8% [98.6%-99.0%], sensitivity 20.6% [95% CI 14.6%-27.9%], PPV 86.5% [95% CI 71.2%-95.5%]).

CONCLUSION

Medicare claims can identify elderly Medicare beneficiaries with PSE with high accuracy. Future epidemiological surveillance of epilepsy could incorporate similar algorithms to accurately identify epilepsy by varying etiologies.

Metacognitive beliefs and illness perceptions are associated with emotional distress in people with epilepsy

PURPOSE

Emotional distress is common in people with epilepsy (PWE) for which efficacious interventions are required. Developing evidence-based treatments should be based on testable models of the psychological mechanisms maintaining psychopathology. The Self-Regulatory Executive Function (S-REF) model proposes that maladaptive metacognitive beliefs and processes are central to the development and maintenance of emotional distress. Although preliminary support exists for the role of metacognitive beliefs in emotional distress in PWE, their role has yet to be tested when controlling for the contribution made by illness perceptions.

METHODS

Four hundred and fifty-seven PWE completed an online survey, which assessed anxiety, depression, metacognitive beliefs, illness perceptions, general demographic factors, and epilepsy characteristics.

RESULTS

Hierarchical regression analyses demonstrated that metacognitive beliefs and illness perceptions were both associated with anxiety and depression when controlling for the influence of demographic variables and epilepsy characteristics. However, metacognitive beliefs accounted for more variance in anxiety and depression than illness perceptions.

CONCLUSION

Metacognitive beliefs appear to make a greater contribution to anxiety and depression in PWE than illness perceptions. Prospective studies are now needed to establish the causal role of metacognitive beliefs in both the development and persistence of emotional distress.

An estimation of global volume of surgically treatable epilepsy based on a systematic review and meta-analysis of epilepsy

OBJECTIVEEpilepsy is one of the most common neurological disorders, yet its global surgical burden has yet to be characterized. The authors sought to compile the most current epidemiological data to quantify global prevalence and incidence, and estimate global surgically treatable epilepsy. Understanding regional and global epilepsy trends and potential surgical volume is crucial for future policy efforts and resource allocation.METHODSThe authors performed a systematic literature review and meta-analysis to determine the global incidence, lifetime prevalence, and active prevalence of epilepsy; to estimate surgically treatable epilepsy volume; and to evaluate regional trends by WHO regions and World Bank income levels. Data were extracted from all population-based studies with prespecified methodological quality across all countries and demographics, performed between 1990 and 2016 and indexed on PubMed, EMBASE, and Cochrane. The current and annual new case volumes for surgically treatable epilepsy were derived from global epilepsy prevalence and incidence.RESULTSThis systematic review yielded 167 articles, across all WHO regions and income levels. Meta-analysis showed a raw global prevalence of lifetime epilepsy of 1099 per 100,000 people, whereas active epilepsy prevalence is slightly lower at 690 per 100,000 people. Global incidence was found to be 62 cases per 100,000 person-years. The meta-analysis predicted 4.6 million new cases of epilepsy annually worldwide, a prevalence of 51.7 million active epilepsy cases, and 82.3 million people with any lifetime epilepsy diagnosis. Differences across WHO regions and country incomes were significant. The authors estimate that currently 10.1 million patients with epilepsy may be surgical treatment candidates, and 1.4 million new surgically treatable epilepsy cases arise annually. The highest prevalences are found in Africa and Latin America, although the highest incidences are reported in the Middle East and Latin America. These regions are primarily low- and middle-income countries; as expected, the highest disease burden falls disproportionately on regions with the fewest healthcare resources.CONCLUSIONSUnderstanding of the global epilepsy burden has evolved as more regions have been studied. This up-to-date worldwide analysis provides the first estimate of surgical epilepsy volume and an updated comprehensive overview of current epidemiological trends. The disproportionate burden of epilepsy on low- and middle-income countries will require targeted diagnostic and treatment efforts to reduce the global disparities in care and cost. Quantifying global epilepsy provides the first step toward restructuring the allocation of healthcare resources as part of global healthcare system strengthening.

