Evidence map›Paper›PMID 29732725›Full record

ArticleDiabetes, obesity & metabolism2018

Time trends and geographical variation in prescribing of drugs for diabetes in England from 1998 to 2017.

Helen J Curtis, John M Dennis, Beverley M Shields, Alex J Walker, Seb Bacon, Andrew T Hattersley, Angus G Jones, Ben Goldacre

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Article in Diabetes, obesity & metabolism, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  17. Knowledge gaps in diabetes research: an evidence mapping of the literature.Journal of diabetes and metabolic disorders · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Helen J CurtisEvidence-Based Medicine DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-3429-9576
John M DennisHealth Statistics Group, Institute of Health Research, University of Exeter Medical School, Exeter, UK.
Beverley M ShieldsRoyal Devon and Exeter Hospital, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
Alex J WalkerEvidence-Based Medicine DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0003-4932-6135
Seb BaconEvidence-Based Medicine DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Andrew T HattersleyRoyal Devon and Exeter Hospital, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
Angus G JonesRoyal Devon and Exeter Hospital, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.ORCID http://orcid.org/0000-0002-0883-7599
Ben GoldacreEvidence-Based Medicine DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.ORCID http://orcid.org/0000-0002-5127-4728

Funding

Medical Research Council MR/K005707/1Medical Research Council MR/N00633XMedical Research Council MR/N00633X/1Wellcome Trust
6 · The paper itself

Abstract

aimsTo measure the variation in prescribing of second-line non-insulin diabetes drugs. MATERIALS AND

methodsWe evaluated time trends for the period 1998 to 2016, using England's publicly available prescribing datasets, and stratified these by the order in which they were prescribed to patients using the Clinical Practice Research Datalink. We calculated the proportion of each class of diabetes drug as a percentage of the total per year. We evaluated geographical variation in prescribing using general practice-level data for the latest 12 months (to August 2017), with aggregation to Clinical Commissioning Groups. We calculated percentiles and ranges, and plotted maps.

resultsPrescribing of therapy after metformin is changing rapidly. Dipeptidyl peptidase-4 (DPP-4) inhibitor use has increased markedly, with DPP-4 inhibitors now the most common second-line drug (43% prescriptions in 2016). The use of sodium-glucose co-transporter-2 (SGLT-2) inhibitors also increased rapidly (14% new second-line, 27% new third-line prescriptions in 2016). There was wide geographical variation in choice of therapies and average spend per patient. In contrast, metformin was consistently used as a first-line treatment in accordance with guidelines.

conclusionsIn England there is extensive geographical variation in the prescribing of diabetes drugs after metformin, and increasing use of higher-cost DPP-4 inhibitors and SGLT-2 inhibitors compared with low-cost sulphonylureas. Our findings strongly support the case for comparative effectiveness trials of current diabetes drugs.

Indexed as

Databases, FactualDiabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsEnglandGeography, MedicalHumansHypoglycemic AgentsMetforminSodium-Glucose Transporter 2 InhibitorsSulfonylurea CompoundsTimeDipeptidyl-Peptidase IV InhibitorsHypoglycemic AgentsMetforminSodium-Glucose Transporter 2 InhibitorsSulfonylurea Compoundsantidiabetic drugcost-effectivenessglycaemic controlprimary caretype 2 diabetes

Identifiers

PMID29732725
PMCPMC6099452

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