Evidence map›Paper›PMID 35575214›Full record

ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2022

Mapping the risk of infections in patients with multiple sclerosis: A multi-database study in the United Kingdom Clinical Practice Research Datalink GOLD and Aurum.

Melissa Wy Leung, Marloes T Bazelier, Patrick C Souverein, Bernard Mj Uitdehaag, Olaf H Klungel, Hubert Gm Leufkens, Romin Pajouheshnia

Open access · hybridAbstract read
In one paragraph

Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.9field-weighted citation impact, top 28% of its field
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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

7 authors at 2 institutions in 1 country.

Melissa Wy LeungDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.ORCID 0000-0002-4710-5841
Marloes T BazelierDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Patrick C SouvereinDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Bernard Mj UitdehaagDepartment of Neurology, Amsterdam Neuroscience, VUmc MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Olaf H KlungelDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Hubert Gm LeufkensDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Romin PajouheshniaDivision of Pharmacoepidemiology & Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences (UIPS), Department of Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands.
Utrecht University · NLAmsterdam Neuroscience · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPeople with multiple sclerosis (pwMS) have an increased risk of infections; risk factors include underlying disease, physical impairment and use of some disease-modifying treatments.

objectiveTo quantify changes in population-level infection rates among pwMS and compare these to the general population and people with rheumatoid arthritis (pwRA), and identify patient characteristics predictive of infections after MS diagnosis.

methodsWe conducted a multi-database study using data on 23,226 people with MS diagnosis from the UK Clinical Practice Research Datalink Aurum and GOLD (January 2000-December 2020). PwMS were matched to MS-free controls and pwRA. We calculated infection rates, and estimated incidence rate ratios (IRR) and 95% confidence intervals (CI) of predictors for infections ⩽ 5 years after MS diagnosis using Poisson regression.

resultsAmong pwMS, overall infection rates remained stable - 1.51-fold (1.49-1.52) that in MS-free controls and 0.87-fold (0.86-0.88) that in pwRA - although urinary tract infection rate per 1000 person-years increased from 98.7 (96.1-101) (2000-2010) to 136 (134-138) (2011-2020). Recent infection before MS diagnosis was most predictive of infections (1 infection: IRR 1.92 (1.86-1.97); ⩾2 infections: IRR 3.00 (2.89-3.10)).

conclusionThe population-level elevated risk of infection among pwMS has remained stable despite the introduction of disease-modifying treatments.

Indexed as

Multiple SclerosisDatabases, FactualHumansIncidenceRisk FactorsUnited Kingdommultiple outcomes multi-database studyMultiple sclerosis infectionsUnited Kingdom Clinical Practice Research Datalink GOLD and Aurumurinary tract infection

Identifiers

PMID35575214
PMCPMC9442628
OpenAlexW4280505004

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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 OpenQuestion graph.