FACETS of health disparities in epilepsy surgery and gaps that need to be addressed

Purpose of review

Disparities in treatment and outcomes of patients with epilepsy have been identified in several distinct patient populations. The purpose of this review is to organize the literature and establish clear pathways as to why certain patient populations are not receiving epilepsy surgery. By establishing the acronym FACETS (fear of treatment, access to care, communication barriers, education, trust between patient and physician, and social support), we set up a pathway to further study this area in an organized fashion, hopefully leading to objective solutions.

Recent findings

Studies revealed that African American, Hispanic, and non-English-speaking patients underwent surgical treatment for epilepsy at rates significantly lower compared to white patients.

Summary

This article explains possible reasons outlined by FACETS for the health disparities in epilepsy surgery that exist in patients of a certain race, socioeconomic status, and language proficiency.

Parental age and risk of epilepsy: A nationwide register-based study

OBJECTIVE

This study aims to examine the association between maternal age, paternal age, and parental age difference at the time of birth and the risk of epilepsy in the offspring.

METHODS

We carried out a prospective population-based register study of all singletons born in Denmark between 1981 and 2012. Cox regression was used to estimate hazard ratios (HRs) of epilepsy and their corresponding 95% confidence intervals (CIs), adjusted for relevant confounders.

RESULTS

We followed 1 587 897 individuals for a total of ~25 million person-years and identified 21 797 persons with epilepsy during the study period. An excess risk of epilepsy was found in individuals born to mothers younger than 20 years (HR = 1.17, 95% CI = 1.07-1.29) and born to parental couples where paternal age exceeded maternal age by at least 5 years. The risk of epilepsy increased with increasing parental age gap and was highest when the father was ≥15 years older than the mother (adjusted HR = 1.28, 95% CI = 1.16-1.41). In contrast to maternal age, we found that paternal age did not independently contribute to offspring epilepsy risk, once we accounted for the parental age difference (P = .1418). The observed associations with maternal age and parental age gap were invariant to epilepsy subtypes, but were modified by age of epilepsy onset, with the effect being most pronounced in the first 10 years of the child's life.

SIGNIFICANCE

Maternal age and parental age gap, but not paternal age, were associated with the offspring's risk of epilepsy. Our results do not support the hypothesis that de novo mutations associated with advanced paternal age increase the risk of epilepsy in the offspring.

Differentiation of spindle associated hippocampal HFOs based on a correlation analysis

High Frequency Oscillations (HFOs) have been described as biomarkers of epileptogenic tissue; however their pathological/physiological classification poses a challenge to their predictive power. For the population of ripples co-occurring with sleep spindles, those ripples improving the antiparallel correlation of ripple-peaks and sleep spindle-troughs were classified as coupled-ripples and the rest as uncoupled-ripples. For the same population of ripples two reference groups called in-SOZ and non-SOZ were formed according to the ripples' location inside or outside the seizure onset zone (SOZ). Nine patients were analyzed and their formed groups were compared using three amplitude, three waveform and three frequency features. The coupled-ripples group showed similar feature values to the non-SOZ group. The correlation based classification approach shows potential to verify the SOZ and predict alterations in the memory consolidation process.

Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data

Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.

Accuracy of claims-based algorithms for epilepsy research: Revealing the unseen performance of claims-based studies

OBJECTIVE

To evaluate published algorithms for the identification of epilepsy cases in medical claims data using a unique linked dataset with both clinical and claims data.

METHODS

Using data from a large, regional health delivery system, we identified all patients contributing biologic samples to the health system's Biobank (n = 36K). We identified all subjects with at least one diagnosis potentially consistent with epilepsy, for example, epilepsy, convulsions, syncope, or collapse, between 2014 and 2015, or who were seen at the epilepsy clinic (n = 1,217), plus a random sample of subjects with neither claims nor clinic visits (n = 435); we then performed a medical chart review in a random subsample of 1,377 to assess the epilepsy diagnosis status. Using the chart review as the reference standard, we evaluated the test characteristics of six published algorithms.

