Evidence map›Paper›PMID 35613765›Full record

ArticleBMJ open2022

Socioeconomic and health factors related to polypharmacy and medication management: analysis of a Household Health Survey in North West Coast England.

Jennifer Downing, Rebecca Taylor, Rachael Mountain, Ben Barr, Konstantinos Daras, Terence Comerford, Anthony Guy Marson, Munir Pirmohamed, Frank Dondelinger, Ana Alfirevic

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Sex differences in single and combined cardiovascular medication use.American heart journal plus : cardiology research and practice · 2026
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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

10 authors at 2 institutions in 1 country.

Jennifer DowningPharmacology and Therapeutics, University of Liverpool, Liverpool, UK j.downing@liverpool.ac.uk.ORCID 0000-0001-7691-1167
Rebecca TaylorLancaster Medical School, Lancaster University, Lancaster, UK.
Rachael MountainLancaster Medical School, Lancaster University, Lancaster, UK.
Ben BarrPublic Health and Policy, University of Liverpool, Liverpool, UK.
Konstantinos DarasPublic Health and Policy, University of Liverpool, Liverpool, UK.
Terence ComerfordNational Institute for Health and Care Research, Applied Research Collaboration North West Coast (NIHR ARC NWC), University of Liverpool, Liverpool, UK.
Anthony Guy MarsonPharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
Munir PirmohamedPharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
Frank DondelingerLancaster Medical School, Lancaster University, Lancaster, UK.
Ana AlfirevicPharmacology and Therapeutics, University of Liverpool, Liverpool, UK.
University of Liverpool · GBLancaster University · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo examine the socioeconomic and demographic drivers associated with polypharmacy (5-9 medicines), extreme polypharmacy (9-20 medicines) and increased medication count. DESIGN, SETTING AND

participantsA total of 5509 participants, from two waves of the English North West Coast, Household Health Survey were analysed OUTCOME MEASURES: Logistic regression modelling was used to find associations with polypharmacy and extreme polypharmacy. A negative binomial regression identified associations with increased medication count. Descriptive statistics explored associations with medication management.

resultsAge and number of health conditions account for the greatest odds of polypharmacy. ORs (95% CI) were greatest for those aged 65+ (3.87, 2.45 to 6.13) and for those with ≥5 health conditions (10.87, 5.94 to 19.88). Smaller odds were seen, for example, in those prescribed cardiovascular medications (3.08, 2.36 to 4.03), or reporting >3 emergency attendances (1.97, 1.23 to 3.17). Extreme polypharmacy was associated with living in a deprived neighbourhood (1.54, 1.06 to 2.26). The greatest risk of increased medication count was associated with age, number of health conditions and use of primary care services. Relative risks (95% CI) were greatest for those aged 65+ (2.51, 2.23 to 2.82), those with ≥5 conditions (10.26, 8.86 to 11.88) or those reporting >18 primary care visits (2.53, 2.18 to 2.93). Smaller risks were seen in, for example, respondents with higher levels of income deprivation (1.35, 1.03 to 1.77). Polypharmic respondents were more likely to report medication management difficulties associated with taking more than one medicine at a time (p<0.001). Furthermore, individuals reporting a mental health condition, were significantly more likely to consistently report difficulties managing their medication (p<0.001).

conclusionAge and number of health conditions are most associated with polypharmacy. Thus, delaying or preventing the onset of long-term conditions may help to reduce polypharmacy. Interventions to reduce income inequalities and health inequalities generally could support a reduction in polypharmacy, however, more research is needed in this area. Furthermore, increased prevention and support, particularly with medication management, for those with mental health conditions may reduce adverse medication effects.

Indexed as

Drug-Related Side Effects and Adverse ReactionsPolypharmacyHealth SurveysHumansLogistic ModelsSocioeconomic FactorsAgeingHealthcare utilisationHealth inequalitiesMedicationPolypharmacySocioeconomic

Identifiers

PMID35613765
PMCPMC9131085
OpenAlexW4281555223

What OpenQuestion holds

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LicenceCC BY-NC
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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.