A methodology to assess the population size and estimate the needed resources for new licensed medications by combining clinical and administrative databases: The example of glycated haemoglobin in type 2 diabetes
Bibliographic record
Abstract
PURPOSE: To develop and validate a model to estimate glycated haemoglobin (HbA1c) values in patients with type 2 diabetes mellitus (T2DM) using a clinical data source, with the aim to apply this equation to administrative databases. METHODS: Using a primary care and administrative Italian databases, namely the Health Search database (HSD) and the ReS (Ricerca e Salute) database, we selected all patients aged 18 years or older on 31 December 2018 being diagnosed with T2DM and without prior prescription of sodium-glucose cotransporter-2 (SGLT-2) inhibitors. We included patients prescribed with and adherent to metformin. HSD was used to develop and test (using 2019 data as well) the algorithm imputing HbA1c values ≥7% according to a series of covariates. The algorithm was gathered by combining beta-coefficients being estimated by logistic regression models using complete case (excluding missing values) and imputed (after multiple imputation) dataset. The final algorithm was applied to ReS database using the same covariates. RESULTS: The tested algorithms were able to explain 17%-18% variation in assessing HbA1c values. Good discrimination (70%) and calibration were obtained as well. The best algorithm (three) cut-offs, namely those providing correct classifications ranging 66%-70% was therefore calculated and applied to ReS database. By doing so, from 52 999 (27.9, 95% CI: 27.7%-28.1%) to 74 250 (40.1%, 95% CI: 38.9%-39.3%) patients were estimated with HbA1c ≥7%. CONCLUSION: Through this methodology, healthcare authorities should be able to quantify the population eligible to a new licensed medication, such as SGLT-2 inhibitors, and to simulate scenarios to assess reimbursement criteria according to precise estimates.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".