The goal of this study was to develop an algorithm for detecting epilepsy cases in managed care organizations (MCOs). A data set of potential epilepsy cases was constructed from an MCO's administrative data system for all health plan members continuously enrolled in the MCO for at least 1 year within the study period of July 1, 1996 through June 30, 1998. Epilepsy status was determined using medical record review for a sample of 617 cases. The best algorithm for detecting epilepsy cases was developed by examining combinations of diagnosis, diagnostic procedures, and medication use. The best algorithm derived in the exploratory phase was then applied to a new set of data from the same MCO covering the period of July 1, 1998 through June 30, 2000. A stratified sample based on ethnicity and age was drawn from the preliminary algorithm-identified epilepsy cases and non-cases. Medical record review was completed for 644 cases to determine the accuracy of the algorithm. Data from both phases were combined to permit refinement of logistic regression models and to provide more stable estimates of the parameters. The best model used diagnoses and antiepileptic drugs as predictors and had a positive predictive value of 84% (sensitivity 82%, specificity 94%). The best model correctly classified 90% of the cases. A stable algorithm that can be used to identify epilepsy patients within MCOs was developed. Implications for use of the algorithm in other health care settings are discussed.
We discuss the importance of developmentally based conceptual models and the impact of diagnostic heterogeneity and offer specific recommendations for future intervention research in the area of recurrent pediatric headache.
The diagnosis of migraine headache in children and adolescents is complex and not well understood. This study was conducted to compare diagnostic rates, using various criteria for pediatric migraine, and specific symptom characteristics in a sample of children referred for care to a specialized pediatric headache clinic. A structured interview was used at the patient's initial assessment visit to elicit symptom patterns and therapies attempted for headache. Clinical diagnoses were based on consensus agreement reached by a multidisciplinary team. Statistically derived diagnostic rates based on International Headache Society (IHS), Prensky, Vahlquist and our own criteria were significantly lower than clinical diagnostic rates. IHS diagnostic rates were differentially distributed as a function of race, but no other effects were found for demographic variables on diagnostic rates. Specific symptom patterns, however, varied as a function of race, gender and age of the child. The results underscore the need for comprehensive, developmentally based models of the evolution of migraine headache as a foundation for future research and the further development of clinically sensitive diagnostic criteria for pediatric migraine.
Chronic daily headache in children and adolescents has not been well described. We analyzed data for 37 children and adolescents who presented with chronic daily headache to our Pediatric Headache Clinic over a 2-year period. These youngsters had five distinct headache patterns; 40% of them had the "comorbid" pattern, 35% were classified with new daily persistent headache, 15% with transformed migraine, 5% with chronic tension-type headache, and 5% could not be classified. There were no significant differences by diagnosis in externalizing and internalizing behaviors, type A behaviors, disability, pain severity, days missed from school, and number of coping skills employed. Children and adolescents with chronic daily headache have distinct clinical patterns, but for the most part, have similar disability. Differences between adult and childhood chronic daily headache are emphasized.
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