RESULTS

The best-performing algorithm used diagnostic and prescription drug data (sensitivity = 70%, 95% confidence interval [CI] 66-73%; specificity = 77%, 95% CI 73-81%; and area under the curve [AUC] = 0.73, 95%CI 0.71-0.76) when applied to patients age 18 years or older. Restricting the sample to adults aged 18-64 years resulted in a mild improvement in accuracy (AUC = 0.75,95%CI 0.73-0.78). Adding information about current antiepileptic drug use to the algorithm increased test performance (AUC = 0.78, 95%CI 0.76-0.80). Other algorithms varied in their included data types and performed worse.

SIGNIFICANCE

Current approaches for identifying patients with epilepsy in insurance claims have important limitations when applied to the general population. Approaches incorporating a range of information, for example, diagnoses, treatments, and site of care/specialty of physician, improve the performance of identification and could be useful in epilepsy studies using large datasets.

Understanding the self-management skills of persons with epilepsy

PURPOSE

To determine whether the self-management skills of persons with epilepsy (PWE) vary across the different domains of the Epilepsy Self-Management Scale (ESMS).

METHODS

172 PWE completed a survey questionnaire as well as the ESMS.

RESULTS

Using ANOVA with pairwise comparison, the mean item scores of the medication, seizure, and safety management subscales of the ESMS were significantly higher than the lifestyle and information management subscales (p<0.01). The mean item score for the lifestyle management subscale was significantly higher than the information management subscale (p<0.01).

CONCLUSION

PWE in our population performed differently across the various domains of the ESMS and did worse on the lifestyle and information management subscales. We discuss the implications of this on patient counseling and education.

Perioperative Risk in Patients With Epilepsy Undergoing Total Joint Arthroplasty

BACKGROUND

Epilepsies is a spectrum of brain disorders ranging from severe, life threatening, and disabling to more benign, but little is known about its impact in the perioperative arthroplasty setting. We sought to determine whether epileptic patients undergoing elective total joint arthroplasty (TJA) would be at increased risk for in-hospital complications and death, prolonged stay, and nonroutine discharge.

METHODS

Using discharge records from the Nationwide Inpatient Sample (2002-2011), we identified 6,054,344 patients undergoing elective primary TJA, of whom 31,865 (0.5%) were identified as having epilepsy. Comparisons of perioperative outcomes were performed by multivariable logistic regression modeling.

RESULTS

Patients with epilepsy were associated with increased in-hospital mortality (odds ratio [OR] 2.03, 95% confidence interval [CI] 1.57-2.62) and morbidity, including (in decreasing order of magnitude of effect estimate): mechanical ventilation (OR 1.74, 95% CI 1.56-1.94), induced mental disorder (OR 1.70, 95% CI 1.56-1.85), stroke (OR 1.63, 95% CI 1.23-2.15), pneumonia (OR 1.34, 95% CI 1.21-1.49), and ileus or gastrointestinal events (OR 1.26, 95% CI 1.12-1.42). Epilepsy was associated with higher risk for blood transfusion (OR 1.30, 95% CI 1.27-1.33), prolonged hospital stay (OR 1.14, 95% CI 1.11-1.17), and nonroutine discharge (OR 1.54, 95% CI 1.50-1.58). We found no association with inpatient thromboembolic events, acute renal failure, and myocardial infarction.

CONCLUSION

Patients with epilepsy are at increased risk for early postoperative complications (especially mechanical ventilation, induced mental disorder, and stroke) and resource utilization after elective joint arthroplasty. Greater awareness of epilepsy and its health consequences may contribute to improvements in the perioperative management of TJA patients.

