2

Surveillance, Measurement, and Data Collection

Existing surveillance data on epilepsy do not provide current or complete information on how this disorder affects the U.S. population. This information is critical to guide prevention and intervention efforts, service delivery programs, quality improvement efforts, and health policy. Currently epilepsy-related data are not standardized across studies, which limits the accuracy of case ascertainment and coding and hinders monitoring of health services and quality of life. In addition, epilepsy is not routinely included in major population surveys, registries, and other databases. Actions needed to provide more timely information on a number of key attributes of the epilepsies—such as incidence, prevalence, comorbidities, services utilization, and costs—include the standardization of definitions and criteria for epilepsy surveillance and research as well as the continuation and expansion of epilepsy-related data collection from a variety of sources. The increasing use of electronic health records, which can be linked across providers and payers, may facilitate the gathering of surveillance data.

Data collection is the first step toward better classification and understanding of the problems individuals with epilepsy and their families face. These data are critical to position us to make informed decisions on deploying limited resources … [to] improve the life of individuals and their families…. We are dealing with a poorly addressed public health problem, and we urge you to help us better define its many dimensions and magnitude in order to begin to offer desperately needed solutions.

-Michelle Marciniak



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2 Surveillance, Measurement, and Data Collection Existing surveillance data on epilepsy do not provide current or complete information on how this disorder affects the U.S. population. This informa- tion is critical to guide prevention and intervention efforts, service delivery programs, quality improvement efforts, and health policy. Currently epilepsy- related data are not standardized across studies, which limits the accuracy of case ascertainment and coding and hinders monitoring of health services and quality of life. In addition, epilepsy is not routinely included in major popula- tion surveys, registries, and other databases. Actions needed to provide more timely information on a number of key attributes of the epilepsies—such as incidence, prevalence, comorbidities, services utilization, and costs—include the standardization of definitions and criteria for epilepsy surveillance and research as well as the continuation and expansion of epilepsy-related data collection from a variety of sources. The increasing use of electronic health records, which can be linked across providers and payers, may facilitate the gathering of surveillance data. Data collection is the first step toward better classification and understand- ing of the problems individuals with epilepsy and their families face. These data are critical to position us to make informed decisions on deploying limited resources . . . [to] improve the life of individuals and their families. . . . We are dealing with a poorly addressed public health problem, and we urge you to help us better define its many dimensions and magnitude in order to begin to offer desperately needed solutions. –Michelle Marciniak 49

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50 EPILEPSY ACROSS THE SPECTRUM P ublic health surveillance systems provide public health agencies, health care providers, policy makers, and the general public with critically important information on the health of people in the United States. Data collected through these efforts provide better understanding of a health condition’s burden (e.g., frequency, severity, impact on function- ing and quality of life, health care use, cost) and risk factors for its onset, comorbidities, and outcomes. This information facilitates priority setting, program development, and evaluation decisions (IOM, 2011a; Trevathan, 2011). Surveillance for public health is defined as “the ongoing, systematic collection, analysis, interpretation, and dissemination of data regarding a health-related event for use in public health action to reduce morbidity and mortality and to improve health” (German et al., 2001); surveillance may include the collection of data from a variety of sources, including registries and disease-specific reporting systems, surveys, and administrative and clinical data sets (CDC, 2011f).1 This chapter describes the need for more comprehensive, timely, and accurate epilepsy surveillance by • discussing gaps in current data, • assessing the measurement and methodological challenges of col- lecting data, and • reviewing available data sources. It also discusses how epilepsy surveillance might be improved by enhanc- ing data collection and standardizing methods of measurement and case ascertainment. Epilepsy surveillance data inform all of the other chapters of this report. However, current epilepsy surveillance resources and mechanisms are inadequate, and improvements are necessary to increase understand- ing of the epidemiologic aspects of epilepsy and to identify effective action in prevention, health care, and community services, as well as education and awareness. At present, public health researchers, policy makers, and advocates are “flying blind” due to the lack of adequate epilepsy surveil- lance data and infrastructure (Trevathan, 2011). While the focus of this chapter is on epilepsy surveillance and data collection in the United States, the assessment is informed by epilepsy surveillance efforts internation- ally as well as by surveillance systems for other health conditions in the United States. The committee’s vision for effective epilepsy surveillance involves the development of active and passive data collection systems that follow stan- 1 As noted in the Data Collection section, data collected through these sources can also be used for epidemiologic research, including longitudinal cohort studies such as the Rochester Epidemiology Project (discussed below).

