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.