Mapping Three Versions of the International Classification of Diseases to Categories of Chronic Conditions

Hamad, Amani F.; Vasylkiv, Viktoriya; Yan, Lin; Sanusi, Ridwan A.; Ayilara, Olawale; Delaney, Joseph A.; Wall‐Wieler, Elizabeth; Jozani, Mohammad Jafari; Hu, Pingzhao; Banerji, Shantanu; Lix, Lisa M.

doi:10.23889/ijpds.v6i1.1406

Cited by 6 publications

(14 citation statements)

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“…We used control charts to monitor the estimated regression model parameters for 16 chronic health conditions during a 45-year period (1974–2018) when three different ICD versions were used to record diagnoses in administrative health data. We focused on the effect of changes in the ICD version on the prevalence of chronic health conditions, which can result in real changes in the usage, meaning, and interpretation of diagnosis codes [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…Changes in ICD versions captured in administrative health data require crosswalks of the diagnoses if data sources are to be integrated. Crosswalks between three ICD versions (ICDA-8, ICD-9-CM, and ICD-10-CA) for multiple chronic health conditions were recently developed [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the selected conditions provide a good representation of the variety of health conditions captured by the ICD system. The chronic health conditions were selected from multiple chronic health conditions derived using Clinical Classification Software (CCS) [ 7 ]. The CCS, developed by the Agency for Healthcare Research and Quality in the US, has been adopted in many studies to provide clinically meaningful and interpretable statistics such as disease prevalence, frequencies, and medical expenditures [ 30 , 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…The CCS, developed by the Agency for Healthcare Research and Quality in the US, has been adopted in many studies to provide clinically meaningful and interpretable statistics such as disease prevalence, frequencies, and medical expenditures [ 30 , 31 ]. Crosswalks of diagnoses codes to CCS categories are provided by Hamad et al [ 7 ]. The selected chronic health conditions include mood and anxiety disorders, menstrual disorders, hypertension, osteoarthritis, anemia, diabetes, asthma, acute myocardial infarction, heart valve disorders, acute cerebrovascular disease, cataracts, breast cancer, colon cancer, lung and respiratory cancers, prostate cancer, and skin cancer.…”

Section: Methodsmentioning

confidence: 99%

“…Each ICD version has a greater number of codes, resulting in new diseases being added, and other diseases being recategorized, removed, or combined [ 1 ]. Though the increasing level of detail with each update of the ICD system is essential for diagnostic and administrative purposes, codes in one ICD version may not map (i.e., correspond) exactly to codes in another ICD version [ 7 ]. This introduces challenges in using ICD codes to track trends in disease prevalence.…”

Section: Introductionmentioning

confidence: 99%

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Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

et al. 2022

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Background Diagnosis codes in administrative health data are routinely used to monitor trends in disease prevalence and incidence. The International Classification of Diseases (ICD), which is used to record these diagnoses, have been updated multiple times to reflect advances in health and medical research. Our objective was to examine the impact of transitions between ICD versions on the prevalence of chronic health conditions estimated from administrative health data. Methods Study data (i.e., physician billing claims, hospital records) were from the province of Manitoba, Canada, which has a universal healthcare system. ICDA-8 (with adaptations), ICD-9-CM (clinical modification), and ICD-10-CA (Canadian adaptation; hospital records only) codes are captured in the data. Annual study cohorts included all individuals 18 + years of age for 45 years from 1974 to 2018. Negative binomial regression was used to estimate annual age- and sex-adjusted prevalence and model parameters (i.e., slopes and intercepts) for 16 chronic health conditions. Statistical control charts were used to assess the impact of changes in ICD version on model parameter estimates. Hotelling’s T2 statistic was used to combine the parameter estimates and provide an out-of-control signal when its value was above a pre-specified control limit. Results The annual cohort sizes ranged from 360,341 to 824,816. Hypertension and skin cancer were among the most and least diagnosed health conditions, respectively; their prevalence per 1,000 population increased from 40.5 to 223.6 and from 0.3 to 2.1, respectively, within the study period. The average annual rate of change in prevalence ranged from -1.6% (95% confidence interval [CI]: -1.8, -1.4) for acute myocardial infarction to 14.6% (95% CI: 13.9, 15.2) for hypertension. The control chart indicated out-of-control observations when transitioning from ICDA-8 to ICD-9-CM for 75% of the investigated chronic health conditions but no out-of-control observations when transitioning from ICD-9-CM to ICD-10-CA. Conclusions The prevalence of most of the investigated chronic health conditions changed significantly in the transition from ICDA-8 to ICD-9-CM. These results point to the importance of considering changes in ICD coding as a factor that may influence the interpretation of trend estimates for chronic health conditions derived from administrative health data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