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51 SURVEILLANCE, MEASUREMENT, AND DATA COLLECTION dardized methodologies to obtain valid measurement. Such systems need to be coordinated, comprehensive, accurate, and timely. In times of economic constraint, collaborative efforts may facilitate this surveillance, which will provide critical information to stakeholders at the local, state, regional, and national levels. Surveillance data can be used to achieve a range of goals, including • guiding programs and policies aimed at prevention, treatment, and rehabilitation; • detecting barriers in health care access and quality, such as delayed diagnosis, treatment gaps, and disparities; • determining optimal service delivery models that are cost-effective; and • providing a basis for further epidemiologic and health services research. GAPS IN INFORMATION ABOUT EPILEPSY We need factual data. This would include the incidence and severity of re- fractory [epilepsy], disparities in access to care, comorbidities . . . and [epi- lepsy’s] impact financially and on quality of life for patients and providers. –Gary Mathern At the heart of public health surveillance are data. The information presented in Chapter 1 and throughout this report about the significant burden of the epilepsies on health and quality of life is based on data col- lected through a variety of surveillance data sources, such as administrative and clinical records, population-based surveys, and registries (discussed later in the chapter). To meet the informational needs of the broad epilepsy community, data collected through epilepsy surveillance systems should be able to provide timely and accurate estimates of incidence and prevalence2 (Chapter 3); • • etiology (i.e., causes), risk factors, and comorbidities (Chapter 3); • health status and quality-of-life outcomes (Chapters 3 and 6); • health disparities (Chapter 4); • quality of care (Chapter 4); and • access to and utilization of health care and community services and costs (Chapters 4 and 6). 2 Incidence is the number of new cases of a disease or disorder in a set period of time; preva- lence is the number of existing cases of a disease or disorder at a given point in time.

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52 EPILEPSY ACROSS THE SPECTRUM For example, accurate and detailed surveillance data on the etiologies of, and risk factors for, epilepsy are needed in order to identify opportunities for public health efforts to prevent epilepsy from developing in the first place or to prevent a range of possible consequences. Furthermore, because the burden of the comorbidities often outweighs that of the epilepsy itself, surveillance of its comorbidities is also crucial to appropriate targeting of public health interventions. Currently, gaps in data collection prevent ac- curate and timely information to monitor and evaluate these basic public health dimensions of the epilepsies, one of the most common neurological disorders in the United States. The data generated by the Rochester Epidemiology Project 3 have formed the foundation of much of the current understanding about the epilepsies in the United States. This project’s contributions have been substantial, but many of the epidemiologic estimates it has generated are outdated and may not reflect the diversity of the current U.S. population. Up-to-date and representative data are needed on epilepsy trends and dis- parities in specific populations in order to generate actionable information that enables the public health community to target its resources for preven- tion and intervention in areas that will produce maximum benefit. Obtaining a complete picture of epilepsy in the United States would re- quire collecting many data elements (Box 2-1). Although all these elements are important—and in an ideal world would be available at the national, state, and local levels—some are more difficult to obtain than others and compromises will need to be made, given limited resources and technology. However, developing the capacity to gather many, if not all, of these data elements—using validated instruments and different data sources on rep- resentative populations and subgroups over time—will enable an informed public health response to promote health and well-being for people with epilepsy. IMPROVING MEASUREMENT AND METHODOLOGY Improving epilepsy surveillance will involve overcoming several chal- lenges in measurement and methodology. Many of the data currently 3 The Rochester Epidemiology Project (http://www.rochesterproject.org/) is a collaborative effort by health care providers in Olmsted County, Minnesota, and the surrounding area. This project links medical records across practices that may see Olmsted County residents, making the linked records available to researchers. These records include inpatient, outpatient, and emergency room visits. Records linkage-based research is ongoing in Rochester for a variety of disorders. Data on epilepsy from 1935 to 1994 have been analyzed to provide estimates of epilepsy incidence, prevalence, and cost, as well as information on etiologies, risk factors, and outcomes (e.g., Annegers et al., 1996; Begley et al., 2001; Ficker et al., 1998; Hauser et al., 1991, 1993). Additional projects undertaken include studies on status epilepticus and the genetics of the epilepsies (e.g., Hesdorffer et al., 1998; Ottman et al., 1996).

