Evidence map›Paper›PMID 38838327›Full record

ArticleJMIR public health and surveillance2024

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

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

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Lisa Em HopcroftNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7022-1322
Helen J CurtisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-3429-9576
Richard CrokerNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-8114-9186
Felix PretisDepartment of Economics, University of Victoria, Victoria, BC, Canada.ORCID 0000-0003-1435-9295
Peter InglesbyNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-7784-1719
David EvansNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-1100-079X
Sebastian BaconNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-6354-3454
Ben GoldacreNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-5127-4728
Alex J WalkerNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0003-4932-6135
Brian MacKennaNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-3786-9063

Funding

Wellcome Trust
6 · The paper itself

Abstract

backgroundWe 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.

objectiveWe sought to extend our previous work and develop a 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 analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures-total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate.

resultsWe identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) 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 (44%-95% absolute reduction). 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 a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time.

conclusionsBy applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation.

Indexed as

Analgesics, OpioidPractice Patterns, Physicians'Databases, FactualDrug PrescriptionsHumansRetrospective StudiesAnalgesics, Opioidanalysis tooldata-drivendata scienceelectronic health recordsgeneral practiceidentificationimplementationsimplementation scienceinterventionopioidopioid analgesicsprescribing dataprimary careproof of conceptunbiased

Identifiers

PMID38838327
PMCPMC11187509

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.