Causal inference as an emerging statistical approach in neurology: an example for epilepsy in the elderly

The elderly population faces an increasing number of cases of chronic neurological conditions, such as epilepsy and Alzheimer's disease. Because the elderly with epilepsy are commonly excluded from randomized controlled clinical trials, there are few rigorous studies to guide clinical practice. When the elderly are eligible for trials, they either rarely participate or frequently have poor adherence to therapy, thus limiting both generalizability and validity. In contrast, large observational data sets are increasingly available, but are susceptible to bias when using common analytic approaches. Recent developments in causal inference-analytic approaches also introduce the possibility of emulating randomized controlled trials to yield valid estimates. We provide a practical example of the application of the principles of causal inference to a large observational data set of patients with epilepsy. This review also provides a framework for comparative-effectiveness research in chronic neurological conditions.

Investigation of automatically detected high frequency oscillations (HFOs) as an early predictor of seizure onset zone

High frequency oscillations (HFOs) during inter-ictal state have been considered as a potential biomarker of epileptogenic regions in brain. The purpose of the current study is to improve and automatize the detection of HFOs basing on HFO distinguishing features followed by unsupervised clustering method, and to predict seizure onset zone (SOZ) using the clustered HFOs. The algorithm successfully separated HFOs of different sub-categories from noise, artifacts, and inter-ictal spikes. We tested this technique on two subjects, and assessed the performance of SOZ prediction by computing the overlapping rate of HFO generative channels and seizure onset channels. In both subjects, we were able to localize the seizure onset area 3 to 4 days before the actual onset of the seizure, with high specificity over 95%. The algorithm showed significant improvement comparing to another existing technique.

Estimating the Prevalence of Treated Epilepsy Using Administrative Health Data and Its Validity: ESSENCE Study

Background and Purpose

Few of the epidemiologic studies of epilepsy have utilized well-validated nationwide databases. We estimated the nationwide prevalence of treated epilepsy based on a comprehensive medical payment database along with diagnostic validation.

Methods

We collected data on patients prescribed of antiepileptic drugs (AEDs) from the Health Insurance Review and Assessment service, which covers the entire population of Korea. To assess the diagnostic validity, a medical records survey was conducted involving 6,774 patients prescribed AEDs from 43 institutions based on regional clusters and referral levels across the country. The prevalence of treated epilepsy was estimated by projecting the diagnostic validity on the number of patients prescribed AEDs.

Results

The mean positive predictive value (PPV) for epilepsy was 0.810 for those prescribed AEDs with diagnostic codes that indicate epilepsy or seizure (Diagnosis-E), while it was 0.066 for those without Diagnosis-E. The PPV tended to decrease with age in both groups, with lower values seen in females. The prevalence was 3.84 per 1,000, and it was higher among males, children, and the elderly.

Conclusions

The prevalence of epilepsy in Korea was comparable to that in other East Asian countries. The diagnostic validity of administrative health data varies depending on the method of case ascertainment, age, and sex. The prescriptions of AEDs even without relevant diagnostic codes should be considered as a tracer for epilepsy.

Epilepsy in India I: Epidemiology and public health

Of the 70 million persons with epilepsy (PWE) worldwide, nearly 12 million PWE are expected to reside in India; which contributes to nearly one-sixth of the global burden. This paper (first of the two part series) provides an in-depth understanding of the epidemiological aspects of epilepsy in India for developing effective public health prevention and control programs. The overall prevalence (3.0-11.9 per 1,000 population) and incidence (0.2-0.6 per 1,000 population per year) data from recent studies in India on general population are comparable to the rates of high-income countries (HICs) despite marked variations in population characteristics and study methodologies. There is a differential distribution of epilepsy among various sociodemographic and economic groups with higher rates reported for the male gender, rural population, and low socioeconomic status. A changing pattern in the age-specific occurrence of epilepsy with preponderance towards the older age group is noticed due to sociodemographic and epidemiological transition. Neuroinfections, neurocysticercosis (NCC), and neurotrauma along with birth injuries have emerged as major risk factors for secondary epilepsy. Despite its varied etiology (unknown and known), majority of the epilepsy are manageable in nature. This paper emphasizes the need for focused and targeted programs based on a life-course perspective and calls for a stronger public health approach based on equity for prevention, control, and management of epilepsy in India.

