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Summary: Purpose. The purpose of the present study was to apply computer algorithms to an administrative data set to identify the prevalence of epilepsy, incidence of epilepsy, and epilepsy-related mortality of patients in a managed care organization (MCO).
Methods. The study population consisted of members enrolled in Lovelace Health Plan, a component of Lovelace Health Systems, a statewide MCO headquartered in Albuquerque, New Mexico. Patient records were obtained from July 1996 to June 2001. Four logistic regression models with high sensitivity and specificity were applied to 1-, 3-, and 5-year time frames in which members were continuously enrolled in the MCO. Incidence was defined for patients who did not have an epilepsy-associated code in the 18 months before the first diagnosis entry. Mortality estimates in the population also were assessed by using a matched control group and linkage to a statewide death registry.
Results: The data yielded estimated prevalence rates of 7–10 per 1,000, depending on age, sex, ethnicity, and time interval. Annualized incidence was 47 per 100,000 for members continuously enrolled for 3 years and 71 per 100,000 for members continuously enrolled for 5 years. Crude mortality rates were 2–2.5 times higher for epilepsy patients identified with the algorithms than for the matched controls. Conditional logistic regression indicated that the odds of death for epilepsy patients as compared with controls ranged from 1.24 to 2.06.
Conclusions. Accurate estimation of prevalence, incidence, and mortality rates for epilepsy is an essential component of disease management in MCOs. The algorithms in this project can be used to monitor trends in prevalence, incidence, and mortality to inform decisions critical to improving the health care needs and quality of life for patients with epilepsy.
Authorities estimate that 2.5 million people are being treated for epilepsy in the United States (1). Each year, 150,000 people are newly diagnosed with epilepsy, and many more cases remain undetected (2). Most studies conducted in developed countries indicate wide variability in the prevalence of epilepsy, ranging from four to 10 cases per 1,000 persons (3–5). The actual rate may be significantly higher, however, because only half of patients with epilepsy are diagnosed within the first 6 months of the disorder (6). Although most patients can control their seizures with proper diagnosis and treatment, it is estimated that 20–25% of patients with seizures do not respond well to treatment (1) and do not achieve proper seizure control with current drug therapies (5).
Efforts to ascertain newly diagnosed cases of epilepsy are particularly challenging, as illustrated by studies that have estimated an incidence in the general population ranging from 50 to 100 per 100,000 people per year (3,4,7). A recent meta-analysis of 40 studies also found wide variability in incidence estimates and in the quality of results, depending on the method, geographic area, demographics, definitions, and classifications of epilepsy and epileptic seizures used (8). Most of these studies were conducted outside of the United States. Although incidence is variable among domestic and foreign studies, a consistent pattern occurs in relation to age. The onset of epilepsy occurs most frequently in the earliest years of life, decreases in adolescents, remains relatively stable in the middle years, and then increases for those aged 60 years and older (1,4,7,9). Epilepsy patients of all ages have an elevated standardized mortality rate 2 to 3 times higher than that of the general population (3,5). Excess deaths may be caused by cerebral diseases that are associated with seizures, fatal injuries during seizures, suicide, and sudden unexpected death in epilepsy (5,10–14). The risk of sudden unexplained death is roughly 20 times higher among epilepsy patients than that in the general population (4).
In managed care organizations (MCOs), disease-management and other programs to promote adherence to best-practice guidelines have grown dramatically in recent years because they promise to decrease costs, promote uniform practice patterns, and significantly improve health care outcomes. A crucial early step in any disease-management program is to identify accurately patients with the target disease so that administrators can estimate incidence and prevalence, develop and focus interventions, and assess the impact of quality-improvement programs. Health care administrators in MCOs could benefit from an accurate method of assessing the distribution of epilepsy patients so that they can allocate resources for the health care needs of this patient population. Clinical diagnosis of epilepsy can be a complex process, as no specific biologic markers exist and recurring seizures are associated with a wide range of disease conditions. Despite the uncertainties of using administrative data for determining diagnostic rates, disease-management studies of epilepsy can provide essential information for primary care physicians in MCOs, who must work collaboratively with specialists to diagnose, classify, and treat patients with epilepsy (2).