et al. 2022

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A unidirectional mapping of ICD-8 to ICD-10 codes, for harmonized longitudinal analysis of diseases

et al. 2023

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Periodic revisions of the international classification of diseases (ICD) ensure that the classification reflects new practices and knowledge; however, this complicates retrospective research as diagnoses are coded in different versions. For longitudinal disease trajectory studies, a crosswalk is an essential tool and a comprehensive mapping between ICD-8 and ICD-10 has until now been lacking. In this study, we map all ICD-8 morbidity codes to ICD-10 in the expanded Danish ICD version. We mapped ICD-8 codes to ICD-10, using a many-to-one system inspired by general equivalence mappings such that each ICD-8 code maps to a single ICD-10 code. Each ICD-8 code was manually and unidirectionally mapped to a single ICD-10 code based on medical setting and context. Each match was assigned a score (1 of 4 levels) reflecting the quality of the match and, if applicable, a “flag” signalling choices made in the mapping. We provide the first complete mapping of the 8596 ICD-8 morbidity codes to ICD-10 codes. All Danish ICD-8 codes representing diseases were mapped and 5106 (59.4%) achieved the highest consistency score. Only 334 (3.9%) of the ICD-8 codes received the lowest mapping consistency score. The mapping provides a scaffold for translation of ICD-8 to ICD-10, which enable longitudinal disease studies back to and 1969 in Denmark and to 1965 internationally with further adaption.

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Developing a prediction model of children asthma risk using population‐based family history health records

Hamad,

Yan,

Jafari Jozani

et al. 2023

Pediatric Allergy Immunology

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BackgroundIdentifying children at high risk of developing asthma can facilitate prevention and early management strategies. We developed a prediction model of children's asthma risk using objectively collected population‐based children and parental histories of comorbidities.MethodsWe conducted a retrospective population‐based cohort study using administrative data from Manitoba, Canada, and included children born from 1974 to 2000 with linkages to ≥1 parent. We identified asthma and prior comorbid condition diagnoses from hospital and outpatient records. We used two machine‐learning models: least absolute shrinkage and selection operator (LASSO) logistic regression (LR) and random forest (RF) to identify important predictors. The predictors in the base model included children's demographics, allergic conditions, respiratory infections, and parental asthma. Subsequent models included additional multiple comorbidities for children and parents.ResultsThe cohort included 195,666 children: 51.3% were males and 17.7% had asthma diagnosis. The base LR model achieved a low predictive performance with sensitivity of 0.47, 95% confidence interval (0.45–0.48), and specificity of 0.67 (0.66–0.67) using a predicted probability threshold of 0.20. Sensitivity significantly improved when children's comorbidities were included using LASSO LR: 0.71 (0.69–0.72). Predictive performance further improved by including parental comorbidities (sensitivity = 0.72 [0.70–0.73], specificity = 0.69 [0.69–0.70]). We observed similar results for the RF models. Children's menstrual disorders and mood and anxiety disorders, parental lipid metabolism disorders and asthma were among the most important variables that predicted asthma risk.ConclusionIncluding children and parental comorbidities to children's asthma prediction models improves their accuracy.

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Mapping Three Versions of the International Classification of Diseases to Categories of Chronic Conditions

Cited by 6 publications

References 36 publications

Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

Transitions between versions of the International Classification of Diseases and chronic disease prevalence estimates from administrative health data: a population-based study

A unidirectional mapping of ICD-8 to ICD-10 codes, for harmonized longitudinal analysis of diseases

Developing a prediction model of children asthma risk using population‐based family history health records

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