The South Carolina Epidemiological Studies of Epilepsy & Seizure Disorders (SCESESD)

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2 The South Carolina Epidemiological Studies of Epilepsy & Seizure Disorders (SCESESD) Final Report THE MEDICAL UNIVERSITY OF SOUTH CAROLINA In Partnership with The SC Budget and Control Board The Office of Research & Statistics PRINCIPAL INVESTIGATOR Anbesaw Selassie, DrPH Co-Principal Investigator Braxton Wannamaker, MD Investigators Elisabeth Pickelsimer, DA Robert Turner, MD, MSCR Gigi Smith, RN, MSN, CPNP Walter Pete Bailey, MPH Mary Tyrell, PhD Epidemiologist & Research Manager Pamela Ferguson, PhD Research Associate Lee Lineberry, BS, (PhD Student) Statistician & Programmer Ja Kook Gu, MSPH CDC Technical Advisor David Thurman, MD, MPH Project Office: (843)

3 Executive Summary The South Carolina Epidemiological Studies of Epilepsy and Seizure Disorders (SCESESD) in the Department of Biostatistics, Bioinformatics, and Epidemiology at the Medical University of South Carolina (MUSC) proposed to conduct a population-based study to determine the prevalence and incidence of epilepsy in South Carolina. The study targeted the general population of South Carolina. According to the 2002 census data, the total population of the state is 4,100,000 and it is the 26 th most populated state in the union. The population is 30% black, 67% white, and 3% other races. The lifetime prevalence of epilepsy has been determined to be 2%. Persons with epilepsy and seizure disorders were identified from four major data sources: Statewide Hospital Discharge (HD), Emergency Department (ED) Visits, Physician Outpatient Visits (POV), and the epilepsy module of the SC Behavioral Risk Factor Surveillance System (BRFSS). The first three data sources supplied information on persons with epilepsy and seizure disorders that have been clinically evaluated. Persons were identified with the diagnosis codes (International Classification of Disease 9 th Revision Clinical Modification, ICD-9-CM) of 345.x (epilepsy and status epilepticus), (febrile seizures), (seizure unspecified), (syncope), and (delirium). Case identification through BRFSS relied on self-report using random-digit dialing telephone survey of the adult population of the state. Broader case ascertainment codes were used to identify individuals that could be misclassified. Detailed clinical information was abstracted from medical charts of representative sample of 3,983 persons (5.4%) with these conditions clinically evaluated in 2001 and By the end of the third year, a total of 21,480 individuals had completed the BRFSS telephone survey making SCESESD among the largest population-based epidemiological study of epilepsy in the United States. SCESESD personnel abstracted medical record information on 3,881 randomly selected persons with epilepsy and seizure disorder. Case-level uniform billing abstracted data were acquired on 73,955 individuals using the expanded case ascertainment codes and a database has been developed. The database includes one primary and up to 9 secondary diagnoses, demographics, number of visits made during the year, payer information, CPT codes, and unique identifiers tied to SSN. The database also includes extensive and detailed information from chart abstraction on ii

4 3,881 individuals. Case-level data with the appropriate weights on 21,480 respondents has been generated. SCESESD will make these data public domain after major findings have been published. This is expected to be no later than October 31, Among the major activities completed by the project, determination of prevalence, incidence, venues of epilepsy care, and measures of data validity are proud accomplishments. Within such a short time, SCESESD impacted public policy debate, and enhanced social activism and awareness about epilepsy and seizure disorders. A steering committee comprised of persons and families with epilepsy, key stakeholders, and community leaders has been formed to promote further research on epilepsy. Data generated by the SCESESD has been used to formulate legislative initiative for social support and waiver programs for persons with epilepsy. Governor Mark Sanford declared November as epilepsy awareness month and more than three major public campaigns have been initiated to promote support groups and raise funds. BRFSS-based prevalence estimate in SC indicate a rate of 2.0% (95% CI: ) for ever having epilepsy and 1.1% (95% CI: ) for active epilepsy among individuals age 18 and older. For the two year period, the annualized estimate of prevalence derived from healthcare encounters for 2001 and 2002 ranges from 0.8% to 0.9% and appears to be in total agreement with the lower range estimate of active epilepsy derived from BRFSS. We further generated a model-based prevalence rate that provided a conservative estimate of 0.8% using information from the case-level data on 70,000 individuals in a loglinear model. The model shows potential to be tested elsewhere and uses uniformly available variables from administrative data systems. The estimated annual incidence rate of epilepsy for the general population of the state solely based on HD and ED data is 3 per 10,000 (0.03%) based on a follow-up period of 3 years. This suggests that there are at least 1,200 individuals who develop epilepsy every year. Assuming a reduction due to the force mortality at 1.68%, a rate 2.5 times higher than the mortality rate in the general population of the state, SC could have about 120,000 persons living with epilepsy by 2011 if all other contributing causes of seizure are held constant. Our incidence estimate also shows that the incidence rate of epilepsy is at least 30 times higher among persons with TBI. Our TBI Registry data indicate that the incidence of epilepsy after TBI is 2.6/100 person-year within the first iii

5 three years of follow-up. With increased incidence of TBI due to various causes, the influx of persons who develop epilepsy may be higher than the current estimate our analysis provides. Our study showed that the healthcare data sources we used can be used to monitor the prevalence of epilepsy and seizure disorders. The predictive value positive (PVP) of HD, ED, and POV suggest usefulness of these data sources for surveillance activities. Our findings indicate that the PVP of 345.x code is remarkably high for all the sources ranging between 93% and 96%. However, the PVP of 780.3x is very low ranging between 14% and 20%. It can be assumed that about 80% of those coded with seizure unspecified code of 780.3x are epilepsy cases based on 3,983 records we reviewed. Given that 70-80% of the persons coded with780.3x are misclassified as seizure and the probability of a case of epilepsy being coded as is 11 times higher than being coded as 345.x, it is imperative to develop a surveillance guideline that takes into account this prolific use to generate a reliable estimate. Our experienced team is more than willing to work with the CDC in assisting the development of such a guideline. Finally, we are proud to declare that SCESESD met all of its objectives according to the stipulation of the award. We have developed a strong, multifaceted surveillance system with diversified data sources that are complementary to each other. A decision analysis tool with an exquisite algorithm addressing each set of diagnosis codes has been developed and validated. Our team and partners are well prepared and ready for more productive research to better the lives of persons with epilepsy. iv

6 Table of Contents Page Executive Summary. i Prevalence rates... 1 Behavioral Risk Factor Surveillance System data 1 Chart abstraction data... 3 Inpatient and emergency department (HD/ED). 3 Physician office visits (POV) 5 Prevalence combining HD/ED/POV data... 7 Model-based approach to estimate prevalence. 7 Incidence rates 12 Posttraumatic epilepsy. 12 Incidence of epilepsy utilizing HD/ ED data Causes (triggers) of seizures and etiologies of epilepsy. 28 Target populations for intervention 35 Medication use - HD/ED data Medication use - POV data Posttraumatic epilepsy 40 Behavioral Risk Factor Surveillance System data.. 40 Focus groups data 41 Severity and subtypes of epilepsy. 42 HD/ED data POV data Behavioral Risk Factor Surveillance System data 48 v

7 Venues and levels of care.. 48 Behavioral Risk Factor Surveillance System data.. 48 POV data Focus groups data Traumatic Brain Injury among persons with seizure disorder Data quality Population and sample information HD/ED data POV data Predictive Value Positive and Sensitivity of seizure and epilepsy codes HD and ED Data POV Data Estimates of PVP and Sensitivity for HD/ED.data. 62 Estimates of PVP and Sensitivity for POV data Algorithm to identify epilepsy patients from administrative datasets. 63 Data quality measures.. 68 Dissemination of results. 72 Purpose and Plan.. 73 Reports, Websites, Presentations, and Publications. 73 Appendices.. 76 A. Abstraction manual for HD/ED charts B. Abstraction manual for POV charts vi

8 C. Comparison of HD/ED sample to population D. Comparison of POV sample to population E. Model-based case-level likelihood of an epilepsy diagnosis 127 F. Posttraumatic epilepsy incidence analysis 133 G. Sampling plan for HD/ED charts H. Sampling plan for POV charts Acknowledgement vii

9 I. Prevalence Rates Aim Provide reliable and stable population-based estimates on the incidence and prevalence rates of epilepsy and other seizure disorders. Prevalence rates were generated using a multifaceted approach. The state-based Behavioral Risk Factor Surveillance System (BRFSS) was used to generate self-reported epilepsy covering three years ( ) of surveillance. A total of 21,480 individuals age 18 and older responded to the survey questions. Existing data sources pertaining to 2001 and 2002 Hospital Discharges (HD), Emergency Department (ED) and Physician Office Visits (POV) provided statewide information on persons with seizure disorders. Each of these is described below. Prevalence rates were generated from both state-based BRFSS and data collected from the clinical encounters A. BRFSS is a state-based, random-digit dialed telephone survey of the noninstitutionalized, U.S. civilian population aged >18 years. The South Carolina Behavioral Risk Factor Surveillance System surveys for 2003 through 2005 included questions on epilepsy listed in Table 1. The survey included questions regarding history of epilepsy and number of seizures experienced during the past three months. Respondents were considered to have active epilepsy if they 1) reported ever having been told by a doctor that they had a seizure disorder or epilepsy and 2) either were currently taking medicine to control epilepsy or had had one or more episodes of seizure during the preceding 3 months. Active epilepsy was categorized further by whether the respondent had had one or more seizures during the preceding 3 months. Data was weighted by sex, race and age to adjust for differences between the survey population and the South Carolina population and 2004 survey results were reported earlier (MMWR, October 28, 2005 / 54(42); ). By the end of the third year, a total of 21,480 individuals had completed the survey 5,926 in 2003, 7,114 in 2004 and 8,440 in 2005 for response rates of 41.6%, 43.8% and 59.1% respectively. The first question was considered as measuring the lifetime prevalence of epilepsy, and had a response rate of 92.0%. Respondents were considered to have active epilepsy if they 1) reported ever having been told by a doctor that they had a seizure disorder or epilepsy and 2) either were currently taking medicine to control it or had had one or more episodes of seizure in the preceding 1

10 3 months. Active epilepsy was further categorized as controlled or uncontrolled based on whether the respondent had had a seizure in the preceding 3 months. Condensed results for the five questions, with active, non-active, controlled, and non-controlled are shown in Table 2, with 95% confidence intervals (CI). Table 1. Survey questions included in the SC BRFSS epilepsy module 1. Have you ever been told by a doctor that you have a seizure disorder or epilepsy? 1. Yes 2. No 7. Don t know/not sure 9. Refused 2. Are you currently taking any medicine to control your seizure disorder or epilepsy? 1. Yes 2. No 7. Don t know/not sure 9. Refused 3. How many seizures have you had in the last three months? 1. None 2. One 3. More than one 4. No longer have epilepsy or seizure disorder 7. Don t know/not sure 9. Refused 4. During the past 30 days, to what extent has epilepsy or its treatment interfered with your normal activities like working, school, or socializing with family or friends? Would you say Not at all 2. Slightly 3. Moderately 4. Quite a bit 5. Extremely 7. Don t know/not sure 9. Refused 5. In the past year have you seen a neurologist or epilepsy specialist for you epilepsy or seizure disorder?* 1. Yes 2. No 7. Don t know/not sure 9. Refused If these responses were given, interviewer skipped the rest of the epilepsy questions. *Included years only. 2

11 Table 2. Weighted summary of the SC BRFSS survey, Total Epilepsy Status N % (95% CI) Do not have epilepsy Have epilepsy Taking medicine Not taking medicine Had seizure in prev. 3 mos. No seizures in prev. 3 mos. No longer have epilepsy Epilepsy interfered Epilepsy did not interfere Epilepsy, nonactive a 19, Epilepsy, active b 207 Active, controlled c 117 Active, uncontrolled d 84 Seen neurologist in past year Yes 88 No ( ) 2.0 ( ) 46.1 ( ) 53.9 ( ) 24.5 ( ) 72.9 ( ) 2.6 ( ) 28.2 ( ) 71.8 ( ) 1.0 ( ) 1.1 ( ) 52.8( ) 47.2( ) 37.8( ) 62.2( ) a nonactive= yes to question 1, but not taking medication and no seizure in previous 3 months b active=taking medication or seizure in previous 3 months c active, controlled=taking medication and no seizure in previous 3 months d active, uncontrolled=seizure in previous 3 months The results show prevalence rate of 2.0% (95% CI: ) for ever having epilepsy and 1.1% (95% CI: ) for active epilepsy among individuals age 18 and older. Chart Abstraction Data following are data sources with abstracted information: B. Inpatient and Emergency Department The SC Budget and Control Board, Office of Research and Statistics (ORS), is the entitled by state law to serve as the repository of data from all nonfederal hospitals and EDs. Data are dumped 90 days after the end of the calendar quarter and the format of data submission is based on the Uniform Billing, 1992 layout, often referred to as UB-92. We obtained data on 70,955 unduplicated individuals from all 62 nonfederal hospitals and 64 emergency departments (EDs) across the state for the calendar year 2001 and 2002 to capture persons with a diagnosis of epilepsy, seizure disorders, syncope, and delirium. Data were unduplicated using the patients unique identifiers. A representative sample of 3,881 unduplicated records (5.5%) were abstracted to collect additional data (please see Appendix A Abstraction manual for 3

12 inpatient/ed charts for information collected). Table 3 compares patient characteristics by abstraction status. Table 3. Characteristics of Patients by Abstraction Status Characteristics Total Abstracted Not abstracted N=70,955 (%) n 1 =3,881 (%) n 2 =67,074 (%) Year: ,948 (43.6) 2,428 (7.9) 28,520 (92.1) ,007 (56.4) 1,453 (3.6) 38,554 (96.4) Age group: 0-9 5,233 ( 7.4) 439 (8.4) 4,794 (91.6) ,715 ( 8.1) 256 (4.5) 5,459 (95.5) ,944 (21.1) 925 (6.2) 14,019 (93.8) ,210 (27.1) 1,207 (6.3) 18,003 (93.7) ,425 (24.6) 789 (4.5) 16,636 (95.5) ,980 ( 9.8) 226 (3.2) 6,754 (96.8) 90+ 1,448 ( 2.0) 39 (2.7) 1,409 (97.3) Primary Payer: Private 20,528 (28.9) 892 (4.4) 19,636 (95.7) Medicaid 11,865 (16.7) 963 (8.1) 10,902 (91.9) Medicare 28,429 (40.1) 1,458 (5.1) 26,971 (94.9) Uninsured 10,133 (14.3) 568 (5.6) 9,565 (94.4) UB-92 diagnosis: 345.x 2,954 ( 4.2) 1,186 (40.2) 1,768 (59.9) ,892 (46.4) 2,566 ( 7.8) 30,326 (922) ,094 (46.6) 112 ( 0.3) 32,982 (99.7) ,015 ( 2.8) 17 (0.8) 1,409 (97.2) Column percent Row percent Cases were selected after stratification by diagnosis category using ICD-9-CM codes of epilepsy (including status epilepticus) (345.x), convulsions (780.3), syncope and collapse (780.2), and acute delirium (293.0). In our evaluation to determine the prevalence of epilepsy from these sources, the following general principles were observed: 1. All characteristics of the sample (sex, race, age, or payer) are comparable with the referent population from which the sample is per diagnosis strata (345.x or 780.3) and calendar year (2001 or 2002). (Appendix C for actual comparisons). A total of 3,983 charts were sampled and reviewed. The accuracy of the charts reviewed is summarized in Table ICD codes of and are listed as independent diagnosis when there is neither a 345.x nor diagnoses. 3. If there is more than one seizure-related code, the code utilized is assigned according to the following hierarchy: 345.x > Data were combined for 2001 and 2002 and annualized. 4