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53 SURVEILLANCE, MEASUREMENT, AND DATA COLLECTION EPILEPSY DATA ELEMENTS Box 2-1 • ge (including birth date when A njuries I possible) Nutritional problems • Sex • Health insurance status • Geographic location • Health care • Race/ethnicity Source of care • Personal and family demographics ype and frequency of use T elationship status R Quality of care Household composition atient’s perceptions of care P Educational attainment quality Employment status Direct costs –Occupation • U se of informal and community ncome I services –Personal Type of caregiver –Household Type of community service • Current health status • Quality of life General health status verall quality of life O pilepsy-specific status E Seizure worry Current medical treatment Emotional well-being status Energy-fatigue –Surgical status Cognitive functioning Disability status –Attention or concentration Mortality, including sudden un- –Memory expected death in epilepsy and edication effects M other epilepsy-related deaths Social functioning • Epilepsy-related ole limitations R Age at onset –Emotional eizure type and frequency S –Physical Epilepsy syndrome Stigma Etiology –Enacted –Stability of underlying –Felt condition ndirect costs I Severity • Comorbidities Somatic disorders Neurological disorders Mental health conditions Cognitive disorders nfectious diseases I nfestations I Physical disabilities SOURCE: Adapted from Thurman et al., 2011. collected cannot be validated, are not comparable, cannot be used to un- derstand trends over time, are not representative of the U.S. population, and cannot be analyzed for important population subgroups. Many of these challenges are shared by clinical researchers as well, who are cur- rently collaborating on the Common Data Elements project (described

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54 EPILEPSY ACROSS THE SPECTRUM below). The following are among the major measurement and methodolog- ical considerations that are barriers to epilepsy surveillance and research: a lack of standardization in case ascertainment4 and diagnos- • tic accuracy, such as the use of varying definitions and coding algorithms;5 • variations in measurement of health service use, quality, access, and costs; • heterogeneous approaches to assessing the impact of epilepsy on health status and quality of life; and • challenges in identifying and recruiting health care providers and people with epilepsy to participate in surveillance and research projects. Case Ascertainment and Diagnostic Accuracy Unlike other disorders that have definable stages, . . . we have not defined epilepsy for epidemiologic [research] in a reproducible manner. –Frances Jensen Determining timely and accurate incidence and prevalence estimates of epilepsy requires identifying individuals within a population who have epilepsy and determining when they developed the disorder. Although this sounds simple, it is unfortunately quite difficult. Case ascertainment and diagnostic accuracy depend on a number of factors, including standardiza- tion and validation6 of definitions and coding of the data, as well as the strengths and limitations of the source of the data (discussed later in this chapter). Surveillance of the epilepsies strives toward complete ascertainment of people with epilepsy. For epidemiologic studies, this is particularly impor- tant to reduce the chance of artificially increasing or decreasing the propor- tion of the study population with epilepsy. Under- or overestimating the number of people with epilepsy in a population can occur for many reasons. For example, if data from health care facilities are used to identify who has epilepsy, some cases will be missed because some people with epilepsy never seek medical care for their seizures (Beran et al., 1985) or cannot access 4 Case ascertainment is the identification and inclusion of people who meet the criteria be- ing studied. 5 An algorithm is the combination of codes and other criteria used to identify a case. 6 Validation involves testing and verifying the accuracy of a specific research method, such as the ability of a set of criteria to identify > 90 percent of the individuals in a population who have epilepsy.

