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

Abstract

AbstractBackgroundWe 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. 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.MethodsWe 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.ResultsWe 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.ConclusionsBy 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.Contributions to the literatureDemonstrating that a data-driven approach can identify and quantify changes in important clinical measures in publicly available NHS dataIdentifying changes in this way allows the unbiased identification of candidates for further qualitative research into intervention design and implementationLarge reductions observed at the CCG level (which are more robust to local circumstances) demonstrate that it is possible to reduce opioid prescribing and that continued and wider success in reducing opioid prescribing is dependent, at least in part, to closing an implementation gap