ABSTRACT

Introduction: Health care utilisation (‘claims’) databases contain information about millions of patients and are an important source of information for a variety of study types. However, they typically do not contain information about disease severity. The goal of the present study was to develop a health care claims index for rheumatoid arthritis (RA) severity using a previously developed medical records-based index for RA severity (RA medical records-based index of severity [RARBIS]).

Methods: The study population consisted of 120 patients from the Veteran’s Administration (VA) Health System. We previously demonstrated the construct validity of the RARBIS and established its convergent validity with the Disease Activity Score (DAS28). Potential claims-based indicators were entered into a linear regression model as independent variables and the RARBIS as the dependent variable. The claims-based index for RA severity (CIRAS) was created using the coefficients from models with the highest coefficient of determination (R2) values selected by automated modelling procedures. To compare our claims-based index with our medical records-based index, we examined the correlation between the CIRAS and the RARBIS using Spearman non-parametric tests.

Results: The forward selection models yielded the highest model R2 for both the RARBIS with medications (R2 = 0.31) and the RARBIS without medications (R2 = 0.26). Components of the CIRAS included tests for inflammatory markers, number of chemistry panels and platelet counts ordered, rheumatoid factor, the number of rehabilitation and rheumatology visits, and Felty’s syndrome diagnosis. The CIRAS demonstrated moderate correlations with the RARBIS with medication and the RARBIS without medication sub-scales.

Conclusion: We developed the CIRAS that showed moderate correlations with a previously validated records-based index of severity. The CIRAS may serve as a potentially important tool in adjusting for RA severity in pharmacoepidemiology studies of RA treatment and complications using health care utilisation data.

ABSTRACT

Automated databases are increasingly used in pharmacoepidemiologic studies. These databases include records of prescribed medications and encounters with medical care providers from which one can construct very detailed surrogate measures for both drug exposure and covariates that are potential confounders. Often it is possible to track day-by-day changes in these variables. However, while this information is often critical for study success, its volume can pose challenges for statistical analysis. One common approach is the use of propensity scores. An alternative approach is to construct a disease risk score. This is analogous to the propensity score in that it calculates a summary measure from the covariates. However, the disease risk score estimates the probability or rate of disease occurrence conditional on being unexposed. The association between exposure and disease is then estimated adjusting for the disease risk score in place of the individual covariates. This review describes the use of disease risk scores in pharmacoepidemiologic studies, and includes a brief discussion of their history, a more detailed description of their construction and use, a summary of simulation studies comparing their performance vis-á-vis traditional models, a comparison of their utility with that of propensity scores, and some further topics for future research.

ABSTRACT

Objective: Use of physician service claims and other administrative data is increasingly being advocated for chronic disease surveillance. However, such data may be vulnerable to reimbursement policy changes. We sought to determine how non-fee-for-service (non-FFS) primary care affects the detection of diabetes using physician claims data.

Methods: Ontarians over age 66 with diabetes and receiving care in a non-FFS setting were identified using prescription claims for glucose-lowering drugs written by non-FFS physicians. We compared the date of incident treatment in this cohort with the diagnosis date in the Ontario Diabetes Database, a validated administrative data algorithm to detect persons with diabetes. We assessed the rate of detection and, among detected cases, whether detection was late (more than 6 months after the index prescription). Survival methods were used to assess detection over time.

Results: Only 49.7% of prescription-defined diabetes cases were detected within six months of the index prescription; 23.7% remained undetected after up to nine years of follow-up. Detected individuals had higher rates of hospitalization for vascular complications than missed cases (15.1% vs 4.8%, p