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55 SURVEILLANCE, MEASUREMENT, AND DATA COLLECTION health care because of socioeconomic or health system barriers (Szaflarski et al., 2006). Variations in the Definition of Epilepsy The use of varying definitions of epilepsy leads to some studies includ- ing cases that others would not, which increases the potential for under- or over-reporting epilepsy incidence and prevalence and prevents researchers from being able to compare data across sites and studies. Efforts are on- going within the epilepsy research field to develop and use standardized definitions and algorithms for identifying epilepsy, epilepsy remission, re- fractory epilepsy, and active epilepsy, despite using different data sources. As described in Chapter 1, the occurrence of two or more unprovoked seizures separated by at least 24 hours is the broad operational definition of epilepsy, which was proposed by the International League Against Epilepsy (ILAE, 1993) and remains the most widely accepted. However, alternative definitions of general epilepsy and epilepsy subgroups continue to be dis- cussed, and accurate and consistent case ascertainment depends on translat- ing these definitions into standard data collection measures; for example, the length of time covered by the term “active” epilepsy may need to be shorter in surveys than in studies of medical records, in order to account for memory recall of survey respondents. The strengths and weaknesses of current methods of case ascertainment in a number of data sources used for epilepsy surveillance are considered later in this chapter. Diagnostic Challenges On the clinical level, epilepsy can be difficult to diagnose (Chapters 1 and 4) because the health care provider rarely sees the seizure occur and accurately identifying the nature of the seizure or seizure-like event involves determining whether it was due to electrical disruptions in the brain (i.e., a seizure) or other reasons and whether it was provoked (e.g., by a fever). For example, seizures suffered during alcohol withdrawal or seizure-like events with a psychological basis may incorrectly be assumed to be epilepsy and may lead to over-reporting of epilepsy cases. On the other hand, under- reporting of epilepsy may occur if the health professional does not recog- nize the symptoms as a seizure. Further, if seizure activity begins following a brain insult such as stroke, the focus may be on the primary diagnosis of cerebrovascular disease, and the seizures may not be diagnosed as epilepsy. Educating primary care providers and other health professionals regarding seizures and epilepsy can lead to more accurate diagnoses (Chapter 5), as can tools such as decision prompts in electronic health records (EHRs) to guide health professionals toward accurate diagnoses.

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56 EPILEPSY ACROSS THE SPECTRUM Diagnostic and Treatment Coding Health care diagnoses and treatment decisions are coded in the pa- tient’s medical record, generally with International Classification of Disease Clinical Modification (ICD-CM) codes, for billing and follow-up purposes. Researchers use these disease- or disorder-specific diagnostic and treatment codes to identify records for individuals with specific health conditions. Using codes for case ascertainment is more cost-effective than conducting reviews of each record by hand or interviewing each individual in the study population (Jetté et al., 2010). Furthermore, the current nationwide drive to implement EHRs (discussed later in the chapter) offers unprecedented opportunities to capture, share, and analyze coded data for surveillance purposes. Nonetheless, epilepsy is challenging to diagnose and match to the appropriate code, and variations in coding practices can lead to over- or under-reporting of epilepsy. Both the ninth revision of the coding structure (ICD-9-CM), which is currently used in the United States, and the ICD- 10-CM classification, which will be implemented in 2013 (HHS, 2009), have a number of codes for different types of seizures, signs, and symptoms and a limited number of codes for epilepsy (ICD-9-CM: 345.xx; ICD- 10-CM: G40.x). Currently there are several limitations to the use of codes for surveil- lance purposes. First, the ICD-9-CM and ICD-10-CM versions lack specific- ity in the epilepsy codes with respect to etiology, which limits researchers’ ability to elucidate risk factors for epilepsy and report outcomes by cause. Second, coding practices differ; for example, an epileptologist often pro- vides more detailed and accurate information for coding as to type of epilepsy or seizure than an emergency department physician or general neurologist (Jetté et al., 2010). Third, few studies have been conducted to validate the algorithms used to identify epilepsy in different health care settings and across age groups; standardization is lacking in the codes used and in the period of “look back” to determine the incidence of epilepsy (see discussion of the data-gathering effort below). “Seizure, convulsion, epi- lepsy” were systematically reviewed as part of the Food and Drug Admin- istration’s (FDA’s) Mini-Sentinel pilot project (discussed later in the chapter) to establish validated algorithms that can be applied in surveillance using administrative and claims data, and Kee and colleagues (2012) found that currently the validity of algorithms for identifying epilepsy in comparison to non-epilepsy seizures varies and further research is needed. Validation studies have found that the presence of multiple occurrences of epilepsy codes—along with record of a prescribed seizure medication— improves accuracy in identifying someone with epilepsy (Holden et al., 2005a,b). Importantly, the algorithms used by Holden and colleagues re- quired that multiple data types be linked (e.g., claims data, data from a

