Background: Malignant mesothelioma (MM) is rare and fatal; survival in most cases is only about one year. Mortality rate is, therefore, a good proxy measure of incidence rate. However, the specific International Classification of Diseases (ICD) code for MM was not available until the Tenth Revision ICD (ICD-10). Little is known on which Ninth Revision ICD (ICD-9) codes were assigned for MM in the ICD-9 era.
Methods: We used a 1996 double-coded mortality file compiled by the National Center for Health Statistics to calculate the detection rate (DR) and confirmation rate (CR) of selected ICD-9 codes.
Results: Of 2386 decedents whose underlying cause of death was MM (ICD-10 code C45), the DR (deaths) of corresponding ICD-9 code was 57% (1365) for code 199 "malignant neoplasm without specification of site;" 19% (448) for code 162.9 "malignant neoplasm of trachea, bronchus, and lung, unspecified;" 13% (310) for code 163 "malignant neoplasm of pleura;" and 11% (271) for other codes. The CR (deaths) for the aforementioned three ICD-9 codes were 4.0% (1365/33,942), 0.3% (448/150,342), and 70.8% (310/438), respectively.
Conclusions: The three ICD-9 codes (199, 162.9, and 163) were the most commonly used codes for MM and composed nine-tenths of all MM deaths in the years before the ICD-10 was introduced. Using only ICD-9 code 163, the code most often used as the surrogate measure of MM in mortality studies in the ICD-9 era, capture may have been only 13% of all MM deaths in the US, and the estimated number of MM deaths missed in 1996 would be 2086.
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Aerosp Med Hum Perform
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Introduction: Insomnia and sleep apnea (SA) can have adverse effects on operating aircraft. This study examined trends in insomnia and SA incidence rates in U.S.
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UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX, 75390, USA.
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The James Buchanan Brady Urological Institute and Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Oncology, The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD. Electronic address:
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Int J Med Inform
January 2025
College of Science and Engineering, James Cook University, Townsville, 4811, QLD, Australia. Electronic address:
Background: Accurate classification of medical records is crucial for clinical documentation, particularly when using the 10th revision of the International Classification of Diseases (ICD-10) coding system. The use of machine learning algorithms and Systematized Nomenclature of Medicine (SNOMED) mapping has shown promise in performing these classifications. However, challenges remain, particularly in reducing false negatives, where certain diagnoses are not correctly identified by either approach.
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