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

Abstract

PurposeTo 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.MethodsUsing 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.ResultsThe 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%.ConclusionThrough 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.

To retrieve values of albuminuria to define the size of CKD patients eligible to SGLT‐2Is: An explorative analysis using a primary care database

“…In a recent work, 1 we developed and validated an algorithm to predict the value of HbA1c (>=7% vs. <7%) through a series of determinants, using an Italian primary care data source. Such an algorithm, whose accuracy was tested versus Multiple Imputation (MI) methodology, allowed us to create a cross-talk between clinical and administrative data source in Italy.…”