Elsevier

Epilepsy Research

Volume 102, Issue 3, December 2012, Pages 173-179
Epilepsy Research

Development and validation of a case definition for epilepsy for use with administrative health data

https://doi.org/10.1016/j.eplepsyres.2012.05.009Get rights and content

Summary

The objective of this study was to develop and validate coding algorithms for epilepsy using ICD-coded inpatient claims, physician claims, and emergency room (ER) visits. 720/2049 charts from 2003 and 1533/3252 charts from 2006 were randomly selected for review from 13 neurologists’ practices as the “gold standard” for diagnosis. Epilepsy status in each chart was determined by 2 trained physicians. The optimal algorithm to identify epilepsy cases was developed by linking the reviewed charts with three administrative databases (ICD 9 and 10 data from 2000 to 2008) including hospital discharges, ER visits and physician claims in a Canadian health region. Accepting chart review data as the gold standard, we calculated sensitivity, specificity, positive, and negative predictive value for each ICD-9 and ICD-10 administrative data algorithm (case definitions). Of 18 algorithms assessed, the most accurate algorithm to identify epilepsy cases was “2 physician claims or 1 hospitalization in 2 years coded” (ICD-9 345 or G40/G41) and the most sensitive algorithm was “1 physician clam or 1 hospitalization or 1 ER visit in 2 years.” Accurate and sensitive case definitions are available for research requiring the identification of epilepsy cases in administrative health data.

Introduction

The global burden of epilepsy is high, with 50 million cases worldwide and a prevalence of 4–8 per 1000 (Forsgren et al., 2005, World Health Organization, 2006). While epilepsy is the second most commonly reported neurological condition worldwide (Murray et al., 1994), there are currently no ongoing surveillance activities for epilepsy regionally, provincially, or nationally in Canada. International Classification of Disease (ICD) codes are used worldwide and are routinely collected and maintained in administrative databases. These data are a valuable source of information including both inpatient and outpatient healthcare encounters. As such, administrative data consisting of ICD-9-CM or ICD-10 codes can be used for studies and surveillance and have already been used successfully in Canada and the US for surveillance of acute and chronic conditions including diabetes and traumatic brain injury (Asghari et al., 2009, Centers for Disease Control and Prevention, 2011, Chen et al., 2010, Hux et al., 2002).

Epilepsy ICD-9-CM and ICD-10 coding has previously been validated in emergency room (ER) and hospital discharge data in a large Canadian region (Calgary, Alberta, Canada) (Jette et al., 2010), as well as in other countries (Christensen et al., 2007, Parko and Thurman, 2009, Pugh et al., 2008). However, in order to develop a comprehensive national population-based epilepsy registry, it is also critical to validate outpatient databases. Physician claims data capture both outpatient and inpatients visits; and are a valuable source for surveillance for epilepsy. However, physician claims data have not yet been validated for epilepsy coding in Canada. The aims of our study were to: (1) validate epilepsy coding in physician claims data; (2) develop validated coding algorithms for epilepsy using inpatient and physician claims data (capturing both inpatient and outpatient visits); and (3) assess whether adding an ER database to the inpatient and physician claims databases enhances the epilepsy case validity.

Section snippets

Patient population

We randomly selected 720/2049 visits (35% of all visits) from the fiscal year April 1, 2002–March 31, 2004 and 1533/3252 visits (47% of all visits) from the fiscal year April 1, 2005–March 31, 2007 from 13 neurologists’ practices in Calgary, Alberta, Canada for chart review as the “gold standard” for diagnosis. All adults ages 18 and older were eligible for inclusion.

Chart samples for validation

Two trained physicians with epilepsy management expertise reviewed each chart to determine if the patient had epilepsy,

Results

Of 2253 charts reviewed, 52% and 48% of charts were from epilepsy and neurology clinics respectively. Age groups were represented as follows: age 18–44 years 53.3%, age 45–65 years 35.3%, age 66–99 years 11.4%. Epilepsy cases represented 44% of charts reviewed, while 1% were associated with patients who had convulsions (but not epilepsy), and 55% were other diagnoses. The most common epilepsy diagnoses based on chart review (reviewer assigned code using ICD-9 or 10) were 345.4

Discussion

Utilizing chart data as the “gold standard” for diagnosis, this study assessed the accuracy and validity of ICD-9-CM and ICD-10 coding for epilepsy patients followed by neurologists. We found that a majority of epilepsy cases can be accurately identified in administrative data using the following case definition: 2 physician claims or 1 hospitalization in 2 years coded with the epilepsy ICD-9 code 345 or ICD-10 codes G40 or G41. This case definition balances the need for both sensitivity and

Acknowledgments

This study was funded by a grant from the Public Health Agency of Canada to N. Jette. N. Jette holds a salary award from Alberta Innovates Health Solutions (AI-HS) and a Canada Research Chair Tier 2 in Neurological Population Health and Health Services Research. H. Quan holds an AI-HS Population Health Investigator Award. S. Wiebe holds the Hopewell Professorship of Clinical Neurosciences Research at the University of Calgary.

References (17)

  • G. Chen et al.

    Validating ICD coding algorithms for diabetes mellitus from administrative data

    Diabetes Res. Clin. Pract.

    (2010)
  • J. Christensen et al.

    Validation of epilepsy diagnoses in the Danish National Hospital Register

    Epilepsy Res.

    (2007)
  • S. Asghari et al.

    Optimal strategy to identify incidence of diagnostic of diabetes using administrative data

    BMC Med. Res. Methodol.

    (2009)
  • Centers for Disease Control and Prevention

    Nonfatal traumatic brain injuries related to sports and recreation activities among persons aged ≤19 years – United States, 2001–2009

    MMWR Morb. Mortal. Wkly. Rep.

    (2011)
  • R.A. Deyo et al.

    Analysis of automated administrative and survey databases to study patterns and outcomes of care

    Spine

    (1994)
  • S.E. Drosler et al.

    International comparability of patient safety indicators in 15 OECD member countries: a methodological approach of adjustment by secondary diagnoses

    Health Serv. Res.

    (2012)
  • L. Forsgren et al.

    The epidemiology of epilepsy in Europe – a systematic review

    Eur. J. Neurol.

    (2005)
  • J.E. Hux et al.

    Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm

    Diabetes Care

    (2002)
There are more references available in the full text version of this article.

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