Prevalence and Incidence of Epilepsy in an Elderly and Low-Income Population in the United States

Background and Purpose

The purpose of this study was to estimate the incidence and prevalence of epilepsy among an elderly and poor population in the United States.

Methods

Arizona Medicaid claims data from January 1, 2008 to December 31, 2010 were used for this analysis. Subjects who were aged ≥65 years and were continuously enrolled in any Arizona Medicaid health plans (eligible to patients with low income) for ≥12 months between January 1, 2008 and December 31, 2009 were considered eligible for inclusion in the study cohort. In addition to meeting the aforementioned criteria, incident and prevalent cases must have had epilepsy-related healthcare claims. Furthermore, incident cases were required to have a 1-year "clean" period immediately preceding the index date. Negative binomial and logistic regression models were used to assess the factors associated with epilepsy incidence and prevalence.

Results

The estimated epilepsy incidence and prevalence for this population in 2009 were 7.9 and 19.3 per 1,000 person-years, respectively. The incidence and prevalence rates were significantly higher for patients with comorbid conditions that were potential risk factors for epilepsy and were of younger age than for their non-comorbid and older counterparts (p<0.05). The prevalence rates were significantly higher for non-Hispanic Blacks and male beneficiaries than for non-Hispanic Whites and female beneficiaries, respectively (p<0.05).

Conclusions

This patient population had higher epilepsy incidence and prevalence compared with the general US population. These differences may be at least in part attributable to their low socioeconomic status.

The relationship of depression to antiepileptic drug adherence and quality of life in epilepsy

We sought to examine the impact of depression upon antiepileptic drug (AED) adherence in patients with epilepsy. We administered the Center for Epidemiologic Studies Depression Scale (CES-D), Neurological Disorders Depression Inventory for Epilepsy (NDDI-E), Seizure Severity Questionnaire (SSQ), and Quality of Life in Epilepsy-10 (QOLIE-10) and measured AED adherence by utilizing the medication possession ratio (MPR) in adult patients with epilepsy identified through a pharmacy claims database. From a sampling frame of over 10,000 patients identified in claims, 2750 were randomly selected and contacted directly by mail to participate in the cross-sectional survey. A total of 465 eligible patients completed a survey. Survey data were combined with administrative claims data for analysis. We conducted a path analysis to assess the relationships between depression, adherence, seizure severity, and quality of life (QOL). Patients with depression scored significantly worse on measures of seizure severity (p=.003), QOL (p<.001), and adherence (p=.001). On path analysis, depression and QOL and seizure severity and QOL were related, but only the NDDI-E scores had a significant relationship with medication adherence (p=.001). Depression as measured by the NDDI-E was correlated with an increased risk of AED nonadherence. Depression or seizure severity adversely impacted QOL. These results demonstrate yet another important reason to screen for depression in epilepsy.

The societal integration of individuals with epilepsy: perspectives for the 21st century

Epilepsy is a common neurologic disorder seen throughout the world. Advances in therapy have made it possible for persons with epilepsy (PWEs) to have improved seizure control and a better quality of life. However, it is not entirely clear whether this has resulted in their successful integration into society. This review examines the societal integration of PWEs, identifying both the progress made and the challenges that continue to hamper further advances. In general, PWEs are more integrated in western-oriented cultures. However, there continue to be ongoing difficulties due to poor education and intellectual functioning, poor social and family support, the undertreatment of coexisting psychiatric conditions, transportation and mobility limitations, and problems obtaining employment. This review also discusses the effects of low socioeconomic status on integration and the persisting prejudices that affect certain racial groups. Most importantly, this review underscores the fact that societal stigma towards PWEs is still very much alive. At the beginning of the 21st century, PWEs still encounter difficulties in their quest for full societal integration. Along with medical advances being made to improve seizure control, much still has to be done to bring about the reforms necessary to help PWEs live more meaningful and productive lives.

