Evidence map›Paper›PMID 30612111›Full record

ArticleBMJ open2019

Predicting poorer health outcomes in older community-dwelling patients with multimorbidity: prospective cohort study assessing the accuracy of different multimorbidity definitions.

Maxime Sasseville, Susan M Smith, Lisa Freyne, Ronald McDowell, Fiona Boland, Martin Fortin, Emma Wallace

Abstract read
In one paragraph

Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Article
  5. Factors Affecting Medication Adherence in Middle-Aged and Elderly Patients in China: A Cross-Sectional Study.Medical science monitor : international medical journal of experimental and clinical research · 2025
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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.

Maxime SassevilleHealth Sciences, Université du Québec à Chicoutimi, Chicoutimi, Quebec, Canada.
Susan M SmithDepartment of General Practice, HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.
Lisa FreyneDepartment of General Practice, HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.
Ronald McDowellDepartment of General Practice, HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.
Fiona BolandDepartment of General Practice, HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.ORCID 0000-0003-3228-0046
Martin FortinFamily Medicine, Université de Sherbrooke, Chicoutimi, Quebec, Canada.
Emma WallaceDepartment of General Practice, HRB Centre for Primary Care Research, Royal College of Surgeons in Ireland (RCSI), Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMultimorbidity is commonly defined and measured using condition counts. The UK National Institute for Health Care Excellence Guidelines for Multimorbidity suggest that a medication-orientated approach could be used to identify those in need of a multimorbidity approach to management.

objectivesTo compare the accuracy of medication-based and diagnosis-based multimorbidity measures at higher cut-points to identify older community-dwelling patients who are at risk of poorer health outcomes.

designA secondary analysis of a prospective cohort study with a 2-year follow-up (2010-2012).

setting15 general practices in Ireland.

participants904 older community-dwelling patients. EXPOSURE: Baseline multimorbidity measurements based on both medication classes count (MCC) and chronic disease count (CDC). OUTCOMES: Mortality, self-reported health related quality of life, mental health and physical functioning at follow-up. ANALYSIS: Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) adjusting for clustering by practice for each outcome using both definitions.

resultsOf the 904 baseline participants, 53 died during follow-up and 673 patients completed the follow-up questionnaire. At baseline, 223 patients had 3 or more chronic conditions and 89 patients were prescribed 10 or more medication classes. Sensitivity was low for both MCC and CDC measures for all outcomes. For specificity, MCC was better for all outcomes with estimates varying from 88.8% (95% CI 85.2% to 91.6%) for physical functioning to 90.9% (95% CI 86.2% to 94.1%) for self-reported health-related quality of life. There were no differences between MCC and CDC in terms of PPV and NPV for any outcomes.

conclusionsNeither measure demonstrated high sensitivity. However, MCC using a definition of 10 or more regular medication classes to define multimorbidity had higher specificity for predicting poorer health outcomes. While having limitations, this definition could be used for proactive identification of patients who may benefit from targeted clinical care.

Indexed as

MultimorbidityPolypharmacyAgedAged, 80 and overChronic DiseaseFemaleFollow-Up StudiesHumansIndependent LivingMaleProspective StudiesQuality of LifeRisk FactorsSelf Reportchronic diseasesmedicationsmultimorbiditypolypharmacyrisk prediction

Identifiers

PMID30612111
PMCPMC6326333

What OpenQuestion holds

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LicenceCC BY-NC
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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 OpenQuestion graph.