13 5. For duplicate observations in 2001 and 2002, individuals present in both years were unduplicated. If they had both a and a 345.x code, the 345.x visit was retained. Abstracted records were reviewed by trained neurology nurse practitioner and nurses. When information extracted required a second opinion, an adult epileptologist (BW) and a pediatric epileptologist (RT) provided their opinion. Following is the summary of the abstracted HD and ED charts comparing diagnosis assigned in UB-92 and clinical reviewers. Table 4. Accuracy of abstracted HD and ED charts, SC Diagnosis after clinician review Diagnosis UB-92 Epilepsy Status Seizure Syncope Inadequate information Total 345.x 1016 (85.2%) 61 (5.1%) 41 (3.4%) 0 74 (6.2%) 1192 (29.9%) (73.1%) 94 (3.4%) 430 (15.4%) 3 (0.1%) 224 (8.0%) 2791 (70.1%) All 3056 (76.7%) 155 (3.9%) 471 (11.8%) 3 (0.0%) 298 (7.5%) 3983 (100%) Based on the findings of Table 4, 85.2% of 345.x codes, and 73.1% of codes, have evidence of epilepsy. When the proportion of clinically confirmed epilepsy is applied to the referent population, the following annualized frequency is obtained: Diagnosis Proportion Estimated # of Group Frequency after review epilepsy cases 345.x: % 3177*.852 = 2, : 31, % 31,695*.731 = 23,169 Estimated number of HD and ED visits with epilepsy = 25,876 There is 4.8% difference in diagnoses when compared to sample population. If we make the assumption that the inconclusive cases due to inadequate information would follow the same distribution as those records that furnished adequate information and ignore them, the proportion of epilepsy will be 90.9% and 79.5% respectively for 345.x and codes. This latter proportion yields 28,086 persons with epilepsy and could be interpreted as upper range of the estimate. C. Physician Office Visit For data obtained from POV, the sample of records reviewed were 302 records, 9.3% of 3,253 unduplicated encounters. Although the sampled of proportion is nearly twice that of HD/ED sample, the smaller number of patients in the POV setting makes it difficult to conduct subset analysis by various attributes due to 5

14 scanty distribution in some of the cells. Hence, categories were collapsed for payer status and only epilepsy (345x) and seizure (780.3) were identified. Detailed distribution by abstraction status with 95% confidence limits is presented in Appendix D. Unlike the HD/ED data, POV data were not identified by year excepting Medicare data which came from 2001 visits. The POV patients have had no ED/HD encounters for the years in mention. The diagnosis codes were hierarchically ranked in the following order: 345.0, 345.1, 345.4, 345.5, 345.6, 345.7, 345.8, > 345.2, > Table 5 shows the distribution of billing codes after review of the records by the clinical nurse practitioner and nurses, and input of the two epileptologists. Table 5. Accuracy of abstracted POV charts, SC Diagnosis after clinician review Diagnosis UB-92 Epilepsy Status Syncope Inadequate information Total 345.x 202 (91.4%) 1 (0.5%) 10 (4.5%) 8 (3.6%) 221 (29.9%) (91.4%) 0 4 (4.9%) 3 (3.7%) 81 (70.1%) All 276 (91.4%) 1 (0.3%) 14 (4.6%) 11 (3.6%) 302 (100%) For both 345.x and codes, the proportion of the records determined to be epilepsy cases was 91.4%. When these proportions were applied as weighting factor, of the 1,157 patients with 345.x, 1,057 and of the 2,096 patients coded with 780.3, 1,916, total 2,973 were deemed to be epilepsy cases. If the 11 records were ignored, the total number would be 3,086. More detailed sample information is found on page 57. The data analyzed from POV is not generalizable to the general population since the data sources are limited to selected practices and do not include children under the age of 2 years. However, epilepsy in this age group is expected to be small and negligible. To approximate the POV data to the general population of epilepsy patients from physician offices through out the state, we assumed that the prevalence of epilepsy among patients encountered in physician offices elsewhere is approximately comparable to the distribution noted in the sampled records among Medicare, Medicaid and SHP insured patients. Based on state demographic data, we assumed that the sample area coverage is approximately 39% of the state. If these values are extrapolated to the rest of the state, the sample analyzed represents a low of 8,494 (2,973/0.35) and a high of 8,817 (3086/0.35) patients with epilepsy. These numbers are most likely an underestimate until we can obtain the number of SC Medicare patients with a 345.x or diagnosis 6

15 code in 2002 who were not seen as an HD/ED patient during 2001 or Following is a step-by-step calculation of prevalence based on the three data sources. Prevalence for Combined HD/ED/POV Data Average population of the state for = 4,081,794 (Source: US Census Bureau) Individuals with epilepsy in SC: o Lower range: 25,876 HD/ED + 8,494 POV = 34,370 o Upper range: 28,086 HD/ED + 8,817 POV = 36,903 Annualized prevalence estimate of clinically attended (~Active) epilepsy in SC for 2001 and 2002 o Lower range: 34,370/4,081,794= 0.84% o Upper range: 36,903/4,081,794= 0.90% D. Model-based Approach to Estimate Prevalence: this approach utilized the 2001 and 2002 ED/HD data with 70,955 unduplicated observations shown in Table 3. The main research effort of this modeling approach is to determine the utility of routinely available surveillance variables in correctly predicting epilepsy using the case ascertainment differential diagnosis codes of 345.x, 780.3, 780.2, and and covariates. The covariates selected for this modeling approach were seven variables: Demographics (age, sex, race, and payer status) and clinical (UB-92 diagnosis, number of visits during the two years, comorbid conditions frequently associated with seizure disorders). The response variable was dichotomous level of clinically confirmed epilepsy (epilepsy vs. no epilepsy) based on the adjudication of the clinical neurology nurse practitioner and nurses and the two epileptologists on the 3,983 charts reviewed (Table 4). The final analysis relied on 3,881 records after deleting 102 records that were abstracted and reviewed in each year. The covariates selected were first evaluated for their bivariate association with the response and only those with p<0.10 were included in the multivariable logistic regression. Only sex was excluded from the multivariable model due to lack of association. The final model included the variables listed in Table 6 along with the various levels of effect. The beta-coefficients from the model were applied to the observations that were not reviewed (n=67,074) to identify the predicated probability of 7

16 epilepsy for each patient conditional on the covariate values each patient satisfied. We used the following formulae to calculate the probability of epilepsy and its 99% confidence interval for each case: (1) P=exp(α+β*Χ)/(1+exp exp(α+β*χ)) (2) 95%CI= exp(α+β*χ)/(1+ exp(α+β*χ)) +/- 1.96*sd. where: α the intercept β = the vector of mean beta-coefficients from the 100 estimation sets X = the vector of explanatory variables sd = the standard deviation of the distribution of mean predicted probabilities To assess the predictive power of the model, a Receiver Operating Characteristic (ROC) curve was constructed for the validation data (n=3,881). A ROC curve is a graphical representation of the trade off between false negative and false positive rates for every possible probability cut off (for example, the tradeoff if only those with a probability of 6% or higher are defined as likely to have epilepsy). Equivalently, the ROC curve is the representation of the tradeoffs between sensitivity and specificity. The curve shows sensitivity on the Y-axis and one minus specificity on the X-axis. A ROC curve that climbs rapidly toward the upper left hand corner of the graph indicates that the true positive rate is high while the false negative rate is low. When the ROC curve follows a diagonal path from the lower left hand corner to the upper right hand corner, it means that every improvement in false positive rate is matched by a corresponding decline in the false negative rate. The Area Under the Curve (AUC) is a representation of the model s ability to correctly discriminate a pair of true epilepsy and non-epilepsy patients the larger the AUC, the higher the ability of the model to correctly discriminate those who have epilepsy from those who do not. Generally, AUC is considered outstanding discriminatory power, as excellent, as very good, as good, and values less than 0.50 are worse than chance 1. As shown in Figure 1, the sensitivity analysis of our model shows an AUC of 0.75, suggesting very good discriminatory power with the covariates identified (Figure 1). The model fit and predictive power is also strong. Given that the model utilized the most parsimonious set 1 Hosmer DW, Lemeshow S. Assessing the fit of the model. In: Applied Logistic Regression. New York: John Wiley & Sons; 2000:

17 of covariates that are routinely available in administrative datasets, the potential usefulness of the model warrants further consideration in other settings. Table 5. Logistic Regression Parameter Estimates Wald Confidence Interval for Parameters Parameter Level of effect Estimate 99% Confidence Limits Intercept β 0 Baseline Agegp β Reference Agegp β Agegp β Agegp β Agegp β Agegp β Agegp Β Racegp β 20 White Reference Racegp β 21 Black Racegp Β 22 Other Comorbgp β 30 Lo/No risk Reference Comorbgp β 31 Hi-risk Visit β 40 1 Reference Visit β Visit β 42 >= Esdgp (UB-92) β 50 Syncope/Delirium Reference Esdgp (UB-92) β 51 Epilepsy Esdgp (UB-92) β 52 SeizureNos Payer β 60 Private Reference Payer β 61 Medicaid Payer β 62 Medicare Payer β 63 Uninsured Hi-risk comorbid conditions include the presence of mental retardation, psychiatric problems, depression, substance abuse, paralysis, and anemia in the secondary diagnosis field. We calculated the probability of epilepsy for each case in the ED/HD surveillance dataset by applying the parameter estimates. For individual cases, the probability could be written in the following manner. P(D) = {1+ exp ( β 0 + β 10 + β β 62 + β 63 )} -1 9

18 To illustrate how the model parameters work in assessing the probability of epilepsy based on the covariates, the following three examples are provided. Example 1: Patient A is 27 years-old, black, with right hemiplegia, had 6 visits during the year, UB-92 diagnosis was 345.1, is Medicaid insured. The probability that this would turn out to be true epilepsy is: {1+exp-( )} -1 = (98.9%) Example 2: Patient B is 17 years-old, native American, with no comorbid condition, had no previous visit during the year, UB-92 diagnosis was 780.2, is privately insured. The probability that this would turn out to be true epilepsy is: {1+exp-( )} -1 = (12.2%) Example 3: Patient C is 74 years-old, Hispanic, with alcoholism and anemia recorded as comorbidities, had eight visits during the year with seizure episode, UB-92 diagnosis was , is Medicare insured. The probability that this would turn out to be true epilepsy is: {1+exp-( ( ) )} -1 = (89.7%) Based on the estimated probabilities and a cutoff point of 47%, patients A and C are likely to be epilepsy cases while patient B is less likely to be an epilepsy case. Figure 1. Sensitivity analysis for predicting Epilepsy from Administrative Datasets Legend: 1. AUC= Area under the curve--ability of the model to correctly discriminate those with epilepsy from those without. It is the reflection of the C statistics from the logistic output. 2. PoI= Point of intersection (0.695)--the point at which sensitivity and specificity are equal, i.e., a cutoff point where false positives and false negatives are balanced. 10

19 To determine the appropriate cutoff point for the predicted epilepsy, we used the 50 th percentile (41.85) of the true positives cases as the cutoff for probable epilepsy and the 40 th percentile (33.48) as the cutoff for possible cases. While it is customary to use the mean and 1 standard deviation below the mean as the upper and lower range of the cutoff points, the choice of the median is a good substitute when data are not normally distributed, which is the case in our distribution. Based on these cutoff levels the proportions of cases that are highly likely and likely to be epilepsy are shown in Table 7. Table 6. Cutoff points and model-based distribution of epilepsy among HD &ED visits, Cumulative Cumulative Epilepsy Location Cutoff Level Frequency Percent Frequency Percent 1)Probable 50 th % of T.P , , )Possible 40 th % of T.P , , )Unlikely <40 th % of T.P , , Any Epilepsy 40 th % of T.P ,921 Rate = (30,921/4,100,000) =0.7500% For individual prediction of epilepsy, we utilized the model parameters as described earlier and calculated the probabilities across the variables reflected as risk characteristics for each person. Appendix E shows a 0.1% randomly selected sample of records to which such an estimate is demonstrated. Overall, when the UB-92 diagnosis is 345.x, the model s prediction agrees with the clinically confirmed diagnosis in 8 out of 10 cases. Conversely, when the diagnosis is 780.3, the model s prediction agrees with the expert decision in 4 out of 10 cases. This suggests that the prediction has higher sensitivity with a tradeoff on specificity. However, for the purposes of surveillance the current cutoff appears to be adequate. Finally, it is important to note that our effort in developing this model as an alternative to time/labor-intensive and costly review of records is an important step towards improving epilepsy surveillance in the US. We feel there is plenty of room to improve the model and test it in various settings. The global estimate of epilepsy noted from our model is comparable with the approach presented earlier. 11

20 II. Incidence rates Aim Determine the incidence of epilepsy in SC from existing data sources A. Posttraumatic epilepsy we utilized the SC Traumatic Brain Injury Follow-up Registry (SCTBIFR) data to estimate the incidence of epilepsy after TBI. The cohort is comprised of persons aged 15 years and older with TBI randomly selected from the SC statewide non-federal hospital discharge data set over four years (January 1, 1999 through December 31, 2002) and recruited to participate in the follow-up telephone interviews. TBI was defined as any discharge with a primary or secondary diagnosis of trauma to the head associated with decreased consciousness, amnesia, other neurological or neuropsychological abnormalities, skull fracture, or intracranial lesion, in accordance with the CDC case definition of TBI. During the recruitment period 4,519 persons were discharged alive. At time of first interview, 3,746 persons (82.9%) were alive and eligible to participate. 2,118 (56.5%) of these were both able to be located and agreed to participate for the first interview one year after their discharge. This cohort was used to examine the relationship between TBI and epilepsy after injury. During the interviews, individuals were asked about the presence of seizures or epilepsy, both before and after their TBI. As detailed in Table 8, initially the questions were less sensitive to the presence of epilepsy after TBI, since an individual with epilepsy, but no recent seizures, might respond negatively. The questions were later changed to be more sensitive and a few were added to gain additional information. Table 8. Epilepsy-related questions used for initial identification of epilepsy cases. 1: Before your injury, did a doctor ever tell you that you had a seizure disorder or epilepsy? 1=Yes 2=No Used for first 1972 interviews: 2: During the past 4 weeks, have you had seizures or epilepsy? 1=Yes 2=No Used from interview number 1973 onward in place of #2 above: 2a: Since your injury [since the last time we talked to you], has a doctor told you that you had developed a seizure disorder or epilepsy? 1=Yes 2=No 2b: Are you currently taking any medicines to control your seizure disorder or epilepsy? 1=Yes 2=No All questions have response options of refused, don t know, and not applicable. 12