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57 SURVEILLANCE, MEASUREMENT, AND DATA COLLECTION visit to a health professional, pharmacy data, membership data). Some studies have the time and resources to review the medical records in order to validate a subsample of the population (e.g., Parko and Thurman, 2009; Pugh et al., 2008); this enables understanding of the degree to which false positives and false negatives exist. Some studies combine information from the medical record with information from patient interviews and subject this information to review by experts to reach a consensus diagnosis (e.g., Benn et al., 2008; Berg et al., 1999; Olafsson et al., 2005). In these circum- stances, cases are most often excluded due to syncope (i.e., fainting) or to seizure-like events with a psychological basis (Scheepers et al., 1998; Smith et al., 1999). For surveillance work, algorithms need to follow agreed-upon definitions and sets of codes so that searches of coded data sources will consistently retrieve cases with epilepsy. Even given accurate coding, challenges remain for many studies because not all records are coded or complete with all required data, such as type of physician seen and race/ethnicity of the patient. Over time, as patients move from one health care provider or system to another, duplicate case counts can occur and attempts to measure incidence are compromised by the movement of patients within and between health care systems. Recently, researchers have begun using natural language processing to search the free text of the EHR in order to validate the ICD codes for specific conditions, such as pneumonia, pancreatic cancer, and psoriatic arthritis (Dublin et al., 2011; Friedlin et al., 2010; Love et al., 2011), and for other purposes, such as identifying patients who were due for recommended screening tests (Denny et al., 2012) or postoperative complications (Murff et al., 2011). One of the next steps in the validation of epilepsy codes is the use of natural language processing to determine their accuracy. Self-Reporting Through Surveys Researchers often use population-based surveys to collect health data. To identify individuals with epilepsy, an initial set of screening questions is generally asked, and these questions vary from survey to survey. These population surveys, such as the Behavioral Risk Factor Surveillance System (BRFSS) (discussed below), rely on self-reports of physician-diagnosed epi- lepsy and tend to generate considerably higher prevalence estimates than those from medical records or community-based studies. Following up on an initial identification of persons with epilepsy based on self-report, more in-depth questions and validation or review, such as medical examinations or review of medical records, help to reconcile these estimates. For example, a prevalence study in New York City produced initial rates of epilepsy similar to the BRFSS; additional information to aid case ascertainment and expert review of responses by a panel of epileptologists lowered prevalence

