Defining diabetes status using medication groups in Medicare data: Trends in prescribing diabetes medications to patients without a diabetes diagnosis over time
Bibliographic record
Abstract
BACKGROUNDApproximately 37 million people in the United States had type 2 diabetes in 2019, 1 leading to poor health, high healthcare utilization, costs, morbidity and mortality.These numbers continue to rise, highlighting the need for population-level prevention and intervention.While cohort studies have been instrumental in estimating the prevalence and incidence of diabetes as well as trends in treatment and control over time, administrative claims data are increasingly used to examine these factors.2,3 One challenge in using administrative data is changes in practice over time, which can impact the sensitivity and specificity of claims-based definitions of health conditions.Multiple studies using Medicare data have reported on the incidence and prevalence of diabetes 2-5 as well as on trends in medication use 6 and glycaemic control.Typically, people are classified as having diabetes using a diabetes diagnosis code or-for studies which have access to prescription claims data-using a combination of International Classification of Disease (ICD) codes and medications.7,8 The advent of new frontline medications to treat diabetes, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodiumglucose cotransporter-2 (SGLT2) inhibitors, has resulted in improvements in other heath conditions, particularly obesity, kidney disease, and congestive heart failure, which has ushered in the prescription of diabetes-related medications for new US Food and Drug Administration-approved indications as well as off-label and/or nondiabetes-specific use.This is important to consider when using administrative data since definitions of diabetes in these data often use a combination of ICD codes and/or use of specific diabetes medications.Since the prescribing patterns of these diabetes medications have changed over time-not only among people with diabetes, but among people without diabetes 9,10 as well-how investigators define diabetes when using medication lists should be revisited, particularly when dealing with data from 2010 and beyond.However, to our knowledge, no study has examined the trends in use of diabetes-related drugs over time among individuals without diabetes diagnosis codes to
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".