21 If an individual had an ICD-9-CM discharge diagnosis code for epilepsy (345.x) or seizure (780.39) at discharge, or if they responded positively to the epilepsy-related questions during one of their interviews, their TBI charts were re-abstracted for additional information pertaining to seizures or epilepsy. Information gathered from chart re-abstraction included evidence of previous history of seizures or epilepsy, antiepileptic drugs (AEDs) prescribed prior to admission and those prescribed at discharge, whether the TBI was a result of a seizure, whether a seizure occurred after the TBI and if so when. The chart re-abstractions were completed prior to the completion of all followup interviews, however, and 30 additional potential cases of posttraumatic epilepsy (PTE) were identified after interview completion. These cases were not re-abstracted for additional information, but were evaluated in regard to seizures based on only the original abstraction and interview information (Figure 2). Due to changes in questions and skip patterns, we were not always able to determine the year of epilepsy onset with certainty. Such cases were assigned the year of epilepsy 13

22 diagnosis. In analyzing incidence, we used epilepsy onset at anytime during the three years following TBI discharge as our outcome. Both interview and medical record information were used to determine the presence of epilepsy prior to and after TBI (see Figure 2). Responses to the questions in Table 8, and re-abstraction information (when available), were used to determine initial categorization. After such determination, all cases with some indication of seizures or epilepsy had all available information reviewed individually by an epidemiologist and by a certified pediatric nurse practitioner who specializes in epilepsy to determine final epilepsy categorization. Any questionable cases were discussed by the entire team, including one or both of the epileptologists. Individuals with pre-existing epilepsy and those with uncertain status were excluded from all analyses of PTE. The incidence rate of PTE during the three years following TBI was calculated based on aggregate data taking into account those individuals not completing three years of interviews, and confidence intervals (CI) were calculated using tabulated values for a Poisson-distributed variable. Incidence of PTE was also calculated stratified by head injury severity using Abbreviated Injury Scale scores, categorized into mild, moderate, and severe. Possible factors involved in developing PTE were analyzed using Poisson regression, with a scale parameter estimated by the square root of Pearson s chi-square divided by the degrees of freedom. The independent variables were derived from initial chart abstraction, chart re-abstraction, and first interview. Independent variables that showed no significant association with the dependent variable on chi-square analyses (p>0.10) were excluded from the initial regression model. Fisher s Exact Test was used in place of chi-square when appropriate. Outcome variables were PTE and no epilepsy. Unlike the incidence rate calculation above, since this analysis involved examining individual characteristics related to outcome, a conservative approach was used and study participants included in the analyses who did not complete all three years of interviews, and who did not report epilepsy prior to their last interview, were assumed to have an outcome of no epilepsy. Table 9 shows information on the independent variables. The Cochran-Armitage test for trend was used to look for trends in outcome across ordinal variables. 14

23 Diagnostics were used to examine the appropriateness of the model. Variance inflation factors of the independent variables were calculated in an equivalent linear regression model to check for multicollinearity. The age category 55+ was inflated, but since this category was necessary in the model it was retained. Variables suspected of interaction were checked, and none was found. Deviance and Pearson chi-square statistics showed adequate fit with no evidence of overdispersion. Since there was attrition in the cohort, those completing three interviews were compared by chi-square to those who did not, to look for differences. Of 3,746 eligible persons, 2,118 (57%) were both located and participated in the first year interview. Of first year participants, 1,536 (72.5%) participated in the 2 nd year interview, and 1,173 (55.4%) participated in the 3 rd year interview. ICD-9-CM codes and interview responses were used to identify 325 potential cases with seizures or epilepsy, and 241 (74%) of their charts were re-abstracted. A total of 115 individuals were determined to have developed PTE in the three years following discharge for TBI. The incidence of PTE in the three years was per person-year, which is equivalent to 2.6 cases per 100 person-years (95% CI 2.1, 3.1). Incidence by head injury severity was 1.2 cases per 100 person-years (95% CI 0.7, 1.8) for mild, 2.2 per 100 person-years (95% CI 1.3, 3.6) for moderate, and 3.9 per 100 person-years (95% CI 3.1, 4.9) for severe. See Appendix F (posttraumatic epilepsy incidence analysis) for more detail on the analysis. Table 10 shows the characteristics of the cohort by epilepsy status. In this analysis, 834 individuals who did not complete all three annual interviews, if they did not report epilepsy prior to withdrawal, were assumed to have an outcome of no epilepsy. Compared to individuals who did not develop PTE, the group with PTE has higher proportions of individuals who are middle-aged, male, Medicaid recipients, have severe TBI, early PTS, have sustained their injuries from violence, have no other injuries, have three or more comorbid conditions, and have a history of a previous head injury, stroke, or depression. Variables in which no significant difference was seen included race, trauma level status of hospital, and pre-tbi education, income, and substance abuse. It should be noted that pre- TBI income was unknown for 7.8% of the cohort, but did not differ between the outcome groups. 15

24 Table 9. Independent variables evaluated for the regression model. Variable and Source Age Discharge dataset Sex - Discharge dataset Ethnicity - Discharge dataset Education Interview report Income Interview report Insurance - Discharge dataset Severity of TBI Discharge dataset Early posttraumatic seizure Re-abstraction information and any information that individuals may have volunteered during interview Etiology of TBI Discharge dataset and original abstraction Number of concomitant injuries Discharge dataset Number of comorbid conditions Discharge dataset Trauma level status of hospital Discharge dataset Previously knockedout/unconscious Original abstraction and interview History of stroke Interview report History of depression Interview report and discharge dataset History of substance abuse Discharge dataset, original abstraction, and interview Additional information on variable At time of injury Prior to TBI In the year prior to TBI Insurance status at time of discharge was grouped into the following categories: uninsured, Medicare, Medicaid plus other indigent programs, and private insurance, which included commercial insurance, Champus, Worker s Compensation (WC), other agencies, and unknown. Commercial insurance made up 87% of the private insurance category. Unknown was 1% of the total cohort. Based on AIS (Association for the Advancement of Automotive Medicine, 1990) score for the head. Assigned by ICDMAP-90 software (Center for Injury Research Policy of the Johns Hopkins University School of Public Health, 1997) based on ICD-9-CM codes. Defined as any seizures within the first month, or if time was unknown, during the acute hospitalization. Violence category includes both with and without the use of weapons. Sports/other/unknown category is 54% other and 18% unknown. Based on ICD-9-CM codes. Based on ICD-9-CM codes and Elixhauser et al s categories.(elixhauser et al. 1998) Interview report of prior episodes of being knocked out or unconsciousness and/or abstraction information on previous TBI. Having been told by a doctor that they had a stroke prior to their TBI. In less than 1% of cases were previous head injury or stroke unknown, and these were grouped in with those categorized as negative for those conditions. Prior to, or at time of discharge from, TBI. Interview report ( Before your injury, did a doctor ever tell you that you had depression? ) and/or ICD-9-CM code 296.2, 296.3, 300.4, or 311 (the latter code, for depressive disorder, was the most common of the 4 codes, representing 72% of individuals with one of these codes). 16% had an ICD-9-CM code and 84% were self-report only. A total of 95% asserted they had been told by a doctor before their injury that they had depression. Prior to, or at time of discharge from, TBI. Based on ICD-9-CM codes 303 through 305 (excluding 305.1), information in chart, and interview questions on drug and alcohol use. 16

25 Table 10. Comparison of characteristics in cases with and without posttraumatic epilepsy. Posttraumatic No known Chi-square Characteristic epilepsy epilepsy p-value (N=115) (N=1846) Age : Gender : Female Male Race: Nonwhite White Education: <HS grad HS grad >HS grad Income: <$20,000 $20-34,000 $35,000+ Unknown Insurance: Uninsured Medicaid/indigent Private/Other Medicare TBI Severity: Mild (AIS=2) Moderate (AIS=3) Severe (AIS=4,5) Early PTS: None known Yes Mechanism: Transportation Fall Violence Other/Unknown Multi Trauma: None 1-2 >=3 No. of comorbidity: None 1-2 >=3 Trauma level: Undesignated Hx. unconsciousess: No Yes Hx. of stroke: No Yes Hx. of depression: No Yes Hx. of substance abuse: No Yes 27 (23.5%) 55 (47.8%) 33 (28.7%) 36 (31.3%) 79 (68.7%) 32 (27.8%) 83 (72.2%) 36 (31.3%) 43 (37.4%) 36 (31.3%) 57 (49.6%) 34 (29.6%) 17 (14.8%) 7 (6.1%) 13 (11.3%) 28 (24.4%) 52 (45.2%) 22 (19.1%) 21 (18.3%) 16 (13.9%) 78 (67.8%) 93 (80.9%) 22 (19.1%) 51 (44.4%) 39 (33.9%) 19 (16.5%) 6 (5.2%) 69 (60.0%) 20 (17.4%) 26 (22.6%) 51 (44.4%) 46 (40.0%) 18 (15.7%) 64 (55.7%) 14 (12.2%) 25 (21.7%) 12 (10.4%) 76 (66.1%) 39 (33.9%) 102 (88.7%) 13 (11.3%) 77 (67.0%) 38 (33.0%) 49 (42.6%) 66 (57.4%) 642 (34.8%) 600 (32.5%) 604 (32.7%) 740 (40.1%) 1106 (59.9%) 430 (23.3%) 1416 (76.7%) 613 (33.4%) 608 (33.1%) 614 (33.5%) 943 (51.1%) 441 (23.9%) 316 (17.1%) 146 (7.9%) 210 (11.4%) 242 (13.1%) 949 (51.4%) 445 (24.1%) 744 (40.3%) 306 (16.6%) 796 (43.1%) 1809 (98.0%) 37 (2.0%) 1028 (55.7%) 522 (28.3%) 144 (7.8%) 152 (8.2%) 837 (45.3%) 470 (25.5%) 539 (29.2%) 1069 (57.9%) 683 (37.0%) 94 (5.1%) 900 (48.8%) 273 (14.8%) 430 (23.3%) 243 (13.2%) 1387 (75.1%) 459 (24.9%) 1735 (94.0%) 111 (6.0%) 1479 (80.1%) 367 (19.9%) 895 (48.5%) 951 (51.5%) <.001 < <

26 Table 11 shows the risk ratios of the independent variables and their 95% confidence intervals from the multivariable Poisson Regression. Results show that after adjusting for all the other variables in the model, individuals more likely to develop PTE had an early PTS, a severe head injury, three or more comorbid conditions, depression prior to or at the time of their TBI, and/or Medicaid health care coverage at time of TBI compared to individuals with the respective referent characteristics. In those variables mentioned above with multiple categories, the other categories did not show a significant impact on risk of PTE. For instance, moderate head injury, being insured by private/worker s Compensation(WC)/Champus/other/unknown insurance, being uninsured, and having one to two comorbid conditions were not significantly related to risk of PTE when compared to the reference categories. The model was also run with private/wc/champus/other/unknown as the reference category for insurance. Individuals with Medicaid or other indigent insurance showed 2.31 (95% CI 1.38, 3.89) greater risk than those with private insurance. There were significant trends of increased risk of PTE with increasing head injury severity (p>.001) and an increasing number of comorbid conditions (p<.001). There was no significant association noted for the remaining variables. Chi-square analyses compared length of participation in the interviews by characteristics to calculate differential rates of attrition. A larger proportion of individuals developing PTE participated all three years (p<.001), with 55% of individuals not developing epilepsy completing all three years, and 70% of those with PTE completing all years. However, chi-square analyses showed no differences between length of participation when compared to severity of head injury (p=.891), or any other of the independent variables except insurance status (p<.001) and education (p=.047). Individuals participating all three years were more likely to have private insurance and to have post high school education. The risk of PTE could be falsely inflated if individuals who were more likely to develop PTE were more likely to participate all three years. However, since there was no difference in participation by severity or most of the other variables, it would appear more likely that the longer individuals remained in the cohort, the more likely we were to identify PTE. This, together with all individuals not 18

27 developing PTE prior to their leaving the cohort early being given an outcome of no epilepsy, might have reduced the strength of the actual risk relationships. Table 11. Risk Ratios of PTE in first 3 years after TBI for 1961 individuals Risk Ratio (95% CI) Characteristics (Reference level) Age (Ref=15-29 years) (0.96, 2.77) (0.54, 2.63) Gender (Ref=Female) Male 1.36 (0.85, 2.17) Insurance (Ref=Medicare) Medicaid or other indigent Uninsured Private/WC/Champus/other/unknown Severity of TBI (Ref=Mild, AIS=2) Severe, AIS = 4 or 5 Moderate, AIS = (1.50, 8.25) 1.98 (0.75, 5.19) 1.52 (0.71, 3.26) 2.41 (1.39, 4.17) 1.70 (0.82, 3.50) Early posttraumatic seizure (Ref=None known) Yes 6.52 (3.81, 11.17) Mechanism of injury (Ref=Transportation) Violence Fall Other (incl sports)/unknown Number of concomitant injuries (Ref =3+) None 1-2 Number of comorbid conditions (Ref=0) (0.89, 3,13) 1.05 (0.61, 1.83) 0.61 (0.24, 1.60) 1.26 (0.73, 2.18) 0.82 (0.42, 1.59) 3.14 (1.57, 6.28) 1.22 (0.76, 1.96) Previously knocked-out/unconscious (Ref=No) Yes 1.30 (0.83, 2.03) History of stroke (Ref=No) Yes 1.83 (0.88, 3.80) History of depression (Ref=No) Yes 1.86 (1.17, 2.96) Most studies have focused on clinical factors directly related to the TBI to determine the risk of PTE. This study differs by including limited clinical factors related to the brain injury, but a number of demographic, socioeconomic, and clinical factors occurring prior to the TBI. In concordance with much of the literature, there was an increased risk of PTE with increased severity. 19

28 Our study showed early PTS increased the probability of epilepsy more than any other factors. Our definition of early was liberalized to include seizures within a month of injury, or if time was unknown, anytime during the acute hospitalization. Since there was no specific interview question concerning early seizures, and not all charts were reabstracted for additional seizure and epilepsy information, it is possible that we did not identify all cases of early seizures. Choice of charts for re-abstraction was based on the presence of epilepsy or seizure discharge codes, or a positive reply to pre- or post-tbi epilepsy, with most of the early PTS identified through re-abstraction information. The code had been used most often in our cohort for pre-existing epilepsy and/or for early PTS. If there were a large number of cases in which was not used for early PTS, there could be a bias toward identifying early PTS in those developing PTE, resulting in an inflated association between PTE and early PTS. Unfortunately, while we know that was often utilized for seizures other than early PTS (ie, pre-existing epilepsy or seizures that caused the TBI) we do not know how often early PTS was not given a code. Transportation-related injuries were the most common mechanism in our TBI cohort. In our cohort, falls and violence had the highest proportions of severe head injuries, and transportation had the greatest proportion of mild head injuries. Mechanism was not significantly related to risk of PTE. In our analysis there was no association between the development of PTE and reporting prior episodes of being knocked out or unconscious, or prior substance abuse. Since alcoholism and the prior occurrence of TBI are more prevalent in individuals with TBI, and both alcoholism and TBI are related to epilepsy, it could be argued that including individuals with these characteristics in our cohort may have increased the incidence of PTE. Ultimately we found no significant relationship between the development of PTE and either characteristic. We were limited in the amount of information we collected on previous substance abuse. Substance abuse included both alcohol and illicit drugs, and was based on ICD-9-CM codes at TBI discharge, self-report and abstraction information on alcohol or drug treatment, self-report on illicit drug use, and self-report on current drinking with comparison to pre-tbi drinking (using the COMBI definition of alcohol use ( 20