Unexpected finding of delayed-onset seizures in HIV-positive, treatment-experienced subjects in the Phase IIb evaluation of fosdevirine (GSK2248761)

BACKGROUND

Fosdevirine (GSK2248761) is a non-nucleoside reverse transcriptase inhibitor with HIV-1 activity against common efavirenz-resistant strains. Two partially blind, randomized, Phase IIb studies were initiated (1 in treatment-naive and 1 in treatment-experienced subjects with HIV) to select a once-daily dose of fosdevirine for Phase III trials.

METHODS

In the SIGNET study, treatment-naive subjects were randomized 1:1:1 to receive once-daily fosdevirine 100 or 200 mg or efavirenz 600 mg, each along with tenofovir disoproxil fumarate/emtricitabine 300 mg/200 mg or abacavir/lamivudine 600 mg/300 mg. In the SONNET study, treatment-experienced subjects with non-nucleoside reverse transcriptase inhibitor-resistant HIV-1 were randomized 1:1:1 to treatment with fosdevirine 100 or 200 mg once daily or etravirine 200 mg twice daily, each along with twice-daily darunavir/ritonavir 600/100 mg and raltegravir 400 mg. The primary efficacy end point was the proportion of subjects with HIV-1 RNA<50 copies/ml. Safety and pharmacokinetics were also addressed.

RESULTS

A total of 35 subjects were exposed to fosdevirine 100 or 200 mg. Trials were halted when 5 treatment-experienced subjects (1 receiving fosdevirine 100 mg, 4 receiving fosdevirine 200 mg) developed new-onset seizures after ≥4 weeks of exposure to fosdevirine. There was no clear association between seizures and fosdevirine plasma drug levels. Time to seizure onset ranged from 28 to 81 days, and all 5 subjects experienced ≥1 seizure after drug discontinuation.

CONCLUSIONS

The delayed onset of seizures after fosdevirine exposure and persistence after discontinuation is without precedent in antiretroviral drug development, leading to additional investigation and underscoring the need for careful subject monitoring.

Defining incident cases of epilepsy in administrative data

PURPOSE

To determine the minimum enrollment duration for identifying incident cases of epilepsy in administrative data.

METHODS

We performed a retrospective dynamic cohort study using Ohio Medicaid data from 1992 to 2006 to identify a total of 5037 incident epilepsy cases who had at least 1 year of follow-up prior to epilepsy diagnosis (epilepsy-free interval). The incidence for epilepsy-free intervals from 1 to 8 years, overall and stratified by pre-existing disability status, was examined. The graphical approach between the slopes of incidence estimates and the epilepsy-free intervals was used to identify the minimum epilepsy-free interval that minimized misclassification of prevalent as incident epilepsy cases.

RESULTS

As the length of epilepsy-free interval increased, the incidence rates decreased. A graphical plot showed that the decline in incidence of epilepsy became nearly flat beyond the third epilepsy-free interval.

CONCLUSION

The minimum of 3-year epilepsy-free interval is needed to differentiate incident from prevalent cases in administrative data. Shorter or longer epilepsy-free intervals could result in over- or under-estimation of epilepsy incidence.

Incidence and prevalence of treated epilepsy among poor health and low-income Americans

OBJECTIVES

To determine the incidence and prevalence of treated epilepsy in an adult Medicaid population.

METHODS

We performed a retrospective, dynamic cohort analysis using Ohio Medicaid claims data between 1992 and 2006. Individuals aged 18-64 years were identified as prevalent cases if they had ≥2 claims of epilepsy (ICD-9-CM: 345.xx) or ≥3 claims of convulsion (ICD-9-CM: 780.3 or 780.39) and ≥2 claims of antiepileptic drugs. Incident cases were required to have no epilepsy or convulsion claims for ≥5 years before epilepsy diagnosis. Subjects were determined as having preexisting disability and/or comorbid conditions, including brain tumor, depression, developmental disorders, migraine, schizophrenia, stroke, and traumatic brain injury, when at least one of these conditions occurred before epilepsy onset.