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58 EPILEPSY ACROSS THE SPECTRUM levels to those of other studies (Kelvin et al., 2007). Generating accurate incidence estimates from self-report population-based studies is not pos- sible due to the difficulty of validating cases and faulty recall concerning the timing of epilepsy onset. In addition, some types of information may not be captured reliably through the self-reports of people with epilepsy and their families; for example, some studies have found that seizure fre- quency counts are underestimates because the majority of respondents are unaware of some seizures (Akman et al., 2009; Blum et al., 1996; Hoppe et al., 2007). A focus is needed on identifying the screening questions that accurately determine the epilepsy status of individuals and contribute to information on overall prevalence. Recently, Brooks and colleagues (2012) validated the use of the five epilepsy-related screening questions developed by the Centers for Disease Control and Prevention (CDC) Epilepsy Group, which have been used by the BRFSS and other population-based surveys, in a sample of patients who receive care at a tertiary care center in Boston. Their findings suggest that prevalence estimates of lifetime and active epilepsy based on self-reports, while slightly higher than estimates based on medical review, are reasonably accurate and valuable for population-based studies. Further work is needed to determine whether their findings are generalizable to other populations. Because individuals may say they have a seizure disorder and not realize they have epilepsy and because communities may differ in the words used for seizures and epilepsy, as well as the extent and nature of the stigma associated with epilepsy, questions should not only follow stan- dardized concepts and methods, but also be culturally adapted, designed using the principles of clear communication, and validated in the specific population being studied. Like other conditions with a similar prevalence, obtaining sufficient data for studying specific segments of the population (e.g., racial/ethnic minorities, age groups) is difficult because large sample sizes are needed. Further, surveys generally do not collect data on certain subpopulations such as homeless individuals or institutionalized individu- als, and many do not include children. Monitoring Health Care Quality, Access, and Direct Costs In the last few decades, greater attention has been focused on the need to conduct surveillance of the quality, access, and value aspects of health care in order to maximize health outcomes and control costs (Chapter 4). Quality of care can be measured in several ways: • by characteristics of health care structure (e.g., type of health care provider seen during visits by patients with epilepsy, type of health care facility where care was sought such as an epilepsy center),

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59 SURVEILLANCE, MEASUREMENT, AND DATA COLLECTION • by elements of the process during a visit by the patient to a health professional (e.g., procedure or test ordered, such as video- electroencephalograph [EEG]), or • by data on the individual’s outcomes or resulting health status (e.g., seizure frequency, disability status) (Brook et al., 1996). Collecting data on the process of care may provide the most sensitive estimates of high-quality care. Performance metrics derived from evidence- based practices can be used to assess and incentivize high-quality care. Importantly, to effect change, these metrics should be oriented to the health care provider’s direct role and responsibilities (Giuffrida et al., 1999). As discussed in Chapter 4, there has been significant progress recently in de- veloping performance metrics specific to epilepsy, such as counseling about treatment side effects or referring a patient with refractory epilepsy for surgical evaluation (Fountain et al., 2011; Pugh et al., 2011). Much work remains to implement the metrics and establish a measurement framework and consistent mechanisms for monitoring the quality of different aspects of epilepsy care. EHRs are a possible source for the collection of relevant data for measuring quality. One goal of the implementation of EHRs is to improve the quality, safety, and efficiency of care by collecting structured data that will allow efficient information exchange (CMS, 2010b; HHS, 2010a).7 Assessing whether people with epilepsy have adequate access to care can be measured by examining potential (e.g., having a usual source of care), realized (e.g., visits to a physician), and outcome (e.g., health status) metrics (Andersen and Aday, 1978). The presence of significant differences in access metrics helps identify health care disparities between disadvan- taged individuals or population groups that differ from the general popula- tion in demographic or socioeconomic status but have comparable needs. Factors to consider in measuring access to care include health system factors (e.g., availability of health care resources and providers, accessibility and acceptability of those resources to potential patients) and personal factors (e.g., gender, age, race/ethnicity, geographic location, education, income, type of insurance coverage). Estimates of the frequency of service use and the related costs con- tribute to assessments of the value of health care (Chapter 4). Capacity is needed to accurately identify and track the frequency of service use by an individual and the costs directly related to those services (e.g., physician visits, diagnostic procedures, hospital stays, prescriptions). In measuring service use, data need to be collected about the type of provider seen and 7 Other goals include reducing health disparities, engaging patients and families in their health care, improving care coordination, and improving public health (CMS, 2010b).

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