CoChat
Sign Up
Cited by 296Chronic Disease Management StrategiesPharmaceutical Practices and Patient OutcomesPrimary Care and Health Outcomes

The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.

The influence of age, gender and socio-economic status on multimorbidity patterns in primary care. first results from the multicare cohort study

Open access
Schäfer, Ingmar, Hansen, Heike, Schön, Gerhard, Höfels, Susanne, Altiner, Attila, Dahlhaus, Anne, Gensichen, Jochen, Riedel-Heller, Steffi, Weyerer, Siegfried, Blank, Wolfgang A, König, Hans-Helmut, von dem Knesebeck, Olaf, Wegscheider, Karl, Scherer, Martin, van den Bussche, Hendrik, Wiese, Birgitt·2012·BMC Health Services Research
DOI 10.1186/1472-6963-12-89PMC PMC3348059
Open this paper in CoChat — free accountOpen PDF

Abstract

BACKGROUND: Multimorbidity is a phenomenon with high burden and high prevalence in the elderly. Our previous research has shown that multimorbidity can be divided into the multimorbidity patterns of 1) anxiety, depression, somatoform disorders (ADS) and pain, and 2) cardiovascular and metabolic disorders. However, it is not yet known, how these patterns are influenced by patient characteristics. The objective of this paper is to analyze the association of socio-demographic variables, and especially socio-economic status with multimorbidity in general and with each multimorbidity pattern. METHODS: The MultiCare Cohort Study is a multicentre, prospective, observational cohort study of 3.189 multimorbid patients aged 65+ randomly selected from 158 GP practices. Data were collected in GP interviews and comprehensive patient interviews. Missing values have been imputed by hot deck imputation based on Gower distance in morbidity and other variables. The association of patient characteristics with the number of chronic conditions is analysed by multilevel mixed-effects linear regression analyses. RESULTS: Multimorbidity in general is associated with age (+0.07 chronic conditions per year), gender (-0.27 conditions for female), education (-0.26 conditions for medium and -0.29 conditions for high level vs. low level) and income (-0.27 conditions per logarithmic unit). The pattern of cardiovascular and metabolic disorders shows comparable associations with a higher coefficient for gender (-1.29 conditions for female), while multimorbidity within the pattern of ADS and pain correlates with gender (+0.79 conditions for female), but not with age or socioeconomic status. CONCLUSIONS: Our study confirms that the morbidity load of multimorbid patients is associated with age, gender and the socioeconomic status of the patients, but there were no effects of living arrangements and marital status. We could also show that the influence of patient characteristics is dependent on the multimorbidity pattern concerned, i.e. there seem to be at least two types of elderly multimorbid patients. First, there are patients with mainly cardiovascular and metabolic disorders, who are more often male, have an older age and a lower socio-economic status. Second, there are patients mainly with ADS and pain-related morbidity, who are more often female and equally distributed across age and socio-economic groups. TRIAL REGISTRATION: ISRCTN89818205.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/1472-6963-12-89

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Sampling and response rate. * the inclusion criterion "multimorbidity" is defined as at least 3 out of 29 ICD10-based diagnosis groups.
Figure 1 Sampling and response rate. * the inclusion criterion "multimorbidity" is defined as at least 3 out of 29 ICD10-based diagnosis groups.CC BY · PubMed Central
Figure 2 Overlapping of multimorbidity patterns (in %) related to the female population.
Figure 2 Overlapping of multimorbidity patterns (in %) related to the female population.CC BY · PubMed Central
Figure 3 Overlapping of multimorbidity patterns (in %) related to the male population.
Figure 3 Overlapping of multimorbidity patterns (in %) related to the male population.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 53 references

