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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Jul 31, 2023
Date Accepted: Feb 12, 2024

The final, peer-reviewed published version of this preprint can be found here:

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study

Hopcroft LE, Curtis HJ, Croker R, Pretis F, Inglesby P, Evans D, Bacon S, Goldacre B, Walker AJ, MacKenna B

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study

JMIR Public Health Surveill 2024;10:e51323

DOI: 10.2196/51323

PMID: 38838327

PMCID: 11187509

Data-driven identification of potentially successful intervention implementations: a retrospective database study using five years of opioid prescribing data from over 7000 practices in England

  • Lisa EM Hopcroft; 
  • Helen J Curtis; 
  • Richard Croker; 
  • Felix Pretis; 
  • Peter Inglesby; 
  • David Evans; 
  • Sebastian Bacon; 
  • Ben Goldacre; 
  • Alex J Walker; 
  • Brian MacKenna

ABSTRACT

Background:

We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies and there is some evidence that progress is being made.

Objective:

We sought to extend our previous work and develop an unbiased, data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented.

Methods:

We analysed five years of prescribing data for three opioid prescribing measures: one capturing total opioid prescribing and two capturing regular prescribing of high dose opioids. Using a data-driven approach, we applied a modified version of our change detection Python library to identify changes in these measures over time, consistent with the successful implementation of an intervention. This analysis was carried out for general practices and CCGs, and organisations were ranked according to the change in prescribing rate.

Results:

We present data for the three CCGs and practices demonstrating the biggest reduction in opioid prescribing across the three opioid prescribing measures. We observed a 40% drop in the regular prescribing of high dose opioids (measured as a percentage of regular opioids) in the highest ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices. Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over two years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time.

Conclusions:

By applying one of our existing analysis tools to a national dataset, we were able to rank NHS organisations by reduction in opioid prescribing rates. Highly ranked organisations are candidates for further qualitative research into intervention design and implementation. Clinical Trial: NA


 Citation

Please cite as:

Hopcroft LE, Curtis HJ, Croker R, Pretis F, Inglesby P, Evans D, Bacon S, Goldacre B, Walker AJ, MacKenna B

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study

JMIR Public Health Surveill 2024;10:e51323

DOI: 10.2196/51323

PMID: 38838327

PMCID: 11187509

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