RESULTS

There were 9,056 prevalent cases of treated epilepsy in 1992-2006 and 1,608 incident cases in 1997-2006. The prevalence was 13.2/1,000 (95% confidence interval, 13.0-13.5/1,000). The incidence was 362/100,000 person-years (95% confidence interval, 344-379/100,000 person-years). The incidence and prevalence were significantly higher in men, in older people, in blacks, and in people with preexisting disability and/or comorbid conditions. The most common preexisting conditions in epilepsy subjects were depression, developmental disorders, and stroke, whereas people with brain tumor, traumatic brain injury, and stroke had the higher risk of developing epilepsy.

CONCLUSIONS

The Medicaid population has a high incidence and prevalence of epilepsy, in an order of magnitude greater than that reported in the US general population. This indigent population carries a disproportionate amount of the epilepsy burden and deserves more attention for its health care needs and support services.

Economic differences in direct and indirect costs between people with epilepsy and without epilepsy

OBJECTIVE

To provide generalizable estimates of economic burden in epilepsy and nonepilepsy populations and a comprehensive accounting for employment-based lost productivity associated with epilepsy in current US health care systems as compared with other chronic diseases.

RESEARCH DESIGN

We use the nationally representative data source (Medical Expenditure Panel Survey) from 1998 to 2009 to create a retrospective cohort of people diagnosed with epilepsy by a health professional and a comparison cohort of people with no epilepsy.

MEASURES

Health care utilization and direct costs for all components of treatment, including prescription medications, wages, employment, educational attainment, family income, and lost productivity were outcomes.

RESULTS

We observed economic disparities associated with epilepsy in the United States despite high rates of modern treatments (89% on anticonvulsant therapies). Only 42% of the people with epilepsy over age 18 reported employment compared with 70% of people with no epilepsy; among those, people with epilepsy reported missing an average of 12 days of work because of illness or injury as compared with 4 days in the nonepilepsy cohort. Holding other variables constant, people with epilepsy had a loss of productivity of $9504 in 2011 dollars compared with people with no epilepsy. In comparison, diabetes was associated with annual average lost productivity valued at $3358 and depression at $3182.

CONCLUSIONS

Lost wage-based productivity associated with epilepsy was nearly equal to combined wage losses associated with diabetes, depression, anxiety, and asthma together. To evaluate societal burden of illness, results illustrate the importance of indirect costs in addition to treated prevalence and direct medical costs.

Geographic variation of epilepsy for older Americans: how close to the geographic variation of stroke?

PURPOSE

Given the strong association of stroke and epilepsy in older persons, and the existence of a Stroke Belt in the United States, we hypothesized that geographic variation in epilepsy prevalence would follow geographic patterns similar to stroke.

METHODS

We used a 2005 5% random sample of Medicare beneficiaries 65 and older in 48 U.S. contiguous states. Epilepsy was identified from claims for physician visits, hospitalizations, and outpatient procedures. Prevalence was obtained by state and county. Logistic regressions determined the independent association of the likelihood of epilepsy (prevalent or new case) and residence in Stroke Belt states, controlling for residence in highest epilepsy prevalence states, demographics (race, age, gender), comorbid conditions, cerebrovascular disease, dementia, and county characteristics.

KEY FINDINGS

Of 1,212,015 beneficiaries, 11.9 per 1,000 had prevalent and 2.9 new cases of epilepsy. Nine of 11 Stroke Belt states were among the 20 states with the highest epilepsy prevalence. Counties in the 10 highest epilepsy prevalence states were more likely to be large urban counties with a higher number of neurologists or neurosurgeons per capita. The higher likelihood of prevalent epilepsy cases associated with Stroke Belt residence was explained by beneficiaries' race; that associated with residence in high epilepsy prevalence states was not. The likelihood of new epilepsy cases was negatively associated with Stroke Belt residence when controlling for covariates.

SIGNIFICANCE

The geographic variation in epilepsy prevalence is not explained by variations in known risk factors. Further research should investigate why eastern U.S. states have higher frequency of epilepsy.