29 Since there was no specific measure of alcohol use prior to TBI, some individuals with prior alcohol abuse may have been missed. Individuals identified as having depression in our cohort were almost twice as likely to develop PTE. Although the majority of these individuals were identified solely through self-report of history of depression (84%), this provides further evidence of depression as a risk factor not only for epilepsy in general, but also for epilepsy after head injury. Individuals in our study showed both an increased risk of PTE with three or more comorbid conditions at discharge, and a trend of increasing proportion of PTE with increased number of comorbid conditions. Since stroke can be a predecessor to epilepsy, especially in older people, we included it as a separate variable. However, people reporting that they had been told they had had a stroke prior to their head injury did not show an increased risk of developing PTE. In addition, neither number of concomitant injuries nor trauma level status of the hospital showed an association with risk of PTE. In our cohort of TBI patients, no significant difference in risk of PTE between age categories was found. It must be noted that among the 4,519 people with TBI eligible for the study, 382 (8.5%) died prior to their first year anniversary of discharge. Of the deceased, 80% were 55 years and older, while that age group made up only 33% of the eligible population. Older persons were more likely to die in the first year, likely removing more seriously injured individuals who would have been at higher risk for PTE, and thus possibly lowering the reported risk of PTE in that age group and in the cohort. Our study found no difference in risk of PTE by race, either before or after adjusting for income, as well as no difference in risk of PTE by gender. There was no difference in the development of PTE by pre-tbi income or education. Interestingly, individuals with Medicaid or other indigent assistance on discharge from their TBI had a significantly increased risk of developing PTE over individuals with Medicare or some form of private health insurance. It is not known whether SES, quality or continuity of health care, or some other factor is responsible for this relationship. The relationship to insurance status remained significant when adjusted for both pre-tbi income and trauma level status of the acute care hospital. It is possible that those 21

30 individuals requiring long-term hospitalization became Medicaid recipients prior to their discharge. Some limitations of this study have been previously mentioned. Most important is the reliance on self-report of seizures or epilepsy status, as well as pre-tbi clinical variables. Individuals could have erroneously reported the occurrence of epilepsy, stroke, depression, substance abuse, or previous TBI, or have underreported them, since we had corroborating clinical information in only some cases. It is possible that individuals who only answered yes to the interview question regarding taking medicine for epilepsy were on medication prophylactically after their TBI. However, this would seem unlikely because of the fairly long span of time between discharge and the beginning of interviews. While recognizing these limitations, our study has the advantages of being population-based, including large numbers, demographic heterogeneity, three years of follow-up, and representing all degrees of severity seen among hospitalized TBI. It also identifies characteristics present either prior to or concurrent with injury associated with the later development of PTE. While confirming some risk factors established by other studies, such as early PTS and severity of head injury, our study has identified other associations with PTE especially depression, the presence of three or more comorbid conditions, and Medicaid insurance that are less well established and warrant further research. Information on such associations can be used to better predict those at increased risk of PTE, and may eventually enable early interventions to reduce this risk or its consequences. B. Incidence of epilepsy utilizing ED and HD data A main difficulty in determining epilepsy incidence is determining the onset of epilepsy. Rarely can a first seizure indicate epilepsy. In general, this only occurs when an individual has a type of epilepsy with a distinctive EEG pattern (for example, infantile spasms where the seizure and EEG findings (hypsarrythmia pattern) are specific to the epilepsy syndrome, or absence epilepsy where the seizure and EEG findings (3 per second spike and wave pattern) are specific to the epilepsy syndrome). We decided to take all cases in which the clinician review indicated a new seizure or new epilepsy case or questionable diagnosis, or if it 22

31 was a new case but the final diagnosis was questionable, and ask ORS to follow them forward in time, looking for any additional diagnoses of or 345.x. Below are the numbers of new cases. Inpatient/ED charts abstracted: 2001 N=2530, 2002 N=1453 New onset cases, inpt/ed, epilepsy or seizure: 2001 n=307, 2002 n=124 New onset cases, inpt/ed, questionable final dx: 2001 n=8, 2002 n=1 Physician office visit (POV) charts abstracted (2001 & 2002): N=302 New onset cases, POV, inpt/ed: 2001 n=8, 2002 n=3 New onset cases, POV, questionable final diagnosis: none A total of 451 cases were identified as new onset cases. Seven of the POV cases were Medicare, and ORS was unable to follow them forward since they do not have any other years of data for Medicare. The other four cases would have involved enlisting two other personnel (one from State Health Plan and one from Medicaid), and it was deemed that the time and expense in tracking just these four would be prohibitive. However, it does not seem unlikely to assume that most individuals with a new onset seizure would likely be seen in an ED or as an inpatient, rather than in a physician s office. In the case of more than one diagnosis code after clinician review, we assigned a code hierarchically: 345.x > > 345.2, > > > The following 440 cases were considered new onset after clinician review: Dx. After review Year Frequency? ? Epilepsy Febrile Febrile Seizure Seizure Status Status The above cases were sent to ORS to follow them forward in time for later or 345.x diagnoses. Of note, there were an additional 298 cases in the database in which new onset could not be determined. Most of these (66%) also had questionable final diagnoses. 23

32 For the purposes of determining epilepsy incidence, a case was considered epilepsy if they had two diagnoses of and/or 345.x, with onset considered the initial seizure. There were 4 cases in which the clinical reviewers had previously assigned or confirmed a diagnosis of new onset epilepsy. Three of these four cases showed subsequent seizures. The one without any subsequent seizures recorded at ORS was removed as a new onset epilepsy case for this analysis. Febrile seizures (780.31) were not counted as seizures. If, however, an individual with a diagnosis had at least two subsequent non-febrile seizure diagnoses, they were counted as a new onset case of epilepsy, with the onset as the year of the first febrile seizure. There were six such cases. The following analysis includes only inpatient and ED cases. 1. ALL INCIDENT CASES FROM SAMPLE By diagnosis assigned after clinician review, and year: Year=2001 Cumulative Cumulative ASSIGNED CODE Frequency Percent Frequency Percent EPILEPSY SEIZURE STATUS Year=2002 Cumulative Cumulative ASSIGNED CODE Frequency Percent Frequency Percent FEBRILE SEIZURE STATUS By original diagnosis and year: Year=2001 Cumulative Cumulative ORIGINAL CODE Frequency Percent Frequency Percent EPILEPSY SEIZURE STATUS Year=2001 Cumulative Cumulative ORIGINAL CODE Frequency Percent Frequency Percent EPILEPSY SEIZURE STATUS

33 2a. TOTAL EPILEPSY SAMPLE PER ABSTRACTION DATA By original diagnosis and year: Year=2001 Cumulative Cumulative ORIGINAL CODE Frequency Percent Frequency Percent EPILEPSY FEBRILE SEIZURE STATUS Year=2002 Cumulative Cumulative ORIGINAL CODE Frequency Percent Frequency Percent EPILEPSY FEBRILE SEIZURE STATUS b. TOTAL EPILEPSY SAMPLE PER ORS (included status as epilepsy, and febrile as seizure) By code and year: Year=2001 Cumulative Cumulative CODE Frequency Percent Frequency Percent X Year=2002 Cumulative Cumulative CODE Frequency Percent Frequency Percent X TOTAL EPILEPSY POPULATION (included status as epilepsy, and febrile as seizure) By code and year: Year=2001 Cumulative Cumulative CODE Frequency Percent Frequency Percent X Year=2002 Cumulative Cumulative CODE Frequency Percent Frequency Percent X

34 The original plan was to choose 35% of 345.x codes (epilepsy), 5% of codes (convulsions), 1% of codes (syncope & collapse), and 5% of codes (acute delirium) for In 2002 we wanted to have a total abstraction of approximately 1500 charts, and inflated that number upward to adjust for expected 15% unlocated charts. As seen further on, these percents were not exact, possibly due to what charts were able to be located, change in personnel, as well as the need to determine final percents on one diagnosis rather than the 2 or more that were sometimes present. In pulling the sample, ORS allowed those with more than one seizure-related diagnosis (ie, and 780.2) to go into more than one group for pulling the sample. When sending us the population from which the sample was pulled, we requested ORS assign one diagnosis to each case, using the hierarchy 345.x>780.3>780.2>293.0, which is the same one used in assigning diagnoses to the abstracted cases. I believe that this most likely inflated the proportions of the higher diagnoses (345.x and 780.3) since they would have priority in labeling. In 2001, 2.1% of the cases had 2 seizure-related diagnoses, and in 2002, 3.0% had 2 seizure-related diagnoses. While ORS was able to match the individuals back to the appropriate individuals in each year, the personnel putting together the population dataset was different from that which pulled the original sample, and they had difficulty matching cases to the exact visit, resulting in some variation in diagnoses and payers. Also of note, in addition to the 11 physician office visits that could not be followed forward, there were 7 HD/ED cases that ORS personnel could not match back to the database so they was unable to follow them forward. Also, there were an additional 3 HD/ED cases in which the diagnoses from ORS did not match the original diagnoses sent in the abstraction sample, so those were not followed forward. Together, that is a total of 21 cases which were new onset but were not able to be followed to determine whether they had additional seizures. Finally, individuals may have moved out of SC or received ED or inpatient care outside of SC between the year of the abstraction and 2005, and thus additional episodes of seizure could have been missed. If any of these cases became epilepsy, it would mean our estimate is an under estimate of the true incidence of epilepsy. 26

35 Of the 10 HD/ED cases that could not be matched, all had original diagnoses of Eight of them were considered after clinical review to be correct, and two were reassigned codes of since their ages were 10 and 74 years. Eight were 2001 cases and 2 were 2002 cases. The incorrectly coded cases were both from Of the 11 physician office visit cases, there were 3 cases from 2001 all of which were originally coded as seizure, and were considered to be correct after clinical review. The other eight cases, five from 2001 and three from 2002, were all originally coded as epilepsy, but were considered to be seizure cases after clinical review. There were slightly fewer epilepsy diagnoses in 2002 than in 2001 (1589/1723=92%), and there were slightly more seizure-related diagnoses in 2002 than in 2001 (19612/18749=105%). Because the sample reflects the population of cases with epilepsy and seizure diagnoses, we did not weight numbers when applying the sample incidence to the population. Therefore, total new cases of epilepsy in 2001 are 47 with 345.x diagnoses, and 1030 with diagnoses. Total new cases of epilepsy in 2002 are 60 with 345.x diagnoses, and 726 with diagnoses. In 2001, 2.7% of 345.x cases and 5.5% of cases became epilepsy within the next 4 years. In 2002, 3.8% of 345.x cases and 3.7% of cases became epilepsy within the next 3 years. Population information was acquired from SC Statistical Abstract (Table 23), 1990, 2000 and US Census Bureau, State Population Estimates 2, 3. Using 345.x (epilepsy & status) and (seizures, including febrile) categories: Inpt/ED population, 2001: 345.x = 1723; = 18,749 Inpt/ED sample per abstraction, 2001: 345.x = 764; = 1766 [Inpt/ED sample per ORS, 2001: 345.x = 762; = 1690] Inpt/ED incident cases, 2001: 345.x = 21; = 97 *2001 cases have at most 4 years of follow-up (through 2005) 2001 sample was 44% of 345.x cases & 9% of cases. Inflating incident cases to population results in 47 cases 345.x and 1030 cases Inpt/ED population, 2002: 345.x = 1589; = 19,612 Inpt/ED sample per abstraction, 2002: 345.x = 427; =

36 [Inpt/ED sample per ORS, 2002: 345.x = 488; = 913] Inpt/ED incident cases, 2002: 345.x = 16; = 38 *2002 cases have at most 3 years of follow-up (through 2005) 2002 sample was 27% of 345.x cases & 5% of cases. Inflating incident cases to total population results in 60 cases of 345.x and 726 cases If NO adjustment for age, sex, or race, using the 2000 SC Census population, result would be the following: 2001: 1077/4,012,000 = = 0.27 per 1000 people incident cases of epilepsy 2002: 786/4,012,000 = = 0.20 per 1000 people incident cases of epilepsy III. Causes (triggers) of seizures and etiologies of epilepsy Aim Determine the underlying causes and etiologies of epilepsy in South Carolina. In abstracting the data from charts, we asked the abstractors to note any information on what caused seizures (i.e., triggers ) and the etiology of epilepsy. As part of the skill building instruction to abstractors, differences between these two concepts were presented, however we had to acknowledge that this distinction can be difficult for someone without a clinical background in epilepsy, and that there can be an overlap, such as the person who experiences a head injury and an immediate seizure, and then goes onto develop epilepsy, or the person with alcoholism and epilepsy. As shown in Appendices A&B, we had specific causes individually listed (injury, illness, fever, lack of sleep, pregnancy, eclampsia, alcohol use, drug use, change in medication, weight gain in children, noncompliance with medication) to try to prevent them from missing any that might be mentioned in the chart. We provided a place for narrative for the abstractors to write any information on causes and etiologies (ie, Brief description of current circumstances that might have contributed to this seizure or seizure-like episode (what provoked this seizure?), and Any past illness, condition, or injury in the patient s history that initially caused the seizure disorder/epilepsy ). The initial results for cause and etiology had a high proportion of cases in which there was no information. For instance, in the inpatient/ed abstracted data, only about 20% of cases had an etiology listed under the actual etiology variable. However, in reviewing the results of the chart abstractions, we noted that there were occasions when the 28

37 abstractors appeared to mix up cause and etiology, and times when a cause or etiology was noted among the text in other variables. In an attempt to use all abstracted information, we have had our pediatric nurse practitioner (GS) who specializes in epilepsy begin reviewing each case individually. If there are triggers that are mentioned in the text but not checked off in the categorical variables, then that is corrected. In addition, we developed a list of categories of etiologies, and she is categorizing text from the etiology text variable, as well as any possible etiology mentioned elsewhere in the abstraction. Since there are over 4,000 cases, this has involved a large time commitment. At present writing, she has completed reviewing 2,000 (50.2%) of the inpatient/ed cases, and intends to complete all cases as time allows, including physician office visit cases. The following analyses on the inpatient/ed data are from the 2000 completed cases, and are limited to those cases determined to be epilepsy after clinician review (n=1,476). When characteristics of age group, sex, race, and primary payer are compared, the records reviewed to date (nearly 50%) are reflective of the total group of epilepsy cases. In reviewing the abstracted data, information that could be used to infer possible etiologic/causal factors were noted to be in multiple areas. Diagnoses were used to assist in understanding causes of the seizure for that visit as well as etiologies for epilepsy, but it should be noted that not all diagnoses were always coded in that section. Instead, diagnostic information could also be found in chart histories and exams that were quoted by the abstractors as additional information deemed as important. The information given often was not complete enough to note if a condition was the etiology or a co-morbid condition of the epilepsy. (The etiologies list was modified and developed from the Sander s article on The epidemiology of epilepsy revisited. Please see Table 12 below.) If such information was present, it was taken into consideration. For example, occasionally a chart would note pre-existing diagnosis of epilepsy but a new onset cerebrovascular accident (CVA) occurring at the hospital visit being reviewed. If this occurred, care was taken to avoid identifying the CVA as the etiology of the epilepsy. However, if CVA was merely listed without any temporal description, an inclusive approach was taken, and it was included under CVA. There are known reasons for the development of secondary epilepsy like Alzheimer s, substance abuse (although debated), head trauma, brain tumors, AIDS, secondary metastasis to the brain, lupus, etc. 29