Record-linkage studies using rigorous methods and analytic approaches to examine the incidence and prevalence of chronic diseases in MCOs are limited. In the case of epilepsy, accurate case identification from MCO electronic claims data is difficult for several reasons, such as the use of antiepileptic drugs (AEDs) for multiple conditions. One recent study, however, used logistic regression to develop and validate an effective method for ascertaining newly diagnosed breast cancer patients in an MCO (15). Although this study provides a useful model for future record-linkage studies in MCOs, researchers have emphasized that epilepsy studies must specifically take into account the classification of seizures, risk factors, and geographic, age, sex, and ethnic differences to provide reliable information about prevalence and incidence (8). The purpose of this study was to develop a sensitive and specific algorithm to determine epilepsy prevalence, incidence, and mortality in an MCO population.
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The purpose of the final phase of this project was to apply sensitive and specific computer algorithms to estimate the prevalence and incidence of epilepsy in an MCO population. The algorithms were developed during the initial phases of the project (16) for use by other MCOs to define their epilepsy patient populations. The results from this study are consistent with the literature indicating overall prevalence estimates of seven to 10 per 1,000, with higher estimates for those patients aged 65+ years ranging from 12 to 18 per 1,000. These findings are similar to the results of other studies showing that approximately 1.5–2% of the elderly population is affected by epilepsy (3,7). This study also supports a consistent finding in the epidemiology literature that males are more likely than females to be affected by epilepsy (7). Although limited previous research has indicated that epilepsy is more common among Hispanics than non-Hispanics (6), this study demonstrated the opposite effect, suggesting heterogeneity among populations classified as Hispanic.
A defined time frame was used to determine a new epilepsy case, and incidence estimates were provided for members continuously enrolled for 3- and 5-year periods. The annualized incidence estimate obtained for the 36-month cohort in this study (47 per 100,000) was close to the range of expectations for a 1-year incidence rate (50–100 per 100,000) as reported in the literature (2). The annualized incidence rate for members enrolled for 60 months (71 per 100,000) was also within the range expected based on reports of annual incidence rates in the literature. As in other studies, we found that determining incidence in an MCO is challenging (8). Even when case definitions are well established, it is difficult to identify new cases because MCO membership is in constant flux, with new members enrolling and others leaving the plan. Identifying new cases also is difficult because, even though many of these patients are new to the MCO, they are not necessarily new epilepsy cases. Preexisting diagnoses are often missed when patients enter an MCO.
Limited research is currently available to ascertain accurate prevalence and incidence estimates of epilepsy patients receiving services in MCOs. Most studies have been conducted on broader populations in larger community settings, resulting in variable incidence and prevalence estimates. This variation is primarily due to problems with diagnosis, different case definitions, and methodologic variations (9). Similar issues arise in studies that rely on administrative data to quantify other medical conditions. One example is a study that developed and evaluated a method for ascertaining newly diagnosed breast cancer cases by using multiple sources and Medicare claims data (15). The current study parallels the breast cancer study by applying similar methodologic and analytic strategies to develop algorithms to quantify prevalence and incidence of epilepsy in an MCO population. The different models allow, with varying degrees of sensitivity and specificity, the use of any one or all of the three types of administrative data that would be available to a typical MCO or medical group: diagnosis codes, procedure codes, and pharmacy codes. When using only a single source of data (e.g., diagnosis codes), the organization or researcher must be aware that the sensitivity and specificity of the models will be different than those in a model using all three types of data. The models built from fewer data sources are less sensitive. Concerning the availability of demographic information on MCO enrollees, both age and gender would certainly be available for any MCO population and would always be able to be included in the model.