High-frequency oscillations (HFOs) in clinical epilepsy

Epilepsy is one of the most frequent neurological diseases. In focal medically refractory epilepsies, successful surgical treatment largely depends on the identification of epileptogenic zone. High-frequency oscillations (HFOs) between 80 and 500Hz, which can be recorded with EEG, may be novel markers of the epileptogenic zone. This review discusses the clinical importance of HFOs as markers of epileptogenicity and their application in different types of epilepsies. HFOs are clearly linked to the seizure onset zone, and the surgical removal of regions generating them correlates with a seizure free post-surgical outcome. Moreover, HFOs reflect the seizure-generating capability of the underlying tissue, since they are more frequent after the reduction of antiepileptic drugs. They can be successfully used in pediatric epilepsies such as epileptic spasms and help to understand the generation of this specific type of seizures. While mostly recorded on intracranial EEGs, new studies suggest that identification of HFOs on scalp EEG or magnetoencephalography (MEG) is possible as well. Thus not only patients with refractory epilepsies and invasive recordings but all patients might profit from the analysis of HFOs. Despite these promising results, the analysis of HFOs is not a routine clinical procedure; most results are derived from relatively small cohorts of patients and many aspects are not yet fully understood. Thus the review concludes that even if HFOs are promising biomarkers of epileptic tissue, there are still uncertainties about mechanisms of generation, methods of analysis, and clinical applicability. Large multicenter prospective studies are needed prior to widespread clinical application.

[Neuroimaging in pediatric epilepsy]

The main causes of epilepsy in children are cortical malformations (hemimegalencephaly, cortical dysplasia, lissencephaly, etc.) and phakomatosis (tuberous sclerosis, Sturge-Weber disease, neurofibromatosis type 1, etc.), perinatal ischemia, traumatisms, infections, mesial temporal sclerosis, metabolic diseases, and tumors. Imaging indications are precise, including partial seizures and a pathological electroencephalogram. Twenty-five percent of these epilepsy cases are pharmacoresistant. Indeed, MRI is essential to consider surgical treatment, allowing one to localize potential epileptogenic anatomic lesions. The protocol includes sequences in three planes of space, weighted in T1, T2, Flair, T1 inversion-recovery, and T1 after gadolinium injection. MRI findings are characteristic for some tumors, but most malformations are subtle. Consequently recent techniques (spectroscopy, diffusion, etc.) are crucial when conventional MRI is not sufficient. The aim of this article is to illustrate, with a substantive image revue, this wide diversity of etiologies in pediatric epilepsy, in order to help the attendee recognize MRI findings, also discussing the role of newer imaging modalities in this field.

Incidence and prevalence of epilepsy among older U.S. Medicare beneficiaries

OBJECTIVE

To determine the prevalence and incidence of epilepsy among U.S. Medicare beneficiaries aged 65 years old and over, and to compare rates across demographic groups.

METHODS

We performed a retrospective analysis of Medicare administrative claims for 2001-2005, defining prevalent cases as persons with ≥1 claim with diagnosis code 345.xx (epilepsy) or 2 or more with diagnosis code 780.3x (convulsion) ≥1 month apart, and incident cases as prevalent cases with 2 years immediately before diagnosis without such claims. Prevalence and incidence rates were calculated for the years 2003-2005 using denominators estimated from a 5% random sample of Medicare beneficiaries. Results were correlated with gender, age, and race.

RESULTS

We identified 282,661 per year on average during 2001-2005 (a total of 704,243 unique cases overall), and 62,182 incident cases per year on average during 2003-2005. Average annual prevalence and incidence rates were 10.8/1,000 and 2.4/1,000. Overall, rates were higher for black beneficiaries (prevalence 18.7/1,000, incidence 4.1/1,000), and lower for Asians (5.5/1,000, 1.6/1,000) and Native Americans (7.7/1,000, 1.1/1,000) than for white beneficiaries (10.2/1,000, 2.3/1,000). Incidence rates were slightly higher for women than for men, and increased with age for all gender and race groups.

CONCLUSIONS

Epilepsy is a significant public health problem among Medicare beneficiaries. Efforts are necessary to target groups at higher risk, such as minorities or the very old, and to provide the care necessary to reduce the negative effects of epilepsy on quality of life.