Evidence map›Paper›PMID 19591524›Full record

ArticleDrugs & aging2009

Patterns of drug use and factors associated with polypharmacy and excessive polypharmacy in elderly persons: results of the Kuopio 75+ study: a cross-sectional analysis.

Johanna Jyrkkä, Hannes Enlund, Maarit J Korhonen, Raimo Sulkava, Sirpa Hartikainen

Abstract read
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In one paragraph

Article in Drugs & aging, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 2 pooled it
5.8field-weighted citation impact, top 4% 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

97 citing papers in PubMed, 2 syntheses or guidelines pooled it, 219 citations in OpenAlex.

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  8. Article
  9. Polypharmacy in Patients With COPD: A Scoping Review.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2025
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  17. Cohort profile: Beijing Healthy Aging Cohort Study (BHACS).European journal of epidemiology · 2024
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37 more citing papers are in PubMed but not listed here.

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

5 authors at 2 institutions in 2 countries.

Johanna JyrkkäSchool of Public Health and Clinical Nutrition, University of Kuopio, Kuopio, Finland. johanna.jyrkka@uku.fi
Hannes Enlund
Maarit J Korhonen
Raimo Sulkava
Sirpa Hartikainen
University of Eastern Finland · FIKuwait University · KW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough the increasing use of drugs in elderly persons has raised many concerns in recent years, the process leading to polypharmacy (PP) and excessive polypharmacy (EPP) remains largely unknown.

objectiveTo describe the number and type of drugs used and to evaluate the role of different factors associated with PP (i.e. 6-9 drugs) and EPP (i.e. > or =10 drugs), with special reference to the number and type of medical diagnoses and symptoms, in a population of home-dwelling elderly persons aged > or =75 years.

methodsThe study was a cross-sectional analysis of a population-based cohort in 1998. The population consisted of home-dwelling elderly persons aged > or =75 years in the city of Kuopio, Finland. The data for the analysis were obtained from the Kuopio 75+ Study, which drew a random sample of 700 elderly residents aged > or =75 years living in the city of Kuopio from the population register. Of these, 601 attended a structured clinical examination and an interview carried out by a geriatrician and a trained nurse in 1998. For this analysis, all home-dwelling elderly participants (n = 523) were included. Study data were expressed as proportions and means with standard deviations. The factors associated with PP and EPP were examined by multinomial logistic regression.

resultsThe most commonly used drugs were cardiovascular drugs (97% in EPP, 94% in PP and 59% in non-PP group) and analgesics (89%, 76% and 54%), respectively. Use of psychotropics was markedly higher in the EPP group (77%) than in the PP (42%) and non-PP groups (20%). The mean number of drugs per diagnosis was 3.6 in the EPP group, 2.6 in the PP group and 1.6 in the non-PP group. Factors associated only with EPP were moderate self-reported health (odds ratio [OR] 2.05; 95% CI 1.08, 3.89), female gender (OR 2.43; 95% CI 1.27, 4.65) and age > or =85 years (OR 2.84; 95% CI 1.41, 5.72). Factors that were associated with both PP and EPP included poor self-reported health (PP: OR 2.15; 95% CI 1.01, 4.59 and EPP: OR 6.02; 95% CI 2.55, 14.20), diabetes mellitus (PP: OR 2.28; 95% CI 1.26, 4.15 and EPP: OR 2.07; 95% CI 1.03, 4.18), depression (PP: OR 2.13; 95% CI 1.16, 3.90 and EPP: OR 2.93; 95% CI 1.51, 5.66), pain (PP: OR 2.69; 95% CI 1.68, 4.30 and EPP: OR 2.74; 95% CI 1.56, 4.82), heart disease (PP: OR 2.51; 95% CI 1.54, 4.08 and EPP: OR 4.63; 95% CI 2.45, 8.74) and obstructive pulmonary disease (including asthma or chronic obstructive pulmonary disease) [PP: OR 2.79; 95% CI 1.24, 6.25 and EPP: OR 6.82; 95% CI 2.87, 16.20].

conclusionThe study indicates that the factors associated with PP and EPP are not uniform. Age > or =85 years, female gender and moderate self-reported health were factors associated only with EPP, while poor self-reported health and several specific disease states were associated with both PP and EPP. The high number of drugs per diagnosis observed in this study calls for a thorough assessment of the need for and outcomes associated with use of these drugs.

Indexed as

Drug Utilization ReviewPolypharmacyAgedAged, 80 and overAge FactorsAnalgesicsCardiovascular AgentsCross-Sectional StudiesData CollectionDrug PrescriptionsDrug Therapy, CombinationFemaleFinlandHomebound PersonsHumansLogistic ModelsAnalgesicsCardiovascular AgentsPsychotropic Drugs

Identifiers

PMID19591524
OpenAlexW2088141733

What OpenQuestion holds

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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.