This study also examined mortality rates among epilepsy patients in an MCO population. Two issues make it difficult to calculate epilepsy-related mortality rates. The first is related to the constantly changing membership in the MCO. As the population changes, the effects on mortality rates are unknown. The second issue concerns the definition of an epilepsy-related death. Such deaths may be caused by injuries or other related illnesses. The underlying cause of death is typically attributed to the most recent event and not epilepsy, so epilepsy-related mortality may be underreported. For this study, we determined mortality by linking a list of prevalent cases and controls from the utilization database with state death-certificate information to obtain primary cause of death and date of death. The mortality results indicated a higher likelihood of death for the identified epilepsy cases than for the matched controls. These results are consistent with the increased risk of mortality associated with epilepsy that has been reported in the literature (3).
Most MCOs, including the MCO whose data were used in this study, do not routinely collect information on the race/ethnicity of their enrollees. Therefore surrogates for ethnicity are useful to provide ethnic comparisons or examine ethnicity as a confounder. Clearly, the GUESS software is primarily useful in the Southwest, where the primary ethnic groups are Hispanic, non-Hispanic white, and Native American. The software would not be useful to study populations with a significant proportion of one or more multiethnic groups for which surname is not useful in assigning ethnicity (e.g., African Americans). The epilepsy identification algorithm and associated models that are the central focus of this article, however, do not require race or ethnicity as variables. Thus MCOs, other health care providers, and researchers can use the algorithm effectively in the absence of direct or indirect information on member ethnicity.
The methods developed in this study may be useful in future research on epilepsy in other health care or MCOs. This study used several models (algorithms) that can be used to estimate prevalence and incidence across several different time frames within MCOs. This approach should offer MCOs flexibility in selecting the model that is most appropriate for their data systems, depending on the quality and types of claims and utilization data captured by their organization. From the development of the algorithms used in this study, it appears that the mere presence of diagnostic codes for epilepsy or seizures (ICD–9–CM codes 345.xx and 780.3x) is insufficient for identifying cases of epilepsy in health care records because these codes alone lack both high sensitivity and positive predictive value (16). However, if multiple occurrences of such codes in a single patient record are considered, and if indicators of AED prescription are also included, then sensitivity and positive predictive value improve considerably. Thus among the algorithms used in this study, model 2 showed a distinct improvement over model 1. However, only small additional improvements were seen in the sensitivity and predictive value of epilepsy case detection when CPT procedure codes (primarily for EEG) or ICD–9–CM codes for psychiatric comorbidity were added, as reflected in models 3 and 4. Overall, being able to select a specific model offers other MCOs the ability to define accurately their existing patient populations, and such identification should assist in the allocation of health care expenditures.
In the first two phases of this study, measurements of sensitivity and specificity of models 1 and 2 indicated that they could correctly classify 88% and 91% of cases, respectively (16), suggesting that these models should perform well when used to estimate epilepsy prevalence and incidence. In application, as demonstrated in the third phase of this study, the model yielded credible estimates of prevalence estimates that are generally consistent with the findings of comparable studies of epilepsy prevalence. The estimates varied only modestly when derived from study population members with 1, 3, and 5 years of continuous enrollment. Thus the models appear to meet expectations that they can provide valid estimates of epilepsy prevalence. The application of our method to assess incidence, however, yielded less consistent estimates. Although these were within the range of incidence rates found among other studies, substantially more variation was found between estimates derived from study members with 3 and 5 years of continuous enrollment. Additional study of methods to apply these models may be needed to ensure more reliable estimates of epilepsy incidence.
In conclusion, these methods show promise for broader use in the study of epilepsy occurrence in MCOs and other defined populations that have linked administrative data covering inpatient and outpatient services. A primary advantage of these methods is their comparatively low cost, because they rely on existing data. Furthermore, these methods may be useful for related research (e.g., studies of secular trends in epilepsy occurrence and studies of health care service delivery). Finally, these methods may assist in the identification and sampling of cohorts of people with epilepsy for follow-up studies. Epidemiologic research and surveillance are important to assess the public health burden of epilepsy, to provide accurate information to assist in policy development, to ensure necessary services for those with epilepsy. The methods described are useful tools for these purposes.