COVID-19 impact on Sodium Valproate Prescribing
Research type
Research Study
Full title
Investigation into the impact of the COVID-19 Pandemic on prescribing patterns of Sodium Valproate
IRAS ID
343663
Contact name
Phillip Walmsley
Contact email
Sponsor organisation
Department for Health and Social Care
Duration of Study in the UK
0 years, 4 months, 0 days
Research summary
During the COVID-19 pandemic, access to medical care was profoundly altered, with in-person appointments curtailed due to social-distancing and lockdown measures, while remote appointments were not immediately available. It is reasonable to assume that this impacted the prescription of medications of all kinds, including those whose usage must be carefully monitored.
Sodium Valproate (SV) is a prescription medication used to treat neurological conditions including epilepsy, bipolar disorder, and migraine headaches. SV can cause severe birth defects if taken during pregnancy, and as such Its prescription is not recommended as a first intervention. Patients prescribed SV that could become pregnant are put on Prevent (a pregnancy prevention programme) requiring an annual assessment from a doctor or nurse.
As a prescription medication that must be monitored carefully, there is a significant benefit to understanding how its prescription was impacted by the COVID-19 pandemic, both in the general population of those prescribed it, and in particular to those patients who could become pregnant.
We will investigate possible changes in the prescription of Sodium Valproate (SV) over the COVID pandemic period, including investigating the prescription of SV generally, and with a focus on people who can become pregnant (most likely to be defined as women within a range of ages). The investigation will use the historic prescription rates for SV to act as a comparator for the COVID pandemic period.
This study will make use of existing primary care data that is securely maintained by OpenSAFELY. The OpenSAFELY platform ensures privacy and transparency through pseudonymization, which removes explicit identifiers from patient records, and a secure data environment.
Researchers for this project will work with pseudonymized patient data, using OpenSAFELY’s standardized tools to convert raw data into research-ready datasets. Code development occurs against simulated, randomly generated "dummy data" to minimize interaction with disclosive patient records. Code is then tested automatically, packaged securely, and executed against real patient data by OpenSAFELY. This process occurs without ever exposing researchers to the patient level data as the only outputs delivered back to researchers are aggregated data with no patient identifiers. This method ensures safety of secure patient information.REC name
East Midlands - Leicester South Research Ethics Committee
REC reference
24/EM/0136
Date of REC Opinion
1 Jul 2024
REC opinion
Further Information Favourable Opinion