Abstract
Predictors of incident Epilepsy in older adults. the Cardiovascular Health Study
Choi H, Pack A, Elkind, MSV, Longstreth WT Jr, Ton TGN, Onchiri F. Neurology 2017;88:870–877.
OBJECTIVE: To determine the prevalence, incidence, and predictors of epilepsy among older adults in the Cardiovascular Health Study (CHS). METHODS: We analyzed data prospectively collected in CHS and merged with data from outpatient Medicare administrative claims. We identified cases with epilepsy using self-report, antiepileptic medication, hospitalization discharge ICD-9 codes, and outpatient Medicare ICD-9 codes. We used Cox proportional hazards regression to identify factors independently associated with incident epilepsy. RESULTS: At baseline, 42% of the 5,888 participants were men and 84% were white. At enrollment, 3.7% (215 of 5,888) met the criteria for prevalent epilepsy. During 14 years of follow-up totaling 48,651 person-years, 120 participants met the criteria for incident epilepsy, yielding an incidence rate of 2.47 per 1,000 person-years. The period prevalence of epilepsy by the end of follow-up was 5.7% (335 of 5,888). Epilepsy incidence rates were significantly higher among blacks than nonblacks: 4.44 vs 2.17 per 1,000 person-years (p, 0.001). In multivariable analyses, risk of incident epilepsy was significantly higher among blacks compared to nonblacks (hazard ratio [HR] 4.04, 95% confidence interval [CI] 1.99–8.17), those 75 to 79 compared to those 65 to 69 years of age (HR 2.07, 95%CI 1.21–3.55), and those with history of stroke (HR 3.49, 95% CI 1.37–8.88). CONCLUSIONS: Epilepsy in older adults in the United States was common. Blacks, the very old, and those with history of stroke have a higher risk of incident epilepsy. The association with race remains unexplained.
Commentary
Epilepsy, whether in prevalent or incident form, is a steadily expanding national and global health concern. Recent US national surveillance data estimating prevalent epilepsy cases in child and adult groups found numbers increasing by a rate approaching 24% from 2010 to 2015 (2.3–3.0 million cases) with over 1% of the population identified as having epilepsy (1). When examining occurrence rates in older adults, numbers are likewise concerning. Estimated prevalence and incident rates from a large random sample of US Medicare beneficiaries found rates of 10.8/1000 and 2.4/1000, respectively (2). The numbers were especially notable for African-American males and for the oldest age group. Data from a large community-based surveillance sample found much higher incident rates in older adults (ages 60–74, 75–89) compared with younger adults (3). The rise as age increased doubled from the younger group to the age 60 to 74 group (from 10.6–25.8/100,000) and then dramatically increased in the oldest group (101.1/100,000). Similar to other studies, race was a factor with African-Americans having a higher incidence rate that Caucasians and that stroke history was a significantly predictive factor. Given the costly growth in terms of economic and healthcare costs, in addition the personal costs related to quality of life reduction in the older person with epilepsy, researchers continue with ongoing efforts to establish more precise incident and prevalent rates that in turn will contribute to developing improved predictor risk models. Of the many challenges when gathering epidemiologic information, one is the form by which it is determined. A number of well-known collection approaches have been described that include health surveys, direct health record reviews, insurance claims-based ascertainment methods, as well as various combination of these methods. Recently reported guidelines for epilepsy epidemiologic and surveillance studies outlined these issues (4). That comprehensive document also highlighted the need for epidemiologic studies of epilepsy to be mindful of study costliness, potential burden of time and privacy to those providing information, accuracy by which to improve the probability of raising the chance for a valid data collection (i.e., specificity and sensitivity values), and that the data be representative of the intended target population (4).
Keeping these guideline recommendations in mind, recognition of accurate identification of epilepsy cases requires a comprehensive approach for gathering multiple data sources. These sources could include information from medical record chart reviews, insurance claims, and survey data. Studies that can access multiple pieces from these medical information sources hold improved prospects of establishing validated estimates of incident epilepsy. Some have been able to access relatively large datasets from regional and national healthcare systems with access to several important pieces of clinical information (i.e., claims, medical records).
