Algorithms to identify COPD in health systems with and without access to ICD coding: a systematic review

Abstract

BackgroundChronic obstructive pulmonary disease (COPD) causes significant morbidity and mortality worldwide. Estimation of incidence, prevalence and disease burden through routine insurance data is challenging because of under-diagnosis and under-treatment, particularly for early stage disease in health care systems where outpatient International Classification of Diseases (ICD) diagnoses are not collected. This poses the question of which criteria are commonly applied to identify COPD patients in claims datasets in the absence of ICD diagnoses, and which information can be used as a substitute. The aim of this systematic review is to summarize previously reported methodological approaches for the identification of COPD patients through routine data and to compile potential criteria for the identification of COPD patients if ICD codes are not available.MethodsA systematic literature review was performed in Medline via PubMed and Google Scholar from January 2000 through October 2018, followed by a manual review of the included studies by at least two independent raters. Study characteristics and all identifying criteria used in the studies were systematically extracted from the publications, categorized, and compiled in evidence tables.ResultsIn total, the systematic search yielded 151 publications. After title and abstract screening, 38 publications were included into the systematic assessment. In these studies, the most frequently used (22/38) criteria set to identify COPD patients included ICD codes, hospitalization, and ambulatory visits. Only four out of 38 studies used methods other than ICD coding. In a significant proportion of studies, the age range of the target population (33/38) and hospitalization (30/38) were provided. Ambulatory data were included in 24, physician claims in 22, and pharmaceutical data in 18 studies. Only five studies used spirometry, two used surgery and one used oxygen therapy.ConclusionsA variety of different criteria is used for the identification of COPD from routine data. The most promising criteria set in data environments where ambulatory diagnosis codes are lacking is the consideration of additional illness-related information with special attention to pharmacotherapy data. Further health services research should focus on the application of more systematic internal and/or external validation approaches.

Comparing health insurance data and health interview survey data for ascertaining chronic disease prevalence in Belgium

“…Another limitation is the lack of additional information such as ICD-10 codes or other clinical diagnostic codes in the case ascertainment from administrative data source. Indeed, validation studies often include information from various sources in the algorithms: health surveys, ICD-10 codes, ATC codes, other clinical diagnostic codes, etc., and this provides much better measures of agreement [ 2 , 3 , 7 , 10 ]. Finally, the BHIS data was used as the gold standard in this study because next to administrative data, it is the only source for obtaining population-based chronic disease prevalence estimates in Belgium.…”

“…To estimate these indicators, a diabetes case definition algorithm based on antidiabetic drug consumption was applied [ 4 ]. Drug use data, especially prescription drugs, have also been frequently used to estimate CDs prevalence [ 5 , 7 , 13 ].…”

Comparing health insurance data and health interview survey data for ascertaining chronic disease prevalence in Belgium

“…Another limitation is the lack of additional information such as ICD-10 codes or other clinical diagnostic codes in the case ascertainment from administrative data source. Indeed, validation studies often include information from various sources in the algorithms: health surveys, ICD-10 codes, ATC codes, other clinical diagnostic codes, etc., and this provides much better measures of agreement [2,3,7,10]. Finally, the BHIS data was used as the gold standard in this study because next to administrative data, it is the only source for obtaining population-based chronic disease prevalence estimates in Belgium.…”

“…Prevalence of CDs is often estimated using population health surveys, disease registers, hospitalization or outpatient records [3][4][5][6][7][8]. Besides these traditional methods, health administrative databases have been used as an alternative, e cient source of data for CDs surveillance [4,5,9,10].…”

“…To estimate these indicators, a diabetes case de nition algorithm based on antidiabetic drug consumption was applied [4]. Drug use data, especially prescription drugs, have also been frequently used to estimate CDs prevalence [5,7,13].…”