Uptake of Biosimilar Insulin Aspart Following Implementation of a Regional Prescribing Policy Intervention; An Interrupted Time Series Analysis (dataset)

Hindley, Ben ORCID: https://orcid.org/0000-0003-2672-3074, Cope, Louise, McCabe, Phillipa and Da Costa, Ricardo (2026) Uptake of Biosimilar Insulin Aspart Following Implementation of a Regional Prescribing Policy Intervention; An Interrupted Time Series Analysis (dataset). [Data Collection]

Abstract

Background: Biosimilar insulin prescribing policy interventions offer the opportunity for healthcare systems to make substantial financial savings through both new initiations and active switching from reference products, without adverse effects on patient outcomes. In September 2024, a large integrated care board in northern England implemented a policy intervention to increase the prescribing of biosimilar insulin aspart.

Aims: The aim of this study was to quantitatively evaluate changes in the uptake of biosimilar insulin aspart following the prescribing policy intervention using an interrupted time series analysis and to estimate the associated financial savings.

Methods: Prescribing data were extracted from the Bennett Institute for Applied Data Science Open Prescribing Database and imported into Microsoft Excel. Pre-intervention and post-intervention trends in prescribing across nine sublocations within the integrated healthcare system were analysed by device type using segmented regression within a linear mixed-effects model. Serial autocorrelation was accounted for using a first-order autoregressive covariance structure. Financial savings associated with the increased biosimilar usage were then estimated by comparing observed prescribing with a modelled counterfactual scenario.

Results: The prescribing policy intervention was associated with a statistically significant increase in the uptake of biosimilar insulin aspart pre-filled pen devices, which reached a market share of 59% after 19 months. No statistically significant post-intervention effects were identified for cartridges or vials. The policy intervention was associated with estimated savings of approximately £350,000 over the study period.

Conclusions: Biosimilar insulin prescribing policy interventions can deliver significant changes in prescribing practice as well as meaningful financial savings, but their effectiveness may vary according to device type and factors such as local implementation.

Additional Information:	Depositing user's licence comment: Copyright 2026 Ben Hindley and co-authors. This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Users are free to share and adapt the material for any purpose, provided appropriate attribution is given to the original authors.
Creators:	Hindley, Ben ORCID: https://orcid.org/0000-0003-2672-3074, Cope, Louise, McCabe, Phillipa and Da Costa, Ricardo
Uncontrolled Keywords:	321403 Clinical pharmacy and pharmacy practice
DOI:	https://doi.org/10.24377/LJMU.d.00000292
Division:	Pharmacy & Biomolecular Sciences
Field of Research:	Biomedical and clinical sciences > Pharmacology and pharmaceutical sciences > Clinical pharmacy and pharmacy practice
Date Deposited:	05 Nov 2026 15:25
Last Modified:	05 Nov 2026 15:25
URI:	https://opendata.ljmu.ac.uk/id/eprint/292
Data collection method:	Prescribing data were extracted from the Bennett Institute for Applied Data Science Open Prescribing Database and imported into Microsoft Excel in September 2026 by the lead researcher. The relative market share expressed as a percentage, for the Reference product and biosimilar were calculated for every month between September 2023 and March 2026 inclusive. A segmented regression analysis using a linear mixed-effects model was performed. Given that longitudinal prescribing data are likely to exhibit temporal autocorrelation, a first-order autoregressive [AR(1)] covariance structure was specified a priori to account for correlation between adjacent observations within each sublocation. To maintain a unified framework, this approach was applied across all device types. To accommodate for possible non-linear trends, a quadratic post-intervention term was added to the model. The time at which prescribing uptake approached saturation was estimated from the fitted quadratic post-intervention function by calculating the post-intervention time point at which the first derivative of the predicted uptake trajectory equalled zero. Cost savings were estimated by comparing observed prescribing with a counterfactual scenario based on continuation of the pre-intervention prescribing trend.
Geographic coverage:	Cheshire and Merseyside
Resource language:	English
Metadata language:	English
Statement on legal, ethical and access issues:	This was a retrospective observational study using anonymous open access prescribing data for the NHS in England. Research Governance at Liverpool John Moores University confirmed that no ethical approval was required (260526LJMUREC618).
Collection period:	From To 1 August 2026 1 November 2026