Research works printed but not linked: 7

  1. Kok R, Avendano M, Mackenbach JP: The association between socioeconomic status and changes in health in Europe. Health, ageing and retirement in Europe (2004-2007): starting the longitudinal dimension. Edited by: Börsch-Supan A, Brugiavini A, Jürges H, Kapteyn A, Mackenbach J, Siegrist J, Weber G. 2008, Mannheim: Strauss, 123-128.
  2. Kassenärztliche Bundesvereinigung: Grunddaten 2006 zur vertragsärztlichen Versorgung in Deutschland. [Basic data for 2006 of statutory health care in Germany.]. 2006, [ http://www.kbv.de/publikationen/125.html ] zur vertragsärztlichen Versorgung in Deutschland. [Basic data for 2006 of statutory health care in Germany.]
  3. Fydrich T, Sommer G, Brähler E: F-SozU: Fragebogen zur Sozialen Unterstützung. [F-SozU: Questionnaire on social support.]. 2007, Göttingen: Hogrefe
  4. Datenreport 2006. Edited by: Statistisches Bundesamt. 2006, Bonn: Bundeszentrale für politische Bildung
  5. Kopetsch T: Dem deutschen Gesundheitswesen gehen die Ärzte aus! Studie zur Altersstruktur-und Arztzahlentwicklung. 2010, Berlin: Bundesärztekammer und Kassenärztliche Bundesvereinigung, [The German public health sector is running short of physicians! Study on the development of age structure and numbers of physicians.]
  6. Rothgang H, Kulik D, Müller R, Unger R: GEK Pflegereport 2009. 2009, Schwäbisch Gmünd: Asgard Verlagsservice, [GEK report on nursing care.]
  7. Im Blickpunkt: Ältere Menschen in Deutschland und der EU. Edited by: Statistisches Bundesam. 2011, Bonn: Focus on the Elderly in Germany and the EU

Cited by

Sorted by citations

Items 101–200 of 294

Related

See all

Metadata

Authors
Schäfer, I., Hansen, H., Schön, G., Höfels, S., Altiner, A., Dahlhaus, A., Gensichen, J., Riedel-Heller, S., Weyerer, S., Blank, W. A., König, H.-H., von dem Knesebeck, O., Wegscheider, K., Scherer, M., van den Bussche, H., & Wiese, B.
Affiliations
  • University Medical Center Hamburg-Eppendorf (DE)
  • University of Bonn (DE)
  • University of Rostock (DE)
  • Goethe University Frankfurt (DE)
  • Friedrich Schiller University Jena (DE)
  • Leipzig University (DE)
  • Central Institute of Mental Health (DE)
  • Technical University of Munich (DE)
  • Medizinische Hochschule Hannover (DE)
Corresponding author
Schäfer, Ingmar
ORCID
Venue
BMC Health Services Research
Publisher
Springer Science and Business Media LLC
Published
2012-04-03
Volume
12
Issue
1
Pages
89-89
ISSN
1472-6963 (ISSN-L)
DOI
10.1186/1472-6963-12-89
PMC
PMC3348059
OpenAlex
W2156394420
Cited by
296
References
53
Citations by year
2026: 6 · 2025: 13 · 2024: 16 · 2023: 20 · 2022: 28 · 2021: 22 · 2020: 28 · 2019: 19 · 2018: 22 · 2017: 22 · 2016: 17 · 2015: 32 · 2014: 32 · 2013: 15 · 2012: 4
FWCI
19.88
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Health Services Research (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Publication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) (submittedVersion, cc-by)
Topics
  • Health Sciences › Medicine › Epidemiology › Chronic Disease Management Strategies
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
  • Health Sciences › Medicine › Family Practice › Primary Care and Health Outcomes
Keywords
multimorbidity, multimorbidity patterns, socioeconomic status, gender differences, age, elderly patients, pain, income, primary care
MeSH terms
Age Factors, Aged, Aged, 80 and over, Chronic Disease, Diagnosis-Related Groups, Family Practice, Female, Germany, Humans, Health Knowledge, Attitudes, Practice, Male, Primary Health Care, Prospective Studies, Quality of Life, Surveys and Questionnaires, Risk Factors, Sex Factors, Social Class, Cohort Studies, Comorbidity, Prevalence, International Classification of Diseases

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review