Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.

<h4>Objectives</h4>The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The sec...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alexandra Prados-Torres, Beatriz Poblador-Plou, Amaia Calderón-Larrañaga, Luis Andrés Gimeno-Feliu, Francisca González-Rubio, Antonio Poncel-Falcó, Antoni Sicras-Mainar, José Tomás Alcalá-Nalvaiz
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c23b3c252d0d466eac51c8b41f7704c0
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c23b3c252d0d466eac51c8b41f7704c0
record_format	dspace
spelling	oai:doaj.org-article:c23b3c252d0d466eac51c8b41f7704c02021-11-18T07:26:26ZMultimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.1932-620310.1371/journal.pone.0032190https://doaj.org/article/c23b3c252d0d466eac51c8b41f7704c02012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22393389/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objectives</h4>The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.<h4>Methods</h4>This observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.<h4>Results</h4>Multimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.<h4>Conclusions</h4>Non-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.Alexandra Prados-TorresBeatriz Poblador-PlouAmaia Calderón-LarrañagaLuis Andrés Gimeno-FeliuFrancisca González-RubioAntonio Poncel-FalcóAntoni Sicras-MainarJosé Tomás Alcalá-NalvaizPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 2, p e32190 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexandra Prados-Torres Beatriz Poblador-Plou Amaia Calderón-Larrañaga Luis Andrés Gimeno-Feliu Francisca González-Rubio Antonio Poncel-Falcó Antoni Sicras-Mainar José Tomás Alcalá-Nalvaiz Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
description	<h4>Objectives</h4>The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.<h4>Methods</h4>This observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.<h4>Results</h4>Multimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.<h4>Conclusions</h4>Non-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.
format	article
author	Alexandra Prados-Torres Beatriz Poblador-Plou Amaia Calderón-Larrañaga Luis Andrés Gimeno-Feliu Francisca González-Rubio Antonio Poncel-Falcó Antoni Sicras-Mainar José Tomás Alcalá-Nalvaiz
author_facet	Alexandra Prados-Torres Beatriz Poblador-Plou Amaia Calderón-Larrañaga Luis Andrés Gimeno-Feliu Francisca González-Rubio Antonio Poncel-Falcó Antoni Sicras-Mainar José Tomás Alcalá-Nalvaiz
author_sort	Alexandra Prados-Torres
title	Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
title_short	Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
title_full	Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
title_fullStr	Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
title_full_unstemmed	Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
title_sort	multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/c23b3c252d0d466eac51c8b41f7704c0
work_keys_str_mv	AT alexandrapradostorres multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT beatrizpobladorplou multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT amaiacalderonlarranaga multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT luisandresgimenofeliu multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT franciscagonzalezrubio multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT antonioponcelfalco multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT antonisicrasmainar multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis AT josetomasalcalanalvaiz multimorbiditypatternsinprimarycareinteractionsamongchronicdiseasesusingfactoranalysis
_version_	1718423498999726080

Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis.

Ejemplares similares