Estimating the completeness of physician billing claims for diabetes case ascertainment using population-based prescription drug data
Bibliographic record
Abstract
INTRODUCTION: Changes in physician reimbursement policies may hinder the collection of billing claims in administrative data; this can result in biased estimates of disease prevalence and incidence. However, the magnitude of data loss is largely unknown. The purpose of this study was to estimate completeness of capture of disease cases for Manitoba physicians paid by fee-for-service (FFS) and non-fee-for-service (NFFS) methods. METHODS: Manitoba's administrative data were used to identify a cohort (≥ 20 years) with a new diabetes medication between 1 April, 2007, and 31 March, 2009. Cohort members were classified by payment method of the prescribing physician (i.e. FFS vs. NFFS). The cohort was then classified as missing or not missing a diabetes diagnosis using physician claims and hospital records. Then, χ2 statistics were used to test for differences in the characteristics of the two groups. RESULTS: The cohort consisted of 12 394 individuals; 86.4% had a prescription for a diabetes medication from an FFS physician. A total of 1172 physicians (81.8% FFS) prescribed these medications for the cohort. Cohort members with a prescription from an FFS physician were older and more likely to reside in the urban Winnipeg health region than those with a prescription from a NFFS physician. A greater percentage of NFFS physicians' cases were missing a diabetes diagnosis (18.7%vs. 14.9% for FFS physicians). CONCLUSION: The results suggest minimal loss of physician claims associated with remuneration policies in Manitoba. This method of assessing data completeness could be applied to other chronic diseases and jurisdictions to estimate completeness.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".