Tag: CANRADnetwork

Estimating the Burden of Osteoarthritis to Plan for the Future

Abstract

Background: With aging and obesity trends, the incidence and prevalence of osteoarthritis (OA) are 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 dataset 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 two physician visits within two years or any hospital admission. Seven alternative case definitions were applied and compared.

Results: Age-sex standardized incidence and prevalence rates were estimated to be 8.6 and 80.3 cases/1000 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 base case, estimates are sensitive to alternative case definitions.

Conclusion: Administrative databases are a key source for estimating the burden and epidemiological 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.

CodingObesity MartinBMC

Abstract

Background: Obesity is a pervasive problem and a popular subject of academic assessment. The ability to take advantage of existing data, such as administrative databases, to study obesity is appealing. The objective of our study was to assess the validity of obesity coding in an administrative database and compare the association between obesity and outcomes in an administrative database versus registry.

Methods: This study was conducted using a coronary catheterization registry and an administrative database (Discharge Abstract Database (DAD)). A Body Mass Index (BMI) ≥30 kg/m2 within the registry defined obesity. In the DAD obesity was defined by diagnosis codes E65 –E68 (ICD-10). The sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) of an obesity diagnosis in the DAD was determined using obesity diagnosis in the registry as the referent. The association between obesity and outcomes was assessed.

Results: The study population of 17380 subjects was largely male (68.8%) with a mean BMI of 27.0kg/m2. Obesity prevalence was lower in the DAD than registry (2.4% vs. 20.3%). A diagnosis of obesity in the DAD had a sensitivity 7.75%, specificity 98.98%, NPV 80.84% and PPV 65.94%. Obesity was associated with decreased risk of death or re-hospitalization, though non-significantly within the DAD. Obesity was significantly associated with an increased risk of cardiac procedure in both databases.

Conclusions: Overall, obesity was poorly coded in the DAD. However, when coded, it was coded accurately. Administrative databases are not an optimal datasource for obesity prevalence and incidence surveillance but could be used to define obese cohorts for follow-up.

Consensus statements for best practices – CANRAD Network Poster

Consensus statements for best practices when using administrative data for rheumatic disease research and surveillance.

CANRAD Network Authors: D. Lacaille, L. Lix, C. Bombardier, S. Bernatsky, J. Askling, A. Aviña-Zubieta, E. Badley , C. Barber, C. Barnabe, S. Benseler, L. Bergeron, L. Bessette, B. Bobechko, C. Cooke, J. Curtis, W. Dixon, C. Duffy, S. Edworthy, B. Elias, D. Feldman , P. Fortin, J. Hanly, G. Hawker, J. Henderson, M. Hudson, S. Jean, J. Kopec, B. Kuriya, J. Labreque, A. Leeong, D. Levy, S. Lim C. Mackay, P. McCrea, P. Nestman, K. Oen, M. Patterson, C. Peschken, D. Power, E. Rahme, A. Rosenberg, L. Rochette, N. Shiff , E. Silverman, C. Sirois, M. Smith, D. Solomon, G. Soon, E. Stringer, S. Suissa, L. Svenson, L. Tucker, E. Vinet, J. Widdifield.