38 There are also diseases/disorders where the development of secondary epilepsy appears to occur frequently whether as part of the disease process or treatment, for example, diabetes, asthma, autoimmune disorders, other cancers, etc. There is also new research proposed that may identify the increased occurrence of a co-morbid condition, like depression or anxiety, with epilepsy due to similar pathophysiology. Therefore, all of these conditions were noted as possible etiologies to be considered. If not specifically noted as a separate etiology, they were coded as other disease processes. Table 12. Potential Etiologies of Epilepsy per Sander. A. Childhood, adolescence and early adulthood 1. Congenital 2. Developmental 3. Genetic 4. Prenatal/perinatal/postnatal factors 5. Febrile seizures 6. Neonatal seizures (not assigned as data do not identify age <12 months) B. At any time 7. Head trauma 8. Central nervous system infections 9. Tumors (brain) 10. Substance abuse 11. Other disease process (Alzheimer s, AIDS, autoimmune, other cancer, cancer metastases, diabetes, asthma, depression/anxiety renal, etc.) C. Elderly 12. CVA D. Certain environments 13. Endemic infections (malaria, neurocysticerocosis, paragonomiasis & toxicariasis) E. Family 14. Relative with epilepsy F. Unknown/not applicable 15. Unknown etiology 16. No identified epilepsy Based upon Sander, JW. (2003). The epidemiology of epilepsy revisited. Current Opinion in Neurology. 16,

39 Other concerns in using the modified epilepsy etiologies list from Sander include identifying neonatal seizures and febrile seizures. The data from ORS did not identify the specific age of an infant between 0 and 11 months. This meant neonatal seizures could not be identified. Also, this may skew the febrile seizure data, as per our protocol all children under 6 years of age were considered to have febrile seizure disorder unless other data was presented which identified another disorder that would rule out febrile seizures, such as cerebral palsy, infantile spasms, etc. Those children younger 6 months were not identified so all children listed as 0 years were given a febrile seizure diagnosis unless other information is included that disputed otherwise. Based on our chart review, 1,430 unduplicated individuals that were determined to have epilepsy had been assessed to identify the etiology of epilepsy. In some cases more than one etiology may be listed for an individual making the total over 100%. Most commonly, the etiology is either unknown, or is not stated in the chart (see Figure 3 below). The next most common, is other disease processes. As mentioned above, etiology is loosely defined, allowing what could be comorbid conditions to be stated as etiology. UNKNOWN/NOT STATED OTHER DISEASE PROCESS SUBSTANCE ABUSE CVA HEAD TRAUMA DEVELOPMENTAL NATAL FACTORS RELATIVE W/EPILPESY CONGENITAL TUMORS(BRAIN) CNS INFECTIONS GENETIC FEBRILE SEIZURES 7.6% 6.7% 4.8% 3.8% 2.8% 2.4% 2.2% 0.8% 0.8% 0.6% 13.9% 28.5% 37.5% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% Figure 3. Etiology categories of inpatient/ed epilepsy cases Any possible causes of seizures for a visit were identified from the narrative, diagnoses, and other variables listed as possible causes. If a person presented with a new 31

40 onset seizure after head injury, both the new onset seizure and head injury were listed to increase the information gleaned from the chart review. In addition, if a person with known epilepsy had no identified cause, the variable of breakthrough seizure was used. The various causes of seizure included in our chart reviews are shown in Table 13. Table 13. Complete list of cause of seizure AED above therapeutic level Accidental near drowning Accidental poisoning Anorexia Anxiety Breakthrough seizure CVA Dehydration Drug overdose Electrolyte imbalance Fever Head injury Herbal ingestion Illness Injury Job injury Medication changes Medication reaction Menses MVA N/A (no seizure related to visit) New onset seizure Noncompliance Postpartum < 1 month Pregnancy Respiratory distress Seizure versus pseudoseizure Shunt failure Sleep deprivation Stress Structural brain issue Substance abuse versus using alcohol and/or drugs Subtherapeutic AED level Suicide attempt TIA Unknown VEEG admit VNS (vagal nerve stimulator) placement VNS replacement 32

41 In analyzing the immediate seizure causes, cases that were abstracted in both 2001 and 2002 were retained, since seizures may be triggered by different factors at different times. In this subset, there were 44 cases with data from both years. 542 of the visits did not involve a seizure, so these were removed, leaving analysis on 952 seizures that occurred in 928 individuals. More than one cause was allowed making the total more than 100 percent. The top causes (those >1% in frequency) are listed below in Figure 4. The most common cause is a breakthrough seizure, which indicates the cause is unknown. That is followed by illness, which indicates a current or recent illness. The next three causes could be considered preventable: noncompliance, subtherapeutic antiepileptic medication level, and substance abuse. They could be prevented with intervention. There are other causes on the list that could be affected by intervention, and include antiepileptic levels above a therapeutic level, sleep deprivation, and injury. Counting just the six causes mentioned, over 50% of these epileptic seizures could possibly be prevented with appropriate treatment, monitoring, and counseling. Medication changes included only changes to antiepileptic medications, and a video electroencephalogram admission (VEEG admit) indicated someone admitted for a VEEG who may have had seizures during the course of the procedure. BREAKTHROUGH SEIZURE ILLNESS NONCOMPLIANCE SUBTHERAPEUTIC AED SUBSTANCE ABUSE MEDICATION CHANGES FEVER AED ABOVE THERAPEUTIC ELECTROLYTE IMBALANCE VEEG ADMIT SLEEP DEPRIVATION INJURY PREGNANCY 28.2% 20.6% 19.7% 15.8% 13.7% 6.2% 4.0% 2.8% 1.8% 1.6% 1.3% 1.3% 1.2% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% Figure 4. Causes of seizure 33

42 We performed similar analysis on the physician office data. This work is still in progress and results presented in this report reflects only those with a final clinicianreviewed diagnosis of epilepsy (n=276). In looking at etiology, only items listed under the variables etiology and family history were considered, with the project epidemiologist categorizing the etiologies according to the same categories as used for the HD/ED data. There should be additional information after clinician review. More than one etiology is allowed making the total is greater than 100%. As with the inpatient/ed data, the most common category is unknown/not stated. There are differences between the etiologies of the HD/ED and the POV patients, which could be due to the differences in both patients and physicians (see Figure 5). For instance, the increase in known relatives with epilepsy may reflect the more in-depth history taken in a physician s office, and the decrease in substance abuse as an etiology may reflect a difference in where health care is accessed. UNKNOWN/NOT STATED RELATIVE W/EPILPESY HEAD TRAUMA CVA CNS INFECTIONS NATAL FACTORS CONGENITAL OTHER DISEASE PROCESS SUBSTANCE ABUSE FEBRILE SEIZURES TUMORS(BRAIN) DEVELOPMENTAL GENETIC 6.9% 5.4% 3.6% 2.9% 2.5% 2.2% 1.1% 1.1% 0.7% 0.7% 0.4% 17.0% 62.7% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% Figure 5. Etiology categories of physician office epilepsy cases The categorized causes are shown below in Figure 6. Only information already categorized in the data by the abstractors is included. This work is in progress and there 34

43 will be an update after clinician review of all information. More than one cause is allowed and the total percent exceeds 100. More than half of the records had no cause listed for the most recent seizure. The second most common cause was a change in antiepileptic medication, followed by injury (other than head) and illness. Noncompliance and substance abuse are not as common among the POV patients as the HD/ED patients. AED level in these patients was the most recent level done, which may or may not have been connected to the most recent seizure, so it was examined separately. In 20% of patients, the AED levels were subtherapeutic, and 17% were above the therapeutic range. NONE OF THESE LISTED 58.7% MEDICATION CHANGES OTHER INJURY FEVER ILLNESS NONCOMPLIANCE HEAD INJURY SUBSTANCE USE/ABUSE SLEEP DEPRIVATION WEIGHT GAIN (PED) PREGNANCY 14.5% 11.6% 10.1% 9.4% 6.2% 5.1% 1.8% 1.8% 0.7% 0.4% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% Figure 6. Causes of seizures among POV patients with epilepsy IV. Target populations for intervention A. Medication use - HD/ED data In abstracting patient charts, we asked the abstractors to note any antiepileptic medication that the patient was taking at the onset of the visit. To examine medication use, we looked only at those with epilepsy that is not new onset. If they were abstracted in both 2001 and 2002 we used the data from Of 2,950 such cases, 2226 (75.5%) had their current AEDs listed on the chart. We classified drugs approved by the Federal 35

44 Drug Administration since 1990 for use in epilepsy as new, based on their recognition by the American Academy of Neurology and the American Epilepsy Society as new generation AEDs. Drugs released prior to this we classified as old. Table 14 below shows the percent of patients taking each drug, and its classification. Patients often took more than one drug making total percent greater than 100. More than half of the patients were taking phenytoin, an older drug with a fairly significant profile of side effects and difficult pharmacokinetics. Of the 263 women between the ages of 15 and 44 years, 263 (60%) were taking either phenytoin, carbamazepine, lamotrigine, or valproate, all of which have recently been shown to be connected to an increased rate of miscarriage and birth defects. Table 14. AEDs being taken by inpatient and ED patients (N=2,226). Trade name Generic name % taking Type Dilantin, Phenytek Depakote/Depakene Carbatrol, Tegretol Luminal Neurontin Keppra Ativan Lamictal Topamax Klonopin Trileptal Valium Zonegran Gabitril Mysoline Felbatrol Zarontin Tranxene Celontin Depacon Phenytoin Divalproex sodium/valproic acid Carbamazepine Phenobarbital Gabapentin Levetiracetam Lorazepam Lamotrigine Topiramate Clonazepam Oxcarbazepine Diazepam Zonisamide Tiagabine Primidone Felbamate Ethosuximide Clorazepate Methsuximide Valproate sodium 55% 19% 18% 13% 6% 5% 4% 4% 3% 3% 2% 2% 1% 1% 1% <1% <1% <1% <1% <1% Old Old New Old New New Old New New Old New Old New New Old New Old Old Old New 36

45 Patients were grouped according to the number of AEDs they had been prescribed prior to admission. 70.5% took only one AED, 24.2% took two AEDs, and 5.3% took three or more AEDs. A logistic regression model was built utilizing the independent variables listed in Table 15. Among individuals who had no insurance were included individuals who were listed as indigent, or who whose visit was covered under charity. Substance use/abuse could indicate either past or present, and included alcohol or illicit drugs. Abstractors could mark either yes to questions concerning past and present use or abuse if such information was charted, could mark no if past and present use or abuse was specifically denied in the chart, or unknown if use or abuse was not mentioned. The outcome was monotherapy versus polytherapy. Table 15. Odds of taking more than one antiepileptic drug. Independent variable Adjusted odds ratio (95% CI) Age group (1.14, 2.65) (2.01, 3.84) (1.58, 2.93) 65+ Reference Gender Female Male Race Nonwhite White Insurance None (plus indigent/charity) Medicaid Medicare Private Substance use/abuse Yes Unknown No Seizure-related visit Yes No 0.99 (0.82, 1.20) Reference 0.73 (0.60, 0.89) Reference 0.50 (0.35, 0.73) 1.30 (0.98, 1.72) 1.72 (1.29, 2.30) Reference 0.24 (0.14, 0.42) 0.42 (0.26, 0.68) Reference 1.36 (1.12, 1.67) Reference Individuals under age 65, whites, individuals on Medicare, individuals without substance abuse or use, and individuals whose visit was seizure-related were more likely to be taking more than one antiepileptic drug. Gender did not appear to affect whether an individual was on 37

46 monotherapy or polytherapy. It was also noted that individuals without insurance were more likely to be on monotherapy than individuals with commercial insurance. Patients were also grouped according to whether they took only newer AEDS, only older AEDs, or took a mix of older and newer AEDs. 79.6% took only older medications, 9.5% took only newer medications, and 10.9% took a mix of the two. Another logistic regression model was built using the same variables as above, but with an outcome of taking either only a new AED(s) or a mix of old & new AEDs versus taking only an older AED(s). Individuals were more likely to be recorded as taking only an older AED(s) if they were over age 65 years, male, nonwhite, had no insurance, were noted to have substance use/abuse or if that knowledge was unknown, and if their visit was not seizure-related (Table 16). Table 16. Odds of taking only older antiepileptic drugs. Independent variable Adjusted odds ratio (95% CI) Age group (0.15, 0.39) (0.18, 0.40) (0.43, 0.97) 65+ Reference Gender Female Male Race Nonwhite White Insurance None/indigent Medicaid Medicare Private Substance use/abuse Yes Unknown No Seizure-related visit Yes No 0.67 (0.53, 0.84) Reference 1.94 (1.54, 2.45) Reference 4.82 (2.94, 7.90) 0.99 (0.74, 1.34) 1.06 (0.77, 1.48) Reference 3.29 (1.79, 6.04) 2.34 (1.42, 3.87) Reference 0.67 (0.53, 0.86) Reference 38

47 B. Medication use - POV data As stated earlier, we asked the abstractors to note what AEDs the patient was listed as taking at the onset of the most recent visit. As a reminder, these are individuals receiving Medicare, Medicaid, or State Health Plan, and who did not have an inpatient or ED visit during 2001 and To examine medication use, we looked only at those with epilepsy that is not new onset. Patients may be taking more than one AED. Table 17 below shows the percent taking each type. Of 276 such patients, 270 (97.8%) had Table 17. AEDs being taken by patients attending POV (n=270). Trade name Generic name % taking Type Carbatrol, Tegretol Dilantin, Phenytek Depakote/Depakene Keppra Lamictal Topamax Neurontin Luminal Klonopin Mysoline Zonegran Trileptal Ativan Gabitril Felbatrol Zarontin Tranxene Valium Celontin Depacon Carbamazepine Phenytoin Divalproex sodium/valproic acid Levetiracetam Lamotrigine Topiramate Gabapentin Phenobarbital Clonazepam Primidone Zonisamide Oxcarbazepine Lorazepam Tiagabine Felbamate Ethosuximide Clorazepate Diazepam Methsuximide Valproate sodium 32% 30% 18% 14% 11% 11% 9% 8% 4% 3% 3% 3% 2% 1% 1% 1% 1% <1% <1% <1% New Old Old New New New New Old Old Old New New Old New New Old Old Old Old New 39