One recent study addressed these issues via a combination approach from both community- and medical claims–based data. Choi and colleagues present a well-described example of efforts aimed at collecting a diversity of clinical and claims data to estimate incidence epilepsy cases in older adults, as well as examine predictor variables of those rates. The authors described the intended targets for study were to 1) accurately identify cases of epilepsy from various sources, and 2) identify factors that predict those epilepsy cases. The authors examined these questions with data obtained from the Cardiovascular Health Study (CHS), a large, longitudinal, four US region, community-based cohort study of coronary artery disease and stroke. This data in turn had been merged with outpatient claims information from the Centers of Medicare and Medicaid Services (CMS). This combined information resource allowed the researchers to examine a wide berth of information from claims-based insurance records on inpatient/outpatient services and procedures, as well as in-depth clinical records, and in-depth participant survey data. The authors detailed their epilepsy case ascertainment process from the records reviewed by which two of the study authors served as reviewers. Cases were categorized as probable epilepsy, possible epilepsy, or not epilepsy from the combination of examined information including International Classification of Diseases, Ninth Revision (ICD-9) claims codes and CHS records reviewed. The authors used commonly employed ICD-9 claims frequencies with at least 2 years of number 345.×× or 780.3× codes to establish an incident case. With the pool of variables gathered from the multiple sources the authors examined a range of predictor variables including demographic (e.g., race, age), medical (e.g., self-reported health, co-morbidities), and cardiovascular/cerebrovascular histories. They found several significant predictors that included history of transient ischemic attack, black race, older age (75–79), postcollege education attainment, congestive heart failure and stroke.
The key findings the authors pointed out were that high incidence rates exist across a representative group of older adults from the Unites States. These incidence rates were similar to some studies cited (2, 3) and higher than others (5). As mentioned, elevated risk for African-American males was found and consistent with prior studies (3, 5) and a mix of risk and nonrisk factors were found. Stroke led the way for the co-morbid conditions analysis approaching a 3-fold risk level, while other expected risk conditions did not carry such risk (i.e., coronary artery disease, obesity, and hypertension). The study provided an excellent example of how combining variety of data source can allow for sophisticated multivariate analysis for large and complex data set. The study was not without limitations as pointed out by the authors including having limited or no access to medical records associated with some of their CMS claims records.
Large claims-based data sets have plenty to offer in terms of approaching existing epidemiologic guidelines related to generalizability in the form of accessing a broad spectrum of racial and socioeconomic parameters as well as complex patterns of medical care utilization (e.g., clinic visits, hospitalizations, and procedures). Claims investigations rely on the accuracy of the claims input via diagnosis and coding accuracy but have no access to the records themselves for more in-depth investigation with puts estimates at risk for poor validity determination (6). The use of validated algorithms can enhance those estimated but balance the needs of both sensitivity and specificity calculations. The Choi study highlights a positive example of accruing data from “merged” sources and provides positive direction toward guideline goals. Additional recent efforts have built upon merged studies such as Choi by testing a range of prior published diagnostic algorithms within a population under investigation (7). Recent refinements in diagnostic classifications of seizures and epilepsy may help improve the specificity values in these statistical formulas (4).
The Choi study and other recent studies have emphasized the need for further study by which to clarify current findings. Questions include better clarification toward better understanding of racial differences (i.e., higher incidence rates in older African-Americans) and improvements toward clarifying etiology among those with unknown etiology of new-onset epilepsy. For example, a review underscored to suspicion that unexplained new-onset seizures are a “heraldic” phase of ongoing subclinical cerebrovascular disease (8). Others have highlighted the emerging evidence linking cardiovascular processes with epilepsy (9). All of these investigations strive toward better identifying, prediction, and preventing disability, morbidity, and mortality (10).