Validating ICD coding algorithms for diabetes mellitus from administrative data

Chen, Guanmin; Khan, Nadia; Walker, Robin; Quan, Hude

doi:10.1016/j.diabres.2010.03.007

Cited by 138 publications

(123 citation statements)

References 13 publications

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“…25 Cunningham and colleagues 26 evaluated the quality of physician visit data (including inpatients and outpatient visits) in Alberta and concluded that physicians submitted a variety of ICD codes for claims and that the face validity of diagnosis coded in physician claims is substantially high. In keeping with our findings, 2 Canadian studies (Quan and colleagues 11 and Chen and colleagues 5 ) analyzed inpatient and outpatient administrative data validity in defining hypertension and diabetes and did not find significant changes between 2001 and 2014. However, Januel and colleagues 9 reported that inpatient data validity improved after ICD-10 implementation in Switzerland.…”

Section: Discussionsupporting

confidence: 66%

“…The published studies examined a relatively short period, usually conducting only 2 or 3 period comparisons over a maximum of 5 years. [4][5][6][7][8][9][10][11][12][13] To fill this gap, we conducted a study to examine the validity of administrative codes from the Discharge Abstract Database for hypertension and diabetes between 2002 and 2013. We used a prospective clinical registry, the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database, as the reference standard.…”

mentioning

confidence: 99%

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Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

et al. 2016

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Section: Discussionsupporting

confidence: 66%

mentioning

confidence: 99%

Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

et al. 2016

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“…Although individuals responsible for ICD coding in the DAD receive standardized training in Canada (33), there may still be coding variations across provinces that could limit the generalizability of these findings. Previous studies have compared the validity of case identification using administrative data against chart review in the areas of hypertension and diabetes mellitus, and found that the sensitivity results (diabetes mellitus range 90.2-92.1%, hypertension range 74.0-76.0%) are not significantly different between 2 provinces (Alberta and British Columbia) (21,24).…”

Section: Discussionmentioning

confidence: 99%

“…The sensitivity and specificity of OA case definition are not high; however, these are the standard algorithms accepted for administrative database OA research. Further, previous research in other chronic disease areas have determined the case definition with the highest validity to be 2 physician claims within 2 years, or 1 hospitalization, with a minimum of 3 or more years of data (21)(22)(23)(24). Our estimates include only individuals who have accessed health care services paid for by the provincial health care insurance plan.…”

Section: Discussionmentioning

confidence: 99%

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Estimating the Burden of Osteoarthritis to Plan for the Future

Marshall

Vanderby

Barnabé

et al. 2015

Arthritis Care & Research

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Objective. With aging and obesity trends, the incidence and prevalence of osteoarthritis (OA) is expected to rise in Canada, increasing the demand for health resources. Resource planning to meet this increasing need requires estimates of the anticipated number of OA patients. Using administrative data from Alberta, we estimated OA incidence and prevalence rates and examined their sensitivity to alternative case definitions. Methods. We identified cases in a linked data set spanning 1993 to 2010 (population registry, Discharge Abstract Database, physician claims, Ambulatory Care Classification System, and prescription drug data) using diagnostic codes and drug identification numbers. In the base case, incident cases were captured for patients with an OA diagnostic code for at least 2 physician visits within 2 years or any hospital admission. Seven alternative case definitions were applied and compared. Results. Age-and sex-standardized incidence and prevalence rates were estimated to be 8.6 and 80.3 cases per 1,000 population, respectively, in the base case. Physician claims data alone captured 88% of OA cases. Prevalence rate estimates required 15 years of longitudinal data to plateau. Compared to the base case, estimates are sensitive to alternative case definitions. Conclusion. Administrative databases are a key source for estimating the burden and epidemiologic trends of chronic diseases such as OA in Canada. Despite their limitations, these data provide valuable information for estimating disease burden and planning health services. Estimates of OA are mostly defined through physician claims data and require a long period of longitudinal data.

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Predicting In-Hospital Mortality in Patients Undergoing Complex Gastrointestinal Surgery

Grendar

Shaheen²,

Myers³

et al. 2012

Arch Surg

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Patients: Adult patients (Ն18 years) hospitalized with a primary or secondary procedure of gastric, hepatic, or pancreatic resection between 2002 and 2007. Main Outcome Measures: Predicting in-hospital mortality using the 4 comorbidity algorithms. Logistic regression analyses were used and C statistics were calculated to assess the performance of the indexes. Risk adjustment methods were then compared. Results: In our study, we identified 46 395 gastric resections, 18 234 hepatic resections, and 15 443 pancreatic re-sections. Predicted in-hospital mortality rates according to the adjustment methods agreed for 43.8% to 74.6% of patients. In all types of resections, the APR-DRGs and Disease Staging algorithms predicted in-hospital mortality better than the Charlson/Deyo and Elixhauser indexes (PϽ.001). Compared with the Charlson/Deyo algorithm, the Elixhauser index was of higher accuracy in gastric resections (0.847 vs 0.792), hepatic resections (0.810 vs 0.757), and pancreatic resections (0.811 vs 0.741) (PϽ.001 for all comparisons). Higher accuracy of the Elixhauser algorithm compared with the Charlson/Deyo algorithm was not affected by diagnosis rank, multiple surgeries, or exclusion of transplant patients. Conclusions: Different comorbidity algorithms were validated in the surgical setting. The Disease Staging and APR-DRGs algorithms were highly accurate. For commonly used algorithms such as Charlson/Deyo and Elixhauser, the latter showed higher accuracy.

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Validating ICD coding algorithms for diabetes mellitus from administrative data

Cited by 138 publications

References 13 publications

Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

Validity of Canadian discharge abstract data for hypertension and diabetes from 2002 to 2013

Estimating the Burden of Osteoarthritis to Plan for the Future

Predicting In-Hospital Mortality in Patients Undergoing Complex Gastrointestinal Surgery

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