Using Administrative Data to Ascertain True Cases of Muscular Dystrophy: Rare Disease Surveillance.

Michael G Smith,Julie Royer,Joshua R Mann,Suzanne Mcdermott

doi:10.2196/publichealth.6720

Abstract

BackgroundAdministrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. Identifying rare but catastrophic conditions is a challenge since approaches for maximizing valid case detection are not firmly established.ObjectiveThe purpose of our study was to explore a number of algorithms in which International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes and other administrative variables could be used to identify cases of muscular dystrophy (MD).MethodsWe used active surveillance to identify possible cases of MD in medical practices in neurology, genetics, and orthopedics in 5 urban South Carolina counties and to identify the cases that had diagnostic support (ie, true cases). We then developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Cases of all types of MD and those with Duchenne or Becker MD (DBMD) that were common to both surveillance systems were examined to identify the most specific administrative variables for ascertainment of true cases.ResultsPassive statewide surveillance identified 3235 possible cases with MD in the state, and active surveillance identified 2057 possible cases in 5 actively surveilled counties that included 2 large metropolitan areas where many people seek medical care. There were 537 common cases found in both the active and passive systems, and 260 (48.4%) were confirmed by active surveillance to be true cases. Of the 260 confirmed cases, 70 (26.9%) were recorded as DBMD.ConclusionsAccuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used.

Highlights

Administrative records that include insurance claims, hospital discharge datasets, and vital records have become important public health tools to understand prevalence of disease in populations [1,2,3,4]
To predict the true cases for those under age 18 years, the variables that were statistically significant were number of times the muscular dystrophy (MD) code was recorded during the study period, having MD identified by an inpatient claim or at least 2 outpatient claims 30 days apart, and being male
To predict the true cases for those identified after age 18 years, we had the following statistically significant predictors: number of times the MD case was recorded during the study period, having MD identified by an inpatient claim or at least 2 outpatient claims 30 days apart, being white, and having another neurologic syndrome coded after the first code of MD in the record

Summary

Introduction

Administrative records that include insurance claims, hospital discharge datasets, and vital records have become important public health tools to understand prevalence of disease in populations [1,2,3,4]. The special case of identifying rare but catastrophic conditions has emerged as a challenge since understanding the effects of these conditions on populations is important for medical, public health, insurance, and advocacy groups [8,9,10]. Administrative records from insurance and hospital discharge data sources are important public health tools to conduct passive surveillance of disease in populations. We developed an algorithm to identify cases based on a combination of ICD-9-CM codes and administrative variables from a public (Medicaid) and private insurer claims-based system and a statewide hospital discharge dataset (passive surveillance). Conclusions: Accuracy of finding a true case in a passive surveillance system was improved substantially when specific diagnosis codes, number of times a code was used, age of the patient, and specialty provider variables were used

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