48 medications listed. 93.7% were seen by a neurologist or epileptologist. While the three most common AEDs are the same, only 30% are taking phenytoin, compared to 55% of the HD/ED patients. Also, 8% are taking Phenobarbital, compared to 13% of the HD/ED patients. Newer AEDs are being used among these patients than among the HD/ED patients. 60.0% took only one AED, 27.8% took two AEDs, and 12.2% took three or more AEDs. 52.2% took only older medications, 23.7% took only newer medications, and 24.1% took a mix of the two. C. Posttraumatic epilepsy A possible target for intervention in preventing epilepsy could be individuals who sustain TBI. It was noted in an earlier section of the report that individuals who developed posttraumatic epilepsy within the first three years of discharge from hospitalization were more likely to have certain characteristics. Individuals who experienced early posttraumatic seizures, with more severe head injury, with a history of depression, with three or more comorbid conditions, and under Medicaid, were more likely to develop posttraumatic epilepsy. While some of these characteristics are markers of risk, some of them are amenable to early intervention to allay posttraumatic epilepsy. Depression as a predisposing factor for epilepsy is fairly new in the literature, but offers an intriguing avenue for further exploration to determine whether treatment of depression could affect the incidence of posttraumatic epilepsy. D. Behavioral Risk Factor Surveillance System data The BRFSS questions on epilepsy include a question on whether an individual reporting having been told they have epilepsy have experienced a seizure within the past three months. Of those individuals reporting one or more seizures within the previous three months, only 74.0% (95% CI ) are currently taking a medication to control epilepsy. While these cases could involve issues of personal choice, nonepileptic seizures, etc., an important area to explore further would be why over a quarter of individuals reporting seizures are not taking medication. The BRFSS also included a question on multivitamin use. Whereas 85% of the general population reported taking a multivitamin, only 69% of individuals who had been told they had epilepsy reported taking a multivitamin. This may be an important area of intervention due to the potential 40

49 effects of antiepileptic drugs. Levels of heavy drinking and obesity were not significantly different between those reporting epilepsy and the general population. However, significantly more individuals with active epilepsy (i.e., those taking medication and/or reporting a recent seizure) were current smokers (36.8%, 95% CI ) than those in the general population (22.5%, 95% CI ). This is another important area for intervention, considering the cardiovascular effects of smoking on the brain, plus the interplay of untreated mood disorders and smoking. E. Focus group data Six focus groups were held in four different geographic locations throughout SC. The topic to be explored was to learn what problems and barriers people with epilepsy in SC face. The specific questions are in Table 18 below. There were 41 individuals who Table 18. Focus group questions. 1) Please tell the group: a) Your first name, when you were diagnosed with epilepsy and how controlled your seizures are now. b) If you are a parent of someone with epilepsy, please tell us the age of your child at diagnosis and how controlled their seizures are now. 2) What services, help, information or care exists to help people with their epilepsy needs or issues? Probes: a) Have you used any of these services you listed over time? If yes, what was your experience in using these services? b) Are there any services you wanted but could not get? If yes, what was the problem? c) What services or help are missing that you need/needed? 3) How does a person find out about services or help for epilepsy? How did you find out? a) How might finding out about services be improved? 4) What challenges or problems has epilepsy created in your life over time? Probes: a) How have these challenges changed throughout your life? b) What types of things have you found helpful in dealing with these challenges and managing your condition? c) What are your thoughts about your future, in relation to epilepsy? 5) In closing, I have one last question that allows you to use your imagination: a) If you could tell health care providers and policy makers about what it s like to live with epilepsy, and what you think they should know, what are some things you would say? 41

50 participated, who together represented 31 persons with epilepsy, either themselves or a family member. While gender and race reflected the population quite well, there were no individuals over the age of 65 years. All categories of education, employment, insurance, and marital status were represented. A manuscript detailing the results of the focus groups has been published (Sample PL. Ferguson PF. Wagner JL. Pickelsimer EE. Selassie AW. Experiences of persons with epilepsy and their families as they look for medical and community care: A focus group study from South Carolina. Epilepsy & Behavior. 9: , 2006.). The groups identified numerous areas where intervention is warranted among people with epilepsy. Clinical issues will be addressed later in the report. Other areas they identified were social isolation, difficulty finding information, difficulty finding and keeping employment, lack of transportation, the perception and/or reality of epilepsy not being considered a disability despite the limitations it can carry, and the stigma that epilepsy continues to carry. They painted a picture of individuals living on the edge of life, apart from the mainstream, stymied in following their dreams, considered by much of the population to be disabled yet unable to obtain disability assistance. These are areas in which intervention is not held back by the need for new clinical developments. Public education and government action could greatly impact on these outcomes. V. Severity and subtypes of epilepsy Aim Describe the specific categories, subtypes, and severity of epilepsy and seizure disorder A. HD/ED data Determining severity of seizures is a difficult task under the best of circumstances. There is no universally agreed upon scale to measure seizure severity. While there are some seizure characteristics that can be used, such as frequency of seizures or type of seizures, it can be argued that severity should be decided by the individual him- or herself. In addition, severity could include medication side effects, as was noted by the focus groups. While this information was not collected from the chart abstractions, information was collected on the number of people with epilepsy on polytherapy, which increases the likelihood of side effects occurring (see earlier section on target populations for intervention). We included several variables in the data abstraction to attempt to get a picture of severity. Unfortunately, there was limited 42

51 information in the patient charts concerning seizure lengths, types of seizures, etc. The following is the information that was obtained from the HD/ED charts. The subset analyzed consists of those cases with a diagnosis of epilepsy after clinician review. Since information was scarce in many charts, individuals whose charts were abstracted in 2001 and 2002 had information retained in both years. If the hospital visit involved a seizure (ie, person went to ED because of a seizure, was admitted because of a seizure, the person had a seizure while at the hospital, etc.) then the abstractor had to attempt to record the number of seizure episodes, the time of onset of the seizure(s), and the length of the seizures(s). There were 1701 cases of epilepsy with a seizure-related hospital visit, which included 48 duplicates. Of the 1,701, 1,281 (75%) had information on number of seizure episodes. Of those with information, 64.4% had one seizure, 14.1% had two seizures, and 21.5% had more than two seizures. 714 cases (42.0%) had information on time of onset of the seizure(s). Of those with information, 33.8% had a morning seizure event, 28.0% had an afternoon seizure event, 15.4% had an evening seizure event, and 22.8% had a seizure event during the night. 510 cases (30.0%) had information on the length of the seizure event. Of those with information, 9.0% had a seizure event lasting less than 30 seconds, 23.5% had an event lasting up to 2 minutes, 31.2% had an event lasting up to 5 minutes, and 36.3% had a seizure event lasting greater than 5 minutes. If there was information in the chart concerning a history of seizures, the abstractor was to collect information on the number of types of seizures and the frequency of seizures. There were 2,751 cases of epilepsy with a noted history of seizures, which included 78 duplicates. Of the 2,751, 300 (10.9%) had information on number of types of seizures. Of those with information, 68.0% had one type of seizure, 17.3% had two types of seizures, and 14.7% had more than two types of seizures. Only 172 cases (6.3%) had information on the frequency of their seizure events. Of those with information, 18.6% had seizures less than once a year, 30.8% had seizures more than once a year, 11.0% had seizures more than once a month, 43

52 21.5% had seizures more than once a week, and 18.0% had seizures more than once a day. There were 959 charts (including 44 patients abstracted both in 2001 and 2002) with an epilepsy diagnosis originally in our sample. Four of these charts had two epilepsy diagnoses recorded. Status epilepticus codes were excluded since they do not necessarily indicate epilepsy. Figure 7 shows the frequency of each diagnosis. The following are the definitions of the ICD-9-CM epilepsy codes: = generalized nonconvulsive epilepsy, = generalized convulsive epilepsy, = petit mal status (epileptic absence status), = grand mal status (status epilepticus, not otherwise specified), = partial epilepsy, with impairment of consciousness, = partial epilepsy, without mention of impairment of consciousness, = infantile spasms, = epilepsia partialis continua (Kojevnikov s epilepsy), = other forms of epilepsy, = epilepsy, unspecified. 70.0% 60.0% 65.4% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 14.0% 7.4% 2.7% 3.6% 1.0% 2.3% 4.0% Figure 7. Original epilepsy diagnoses recorded on the HD/ED charts (n=959). After clinician review of the abstracted information and the previous diagnoses of seizures and/or epilepsy, there were 3,056 patients with epilepsy diagnoses, with 88 of the records of these patients abstracted in both 2001 and Fourteen percent had more than one epilepsy diagnosis listed. Figure 8 shows the frequencies of epilepsy diagnoses after clinical review of the abstracted information. These cases include status codes, as an indication of severity. Furthermore, post-chart review evaluation indicated 44

53 that 13.1% of those with epilepsy diagnoses were being seen for a status seizure (there was only one case with a diagnosis, representing less than 0.1%). The large percent of cases with is an indication of the scarcity of specific information on the charts with regard to seizure characteristics. Perhaps, the assignment of this code may also suggest the lack of continuity of care by a neurologist/epileptologist. It should be noted that large proportions of epilepsy patients who receive their cares through the ED are uninsured or underinsured to get the appropriate evaluation. 90.0% 80.0% 70.0% 60.0% 50.0% 83.7% 40.0% 30.0% 20.0% 13.1% 7.9% 10.0% 4.5% 0.5% 2.9% 0.2% 0.9% 0.6% 0.0% 0.0% Figure 8. Epilepsy diagnoses after clinician review of the HD/ED charts (n=3,056). B. Physician Office Visit (POV) data Data from the 302 patients from POV reflect visits in either 2001 or 2002 and not seen in an ED or HD during the period of surveillance. Further, these are patients whose insurance provider is Medicare, Medicaid, or State Health Plan. After clinician review, 276 of the cases were determined to be epilepsy. Salient findings of this evaluation indicate the following key points. Of the 276 epilepsy cases, 182 (65.9%) had information concerning number of seizures experienced during their most recent episode of seizure(s). Of those with information, 75.8% had had one seizure, 15.4% had had two seizures, and 8.8% had had more than two seizures. 45

54 Only 21 (7.6%) had information recorded concerning the time of the most recent seizure event. Of those with information, 28.6% had a morning seizure, 14.3% had an afternoon seizure, 4.8% had an evening seizure, and 52.4% had had a seizure during the night. 102 cases (37.0%) had information about the length of the most recent seizure event. Of those with information, 30.4% had a seizure lasting 30 seconds or less, 36.3% had a seizure lasting 2 minutes or less, 20.6% had a seizure lasting 5 minutes or less, and 12.7% had a seizure lasting more than 5 minutes. 229 cases (83.0%) had information about how many types of seizures the patient experiences. Of those with information, 83.0% had one type of seizure, 10.9% had two types of seizures, and 6.1% had more than two types of seizures. In 275 of the charts, there was no indication that this was a visit for a first seizure. In these cases, the abstractor was asked to collect information on the frequency of the seizures in the past year. 181 (65.8%) had information on seizure frequency. Of those with information, 24.3% had seizures less than once a year, 29.3% had seizure more than once a year, 26.0% had seizures more than once a month, 12.7% had seizures more than once a week, and 7.7% had seizures more than once a day. Of the entire sample of 302 physician office visit cases, there were 221 cases with an original diagnosis of epilepsy. Only one diagnosis for each case was sent to us from the Office of Research and Statistics (ORS). Figure 9 shows the frequency of the epilepsy diagnoses. After clinician review, there were 276 cases of epilepsy (see Figure 10). Ten percent were given two epilepsy diagnoses. There were no cases of status, as would be expected in an office setting. There are far fewer cases of (epilepsy, unspecified) in the office charts, both among the original diagnoses and after review, due to the increased information available on seizure specifics in these charts compared to the HD and ED charts. The most common diagnoses originally on these charts were 345.0, 345.4, and After review, the three most common diagnoses were 345.4, 345.1, and We questioned if perhaps the original diagnoses were actually recorded as 345, without the fourth digit, but ORS confirmed that they were actually listed as

55 Taken together, information gleaned from HD/ED is less informative than information gleaned from POV with regard to determination of seizure type and severity. The limited information on HD/ED data is yet additional evidence about the type and continuity of care provided to epilepsy patients. Furthermore, the distinct difference is the large number of patients with epilepsy attending physician offices have insurance while the large proportion of patients (14.3%) evaluated in HD/ED have no insurance. 45.0% 40.0% 35.0% 30.0% 25.0% 20.0% 15.0% 10.0% 5.0% 0.0% 40.3% 30.3% 20.4% 5.9% 3.2% 0.0% 0.0% 0.0% Figure 9. Original epilepsy diagnoses recorded on the POV charts (n=221). 70.0% 60.0% 59.1% 50.0% 40.0% 30.0% 20.0% 25.0% 19.2% 10.0% 0.0% 6.2% 0.7% 0.0% 0.0% 0.0% Figure 10. Epilepsy diagnoses after clinician review of POV charts (n=276). 47

56 C. Behavioral Risk Factor Surveillance System (BRFSS) data The BRFSS collects information on the number of mentally unhealthy, physically unhealthy, overall unhealthy, and activity-limited days individuals report for the preceding 30 days. In addition, they ask about disability with the question, Are you limited in any way in any activities because of physical, mental, or emotional problems?. After receipt of the first two years of BRFSS data (2003 and 2004), we analyzed the epilepsy responses in conjunction with these five variables, which may be considered a reflection of the severity of epilepsy when compared to the general population results. The results were published in an MMWR article entitled, Prevalence of epilepsy and health-related quality of life and disability among adults with epilepsy - South Carolina, 2003 and (MMWR, October 28, 2005 / 54(42); ; Centers for Disease Control and Prevention). We found that individuals with active epilepsy (i.e., taking medication for epilepsy and/or reported seizure activity in the previous three months) had more than twice as many mentally unhealthy days, physically unhealthy days, overall unhealthy days, and activity-limited days than the general population. 17.9% (95% CI ) reported having disability, compared to 63.5% (95% CI ) of individuals with active epilepsy. Even individuals with inactive epilepsy (not currently taking antiepileptic medications, and no seizure activity in the preceding three months) had a significantly higher percentage reporting disability (28.2%, 95% CI ) than the general population. VI. Venues and Levels of Care Aim Document the venues and levels of care provided to persons with epilepsy A. Behavioral Risk Factor Surveillance System data In the final two years of the epilepsy module for SC BRFSS (2004 and 2005) we asked a fifth question, In the past year have you seen a neurologist or epilepsy specialist for you epilepsy or seizure disorder?. Of individuals who responded that they had ever been told by a doctor that they had epilepsy, 37.8% (95% CI: ) responded affirmatively. Of those with active epilepsy (i.e., currently taking medicine for epilepsy and/or had seizure activity within the previous three months), 65.9% (95% CI: ) reported they had seen a 48

57 neurologist within the past year. Unfortunately, numbers are too small to report by specific characteristics. 82.6% (95% CI ) of individuals with active epilepsy reported they had a personal doctor or health care provider, and 72.9% (95% CI ) had had a routine check-up in the past twelve months. Far fewer, however, reported seeing a dentist within the past twelve months only 53.5% (95% CI ) of individuals with active epilepsy. Since some AEDs can affect oral health, this can be considered an important part of routine health care for individuals with epilepsy. A large percentage of individuals with active epilepsy reported there was a time in the past 12 months when they needed to see a doctor but could not because of cost % (95% CI ). In the general population, only 15.6% (95% CI ) reported not being able to see a doctor because of the cost. 77.5% (95% CI ) of individuals with active epilepsy reported having health care coverage. Although the confidence intervals do not indicate a statistically significant difference, 86.5% of individuals with active epilepsy who did not have a seizure within the past three months had health care coverage, whereas only 68.5% of individuals with a recent seizure had health care coverage. B. Physician office visit data Information gleaned from the SC ORS, for 2001 and 2002 Medicaid and State Health Plan participants suggests gaps in the receipts of care among those individuals with seizure unspecified diagnosis. Comparison between persons coded with 345.x and indicate differences in the type of care received. When analyzing only individuals with a 345.x or diagnoses, and who had not gone to an ED or been an inpatient during that time, 75% of those with a 345.x diagnosis had seen a neurologist, versus 35% of those with a diagnosis. This suggests that diagnosis of 345.x is more likely to be assigned among patients evaluated by neurologists compared to other physicians. This may have numerous implications on the type of care received by these patients. First, without specific diagnosis, appropriate medical care, particularly medication, is less likely to be effective. Second, this may also suggest lack of neurologists/epileptologists in the state, limiting access to specialty care particularly for those without good insurance. Third, although blacks account for 30% of the state 49

58 residents, they represent 38% of the patients who received care through HD/ED in 2001 and 2002, suggesting limited access to specialty care in private practices. Further, among blacks16.3% and 25.2% are uninsured and Medicaid insured respectively while among whites 12.5% and 11.6% are respectively uninsured and Medicaid insured. This might also have implications in access to specialty care since the case mix of uninsured and Medicaid is very low in POV settings. C. Focus group data Barriers to care and accessing epilepsy-related services, and what life with epilepsy is like were discussed among the focus groups held throughout South Carolina. Participants reported problems in obtaining an epilepsy diagnosis, with delays in diagnosis consisting of years. Other findings include the difficulty of life with epilepsy and the permanency of these difficulties, life-long searches for help with epilepsy, and how those searches are seldom easy or successful. Also highlighted are several services that have provided helpful support, and recommendations for potential improvements in public education, professional training, and helpful interventions. Participants mentioned difficulty in obtaining clinical information from physicians, and turning to other sources for information, such as the internet. In addition, there was a need for information on assistance with low-cost health care providers, and financial assistance with medication. Additional details on barriers to care are described in detail in the published manuscript (Sample PL. Ferguson PF. Wagner JL. Pickelsimer EE. Selassie AW. Experiences of persons with epilepsy and their families as they look for medical and community care: A focus group study from South Carolina. Epilepsy & Behavior. 9: , 2006.). VII. TBI Among Persons with Seizure Disorder Aim Determine the burden of TBI among persons with seizure disorders A. Pre-existing seizure disorders and TBI This evaluation utilized surveillance data collected from statewide HD and ED from1996 through Pre-existing epilepsy and/or seizure disorders (ESD) were identified using a comorbid diagnosis of ICD-9-CM codes 345.xx and from either primary or secondary diagnosis fields among persons with TBI. The underlying assumption for this analysis is that these conditions, 50

59 by virtue of being chronic in nature, are temporally antecedent to the TBI. Further, the coding conventions of ICD-9-CM clearly indicate neither 345.x nor should be used for convulsions precipitated by head trauma either immediately or within the first week of head injury. The corollary of that convention assumes that the higher rate of TBI among persons with ESD, compared to those without, may suggest the excess burden of TBI is attributable to pre-existing ESD. In this evaluation, we systematically analyzed surveillance data on a total of 128,882 unduplicated persons with a diagnosis of TBI. Of these, 4,043 (2.7%) had a comorbid diagnosis of 345.xx (epilepsy) and/or (seizures not otherwise specified). Among the 4,043 individuals with a comorbid diagnosis of ESD, a subset of records identified through 2001 (N=2,170), were stratified by diagnosis and a random sample of 100% of the persons with 345.xx and 50% of the 780.3x diagnoses were selected resulting in 1,145 cases. Of these, the SC Department of Health and Environmental Control abstractors were able to find and abstract the records of 872 (76.2%) cases. A total of 528 (60.6%) satisfied the case definition of pre-existing ESD, which is defined as evidence of epilepsy or seizures prior to the onset of TBI validated by: 1) eyewitness history indicating that the seizure triggered the fall that lead to the TBI, 2) a seizure related visit at least one month prior to the TBI, or 3) use of antiepileptic drugs prior to the TBI. There was no significant difference between persons whose records were found and those whose records were missing regarding demographic and clinical characteristics (p>0.10). Thus, the data analyzed are at least 61% true ESD patients. Data analysis included descriptive and analytical comparisons of demographic and clinical characteristics between those with and without pre-existing ESD. Bivariate and multivariable logistic regression analyses indicated that all variables retained in the final model showed association with pre-existing ESD (Table 19). Comparison of crude proportions and odds ratios showed significant differences between persons with ESD and those without regarding age, race, severity, payer type, type of injury, other comorbidities, and repetitiveness of the trauma. Persons with ESD tend to be older, black, with more than 2 TBI during the time interval, more likely to be injured due to fall or adverse effects of drugs, and to be insured by Medicaid and/or Medicare then those without pre-existing TBI. Table 20 shows multivariable adjusted odds ratios. All the 51

60 variables noted earlier retained statistical significance with the exception of the 65 and older age group. This effect is due to collinearity with Medicare payer status, which retained the comparable degree of association with presence of ESD. The observed associations suggest the implications of ESD in contributing to the cycle of seizure and the repetitiveness of these injuries. The risk of repetitive TBI is 3-fold among persons with ESD compared to those without. To further elucidate the burden of other comorbid conditions associated with persons with ESD and TBI, we used the comorbidity index developed by Elixhasuer et al. (1) and evaluated the odds of having the conditions listed in Table 21 as a function of preexisting ESD. Accordingly, age-sex-race adjusted odds ratios indicate significant association (P<0.05) for six conditions (AIDS, alcohol abuse, paralysis, neurological disorders, fluid & electrolyte disorders, and coagulopathies). In conclusion, our findings suggest that persons with ESD carry a disproportionate burden of TBI. The standardized morbidity ratio of TBI is at least 1.6 to 2.0 times greater among persons with ESD than what is noted in the general population of the state even when we assumed the lifetime prevalence rate of epilepsy (2.0%). When the prevalence is defined as 1.0%, which is the case based on our prevalence report, the morbidity ratio is 3 to 4 times greater that the rate in the general population of the state. Furthermore, the increased risk of repetitiveness of TBI among persons with ESD is a profound concern since more head trauma has the potential to aggravate the intensity and severity of seizure. 52

61 Table 19. Characteristics of TBI patients by ESD, SC, (N=128,882). Characteristics Yes % (N=4,043) Pre-existing ESD No % (N=124,839) Crude Odds Ratio 95% Confidence Interval Age Reference Mean (SD) 41.2 (24.5) 31.5 (24.0) Race/Sex Black Female Black Male White Female White Male Reference TBI Severity Severe Moderate Mild Reference Cause of Injury Drug adverse/poison All Others Fall Transport Struck by/against Violence Reference Payer Medicare Medicaid Other government Uninsured Commercial Reference Care Type Inpatients Outpatients Reference TBI Injury Type Intracranial Skull fracture Concussions Unspecified injury Reference Comorbidity Reference Concomitant Injury No Yes Reference Repetitive TBI Reference 53

62 Table 20. Adjusted odds ratios comparing TBI patients with ESD and without (N=128,882). Characteristic Adjusted Odds Ratio 95% Confidence Interval Age Reference Race/Sex Black Female Black Male White Female White Male 1.00 Reference TBI Severity Severe Moderate Mild 1.00 Reference Cause of Injury Drug adverse/poison All Others Fall Transport Struck by/against Violence 1.00 Reference Payer Medicare Medicaid Other government Uninsured Commercial 1.00 Reference Care Type Inpatient Outpatient 1.00 Reference Comorbidity Reference Concomitant Injury No Yes 1.00 Reference Repetitive TBI Reference 54

63 Table 21. Odds of comorbid conditions among persons with TBI by pre-existing ESD Pre-existing ESD Comorbid Condition Yes Freq. (%) No Freq. (%) Unadjusted OR (95%CI) Adjusted OR (95%CI) Congestive heart failure 117 (2.89) 1235 (0.99) 2.98 ( ) 0.78 ( ) Cardiac arrhythmias 165 (4.08) 1932 (1.55) 2.71 ( ) 0.83 ( ) Valvular disease 49 (1.21) 517 (0.41) 2.95 ( ) 0.79 ( ) Pulmonary circulation disorders 3 (0.07) 63 (0.05) 1.46 ( ) 0.37 ( ) Peripheral vascular disorders 32 (0.79) 329 (0.26) 3.02 ( ) 0.75 ( ) Hypertension 662 (16.37) 9050 (7.25) 2.51( ) 0.77 ( ) Paralysis 126 (3.12) 518 (0.41) 7.72 ( ) 1.77 ( ) Neurological disorders 142 (3.51) 921 (0.74) 4.90 ( ) 1.39 ( ) COPD/Asthma 243 (6.01) 2748 (2.20) 2.84 ( ) 1.02 ( ) Diabetes 245 (6.06) 3817 (3.06) 2.05 ( ) 0.64 ( ) Renal failure 74 (1.83) 454 (0.36) 5.11 ( ) 0.99 ( ) Liver disease 50 (1.24) 221 (0.18) 7.07 ( ) 1.13 ( ) Peptic ulcer disease 9 (0.22) 155 (0.12) 1.80 ( ) 0.48 ( ) AIDS 21 (0.52) 45 (0.04) ( ) 2.18 ( ) Lymphoma 8 (0.20) 72 (0.06) 3.44 ( ) 0.79 ( ) Metastatic cancer 16 (0.40) 143 (0.11) 3.47( ) 0.81 ( ) Solid tumor without metastasis 76 (1.88) 1000 (0.80) 2.37 ( ) 0.70 ( ) Rheumatoid arthritis 22 (0.54) 222 (0.18) 3.07 ( ) 0.93 ( ) Coagulopathy 214 (5.29) 1176 (0.94) 5.88 ( ) 1.36 ( ) Weight loss 76 (1.88) 487 (0.39) 4.90 ( ) 1.13 ( ) Fluid and electrolyte disorders 499 (12.34) 3210 (2.57) 5.34 ( ) 1.33 ( ) Blood loss anemia 23 (0.57) 227 (0.18) 3.14 ( ) 0.91 ( ) Alcohol abuse 253 (6.26) 897 (0.72) 9.23 ( ) 2.20 ( ) Drug abuse 145 (3.59) 1637 (1.31) 2.80 ( ) 0.94 ( ) Psychoses 92 (2.28) 844 (0.68) 3.42 ( ) 0.95 ( ) Depression 105 (2.60) 1144 (0.92) 2.88 ( ) 0.88 ( ) Stroke 97(2.40) 590 (0.47) 5.18 ( ) 1.13 ( ) Hypotension 69 (1.71) 951 (0.76) 2.27 ( ) 0.67 ( ) Adjusted for age, sex, and race 55

64 VIII. Data quality Aim Evaluate quality of the data and accuracy of the estimates A. Population and sample information the sources of the data and the methods of sampling play a significant role in the determination of data quality. These concepts are tied to coverage of the data sources and the representativeness of the sample, each of which determine the external validity of the findings. Coverage refers to the extent to which all persons in the geopolitical jurisdiction are included in the numerator and such numerator is reflective of the pool of individuals in the referent population. Sample representativeness indicates the extent to which every single unit in the frame has an equal probability of inclusion in the sample such that the aggregate nature of the sample is truly reflective of the population in the frame. In our surveillance of epilepsy, we made every effort to insure that the information collected is inclusive and selection is unbiased. To further elucidate these points, we present key information about the data sources. 1. Hospital Discharge and Emergency Department the South Carolina Office of Research and Statistics (SC ORS) houses an administrative billing dataset with information from all non-federal acute care hospital discharges and ED visits in SC. Among the various variables included billing abstracted data are personal identifiers and up to ten diagnosis codes. As a major partner to the project and a stakeholder in statewide health and demography data collection, ORS identified a sample of individuals from 2001 and 2002 with epilepsy (345.x), convulsions (780.3), syncope and collapse (780.2), and acute delirium (293.0). The latter two codes were included on the chance that some seizures might be miscoded as syncope or delirium. The initial plan was to choose 35% of 345.x codes, 5% of codes, 1% of codes, and 5% of codes for 2001 for a total of approximately 3,000 charts. From the 2002 population we wanted to have a total abstraction of approximately 1,500 charts, and based on our experience with the 2001 data, inflated that number upward to adjust for expected rate of 15% unlocated charts. See Appendix G for the HD/ED sampling plan. Accordingly, ORS identified 3,000 and 1,742 charts from 2001 and 2002 encounters respectively for abstraction. When drawing the 2002 sample, we decided to allow cases from 2001 to be included in the population. Of the final

65 chart abstractions, 102 (7.0%) of the cases had also been abstracted in the 2001 sample. ORS sent information from these records to SC Department of Health and Environmental Control (SC DHEC). DHEC abstractors went to the hospitals and abstracted information from those records based on the abstraction tool we had developed. DHEC then sent the abstracted information to ORS, where all identifying information was removed. The de-identified data was then sent to MUSC. To assist MUSC in determining history of seizures, ORS also sent a database consisting of the unique IDs they had assigned each case, along with the diagnosis codes from any earlier hospital or ED visits with a seizure-related code. MUSC received 3,998 abstractions from ORS (2001 N=2538, 2002 N=1460). This included 5 duplicates (same chart abstracted twice) and 5 blank abstractions, plus 2 abstractions done that appeared not to have been part of original sample. Thus, there was a total of 3988 unique abstractions completed (2001 N=2535, 2002 N=1453). We further found that 3 of the charts appeared to have been erroneously coded with or 345.x, so we removed them from analysis, reducing the total to 3985 charts (2001 N=2532, 2002 N=1453). After matching the charts back to the original population, there were two charts abstracted that were never in original sample. These were excluded from analysis yielding a total of 3,983 charts with seizure-related (ie, epilepsy, convulsion/seizure, syncope & collapse, and/or acute delirium) diagnoses (2001 N=2530, 2002 N=1453). In order to evaluate the representativeness of our sample to the population, we requested from ORS a dataset with the entire population of unduplicated cases with a seizure-related diagnosis code in 2001 and 2002, with age group, sex, race, payer, and diagnosis code grouping (if more than one seizure-related code, grouping assigned with the following hierarchy: 345.x > > > 293.0), with abstracted cases marked. Below is a summary of how well the data for those marked cases matched with our abstracted data. See Appendix C for a comparison of the proportions of sex, race, age group, and payer for and 345.x diagnoses and year in cases marked as abstracted versus the population. The following is a brief summary of the finding regarding comparability of the records abstracted to the information in the frame. 57

66 1. Age and gender have accuracy of 99.8% 2. Race has a match rate of 99.4%. 3. Payer is 98.1% accurate. 4. Diagnosis from chart review is accurate in 95.2 % of the cases with the diagnosis listed in UB-92 when arranged hierarchically as 345.x > > > Physician Office Visits captures individuals not seen in HD/ED setting. ORS is the repository of the data from physician offices when primary payer is Medicare, Medicaid, or State Health Plan an insurance plan for state employees, their dependents, and retirees. The data format for reporting is based on CMS-1500, a billing form that includes all variables but inpatient care and related procedures. The POV report does not include out-of pocket payers (self-pay) and these are expected to be <5% of the patient pool in physician offices. The proportion of self-pay in POV setting is even expected to be much lower when it comes to persons with chronic conditions like seizure disorders. We sampled from 2001 and 2002, except for Medicare, for which we only had 2001 data available at the time of the sampling in Data abstraction from physician offices was entirely voluntary on their part. Due to this, the practice from which the sample of charts abstracted from participating practices was neither random nor representative of all such practices in the state. The practices that volunteered for chart abstraction were identified and contacted by the project epileptologist and investigator (BW), asking them whether they would be willing to have their records abstracted by SC DHEC personnel. He strove to include a variety of specialties. Overall 90 practitioners agreed to participate, representing 32 family practice, 8 internal medicine, 29 neurology, 8 obstetrics/gynecology, and 13 pediatric specialties. However, some practitioners had no cases represented in the sample, and some offices could not find the requested records. The final abstracted sample included data from 46 practices, representing 8 family practice, 6 internal medicine, 26 neurology, 2 obstetrics/gynecology, and 4 pediatric specialties, however most of the population was from neurology offices. We asked the abstractors to note the type of specialty visit for each case. They recorded the following distribution: 9 from primary care, 199 from neurology, 83 from epileptology, and 11 unknown. 58

67 Cases were restricted to those individuals who had no encounter in either HD or ED in SC during 2001 or The rationale for this was to have two exclusive groups those seen in HD/ED setting, and those seen in a POV setting. ORS estimates that during the surveillance period, Medicare, Medicaid, and SHP covered approximately 39% of the population of SC. We initially attempted to abstract 400 charts. Due to difficulty finding the charts, a series of four samples were eventually drawn to reach a revised target of 300. Out of a total drawn sample of 581 charts, 302 (52.0%) were found and abstracted. We initially planned on pulling a ratio of 8:2, epilepsy to seizure codes. However, as the sampling progressed, more seizure codes were included as necessary to reach our target goal. Due to limited information at ORS, Medicare charts were the most difficult to find, and could only be sampled from (Please see Appendix H for the physician office visit sampling plan.) The HD/ED sample showed that all cases pulled with a or code also had either a 345.x or codes. The sampling strategy for the office charts was restricted to only 345.x and diagnoses. Rather than allowing cases with more than one seizure-related diagnosis to be pulled under either diagnosis, this sample was pulled using only one diagnosis for each case, and it is unknown whether the cases had a second seizure-related diagnosis. However, we feel second diagnoses were likely rare, since in examining the HD/ED data, and looking only at and 345.x codes on the charts, there were only 16 (0.8%) cases in 2001 that had more than one of those diagnoses, and there were only 12 (0.6%) cases in 2002 that had more than one of those diagnoses. Assuming that more than one epilepsy or seizure code per chart is equally as rare in the POV charts, there should be minimal effect from limiting to one seizure-related code. As mentioned above, to compare our sample with the population, we requested from ORS a dataset with the entire population of unduplicated cases with a seizure-related diagnosis code in 2001 and 2002 (only 2001 available for Medicare), with age group, sex, race, payer, and diagnosis, with abstracted cases marked, for each of the three payer groups. Below is a summary of how well information acquired through chart review correctly matched with the POV data from ORS. Due to initial matching error of the unique identifiers, this analysis is restricted to 246 (81.5%) of the records. Please see Appendix D for a comparison of 59

68 the proportions of the characteristics seen below in cases marked as abstracted versus the population. The following is a brief summary of the finding regarding comparability of the records abstracted to the information in the frame. 1. Age and gender have accuracy of 98.8% 2. Race has a match rate of 99.6%. 3. Payer is 75.6% accurate. 4. Diagnosis from chart review is accurate in 92.3 % of the cases with the diagnosis listed in CMS-1500 when arranged hierarchically as 345.x > B. Predictive Value Positive (PVP) and Sensitivity of seizure and epilepsy codes PVP reflects the ability of the ICD-9-CM codes assigned in administrative datasets to correctly identify epilepsy cases. Conversely, sensitivity implies the extent to which true cases and true non-cases of epilepsy from medical records are correctly differentiated by the codes in the administrative datasets. While sampling is often driven by the codes assigned in the administrative datasets, the prospect of correctly conducting sensitivity is likely to be biased since a priori case selection from medical charts is difficult at best and impossible at worst. Thus, emphasis of this evaluation is on PVP while Sensitivity will be interpreted with caution. 1. HD and ED Data to determine PVP and Sensitivity, codes were arranged in hierarchical manner: epilepsy (345, 345.0, 345.1, 345.4, 345.5, 345.6, 345.7, 345.8, 345.9) > status (345.2, 345.3) > seizure (780.3, , or ) > syncope (780.2) > delirium (293.0). In our sample it happened that any original and codes were always accompanied by either a or a 345.x. In some cases, some of the charts have more than one seizure-related codes and any one of these codes were used to validate PVP and sensitivity. Table 22 summarizes the diagnosis codes. 60

69 Table 22. Inpatient/ED diagnoses before and after abstraction review. Diagnosis after Clinician Review Original Epilepsy Seizure Status Syncope Unknown Diagnosis Epilepsy Seizure Status (86.8%) 296 (89.4%) 1231 (69.8%) 809 (78.9%) 99 (72.3%) 76 (79.2%) 24 (3.8%) 15 (4.5%) 303 (17.2%) 127 (12.4%) 1 (0.7%) 1 (1.0%) 3 (0.5%) 3 (0.9%) 74 (4.2%) 20 (2.0%) 36 (26.3%) 19 (19.8%) (0.1%) 1 (0.1%) (8.9%) 17 (5.1%) 155 (8.8%) 69 (6.7%) 1 (0.7%) 0 Total 628 (15.8%) 331 (8.3%) 1765 (44.3%) 1026 (25.8%) 137 (3.4%) 96 (2.4%) All 3056 (76.7%) 471 (11.8%) 155 (3.9%) 3 (0.1%) 298 (7.5%) 3983 (100%) Epilepsy there were 959 cases coded as epilepsy in the HD/ED coding scheme. After clinician review, 841 (87.7%) of these were considered correct as epilepsy codes. 39 (4.1%) of them were considered to be a code, 6 (1%) were considered to be status epilepticus (345.2, 345.3), and 73 (7.6%) were considered to not have enough information to assign a diagnosis. Seizures there were 2,791 cases with seizure codes. After clinician review, 430 (15.4%) were considered correct as a seizure code. 2,040 (73.1%) were considered to have a history of epilepsy, 94 (3.4%) were considered to be status epilepticus, 3 (<1%) were considered to be syncope (780.2), and 224 (8.0%) did not have enough information to assign a diagnosis. Status there were 233 cases with status epilepticus codes (345.2, 345.3). After clinician review, 55 (23.6%) were considered to be correct as status epilepticus. 175 (75.1%) were considered to have a history of epilepsy, 2 (<1%) were considered to be 780.3, and 1 (<1%) was considered to not have enough information to assign a diagnosis. Febrile Seizure there were 110 patients coded as febrile seizure, of which 96 (87.3%) were correct. 8 (7.3%) were determined to have epilepsy after clinician review. 2. POV Data determination of PVP and Sensitivity from POV generally followed the same principle with codes were arranged in hierarchical manner but only for 61

70 epilepsy and seizure codes: epilepsy (345, 345.0, 345.1, 345.4, 345.5, 345.6, 345.7, 345.8, 345.9) > status (345.2, 345.3) > seizure (780.3, , or ). There were no other codes sampled from the POV dataset. As mentioned earlier, while the sampled observations were randomly selected, the offices from which the sample of charts was pulled were a convenience sample. However, there appears to be comparability between the samples drawn and the frame regarding some of the characteristics. After removing the 11 cases in which there was not enough information to determine a diagnosis after review, there were 165 and 126 records sampled for 2001 and 2002 respectively. Seventy-three percent (213) of the records had an original diagnosis code of epilepsy while 27% (78) records had seizure as the original diagnosis. 3. Estimates of PVP and Sensitivity for HD/ED data using the clinical reviewers evaluation as the correct classification, we generated the following estimate for the sources. While the PVP of epilepsy codes range between 95% and 96%, the corresponding value for seizure codes remain very low (20% and 14% for 2001 and 2002 respectively). This suggests that 80-86% of the cases coded as seizure are epilepsy codes, while 95% of the cases coded as epilepsy are true epilepsy cases. Unfortunately, 780.3x is the most prolific code in the seizure category and the ratio of 345.x to 780.3x is 1:11. It should be noted that until proven otherwise, codes should be presumed as epilepsy cases for surveillance purposes. With the constraint of the sampling scheme in mind, 780.3x codes have high sensitivity due to the large proportion false positive cases they capture. Conversely, the sensitivity of 345.x codes is very low due to the small number of false positive cases they capture (please see the calculation in the prototype tables shown by year) Code assigned after clinician review Epilepsy Seizure Total HD/ED Epilepsy UB-92 Seizure code Total PVP of 345.x = 545/569 = 96% PVP of = 304/1540 = 20% Sensitivity of 345.x = 545/1781 = 31% Sensitivity of = 304/328 = 93% 2002 Code assigned after clinician review Epilepsy Seizure Total HD/ED Epilepsy UB-92 Seizure code Total PVP of 345.x = 296/311 = 95% PVP of = 127/938 = 14% Sensitivity of 345.x = 296/1107 = 27% Sensitivity of = 127/142 = 89% 62

71 4. Estimates of PVP and Sensitivity for POV data as indicated in the aforementioned description, all procedures for estimating PVP and sensitivity remained the same. The estimated PVP for POV is even higher than what was noted in the HD/ED dataset. Estimate range between 94% and 97%, the corresponding value for seizure codes remain much lower at 4% and 6% for 2001 and 2002 respectively. This lower rate for seizure codes is mainly accounted by the very small number of patients coded with 780.3x in POVs. Perhaps the large part of this might be the client pool of patients in physician offices who already have well-established diagnoses. The tables below show the calculated values for the two measures for 2001 and Code assigned after clinician review Epilepsy Seizure Total POV Epilepsy code Seizure Total PVP of 345.x = 108/115 = 94% PVP of = 3/50 = 6% Sensitivity of 345.x = 108/155 = 70% Sensitivity of = 3/10 = 30% 2002 Code assigned after clinician review Epilepsy Seizure Total POV Epilepsy code Seizure Total PVP of 345.x = 95/98 = 97% PVP of = 1/28 = 4% Sensitivity of 345.x = 95/122 = 78% Sensitivity of = 1/4 = 25% As a summary, the PVP of 345.x codes is very high across the two data sources. Persons coded with 345.x are about 95% of the time true epilepsy cases. However, 70-80% the persons coded with780.3x are misclassified as seizure. Sensitivity as a measure of data validity is not reliable due to the difficulty of the sampling scheme. C. Algorithm to identify epilepsy patients from administrative datasets among the major achievements of our project is the lessons learned and the experience amassed on identifying critical components of the clinical decision making process. The two epileptologists (BW and RT) and the neurology nurse practitioner (GS) have a cumulative total of 65 years of clinical experience in neurology and epileptology. The extensive and elaborate data abstraction tool along with the 3,983 charts that have been reviewed provided very useful information to develop an algorithm among commonly miscoded differential diagnoses of seizure disorders. Hence, we developed four sets of algorithms regarding persons coded as Syncope (780.2), Febrile Seizure (780.31), Seizure NOS (780.39), and Epilepsy (345.xx), each of which are described below. 63

72 1. For cases coded as 780.2: Syncope--If a visit is coded as but previous visits are noted as coded for or 345, then the diagnosis is less likely to be and it is important to consider or 345. If antiepileptic medications (AEDs) are listed or an AED drug level was done, then it is less likely to be and 345 should be more strongly considered. If there is presence of a vagal nerve stimulator (VNS), VNS interrogation/parameters adjusted (CPT codes or 95974) or VNS implanted, the code 345 must be considered. If there is a diagnosis for a genetic, neurologic, developmental or metabolic disorder, it is less likely to be and more likely to be 345. If there is a code for epilepsy surgery, then the code is a 345 and should not be coded. Figure 11 depicts the decision path for For visit coded as : Febrile seizure disorder if a person is less than 6 months or greater than 6 years and has been coded as a febrile seizure disorder, then that code is incorrect due to the diagnostic criteria and should be changed to a or 345 code. If there is a concurrent illness that could cause fever and/or fever is coded, then is the likely code. If a visit is coded as but previous visits are noted as coded for or 345, then the diagnosis is less likely to be and it is important to consider or 345. If antiepileptic medications (AEDs) are listed or an AED drug level was done, then it is less likely to be and 345 should be more strongly considered. If there is presence of a vagal nerve stimulator (VNS), VNS interrogation/parameters adjusted (CPT codes or 95974) or VNS implanted, the code 345 must be considered. If there is a diagnosis for a genetic, neurologic, developmental or metabolic disorder, it is less likely to be and more likely to be 345. If there is a code for epilepsy surgery, then the code is a 345 and should not be coded. Figure 12 is a graphic depiction of the decision rules for

73 Figure 11 Decision algorithm for code 3. For visits coded as : Seizure, NOS if a visit is coded as but previous visits are noted as coded for or 345, then the diagnosis is less likely to be and it is important to consider 345. If antiepileptic medications (AEDs) are listed or an AED drug level was done, then it is less likely to be and 345 should be more strongly considered. If there is presence of a vagal nerve stimulator (VNS), VNS interrogation/parameters adjusted (CPT codes or 95974) or VNS implanted, the code 345 must be considered. If there is a diagnosis for a genetic, neurologic, developmental or metabolic disorder, it is less likely to be and more likely to be 345. If there is a code for epilepsy surgery, then the code is a

74 and should not be coded. Figure 13 is a graphic depiction of the decision rules for Figure 12. Decision algorithm for code 4. For visits coded as 345.xx: Epilepsy disorders if a visit is coded as 345 but previous visits are noted as coded for or 345, then the diagnosis is less likely to be and it is important to consider 345. If antiepileptic medications (AEDs) are listed or an AED drug level was done, then 345 should be more strongly considered. If there is presence of a vagal nerve stimulator (VNS), VNS interrogation/parameters adjusted (codes or 95974) or VNS implanted, the code 345 must be considered. If there is a diagnosis for a genetic, neurologic, 66

75 developmental or metabolic disorder, it is more likely to be 345. If there is a code for epilepsy surgery, then the code is a 345. Figure 14 is illustrative chart of the decision procedures to establish or rule out the diagnosis of epilepsy. Figure 13. Decision algorithm for code 67