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Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies
BMJ Open, Volume: 12, Issue: 4, Start page: e057017
Swansea University Authors:
Ashley Akbari , Ronan Lyons
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DOI (Published version): 10.1136/bmjopen-2021-057017
Abstract
Objective: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence. Methods: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, E...
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<?xml version="1.0"?><rfc1807><datestamp>2022-05-27T10:14:45.4501157</datestamp><bib-version>v2</bib-version><id>59936</id><entry>2022-05-02</entry><title>Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies</title><swanseaauthors><author><sid>aa1b025ec0243f708bb5eb0a93d6fb52</sid><ORCID>0000-0003-0814-0801</ORCID><firstname>Ashley</firstname><surname>Akbari</surname><name>Ashley Akbari</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>83efcf2a9dfcf8b55586999d3d152ac6</sid><ORCID>0000-0001-5225-000X</ORCID><firstname>Ronan</firstname><surname>Lyons</surname><name>Ronan Lyons</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2022-05-02</date><deptcode>HDAT</deptcode><abstract>Objective: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence. Methods: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics. Results: 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%). Conclusions: The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics.</abstract><type>Journal Article</type><journal>BMJ Open</journal><volume>12</volume><journalNumber>4</journalNumber><paginationStart>e057017</paginationStart><paginationEnd/><publisher>BMJ</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2044-6055</issnPrint><issnElectronic>2044-6055</issnElectronic><keywords>Systematic Review</keywords><publishedDay>29</publishedDay><publishedMonth>4</publishedMonth><publishedYear>2022</publishedYear><publishedDate>2022-04-29</publishedDate><doi>10.1136/bmjopen-2021-057017</doi><url/><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This study was funded by Health Data Research UK (CFC0110).</funders><lastEdited>2022-05-27T10:14:45.4501157</lastEdited><Created>2022-05-02T13:15:30.6040162</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Medicine</level></path><authors><author><firstname>Iris Szu-Szu</firstname><surname>Ho</surname><orcid>0000-0003-4980-7780</orcid><order>1</order></author><author><firstname>Amaya</firstname><surname>Azcoaga-Lorenzo</surname><orcid>0000-0003-3307-878x</orcid><order>2</order></author><author><firstname>Ashley</firstname><surname>Akbari</surname><orcid>0000-0003-0814-0801</orcid><order>3</order></author><author><firstname>Jim</firstname><surname>Davies</surname><order>4</order></author><author><firstname>Peter</firstname><surname>Hodgins</surname><order>5</order></author><author><firstname>Kamlesh</firstname><surname>Khunti</surname><orcid>0000-0003-2343-7099</orcid><order>6</order></author><author><firstname>Umesh</firstname><surname>Kadam</surname><order>7</order></author><author><firstname>Ronan</firstname><surname>Lyons</surname><orcid>0000-0001-5225-000X</orcid><order>8</order></author><author><firstname>Colin</firstname><surname>McCowan</surname><order>9</order></author><author><firstname>Stewart W</firstname><surname>Mercer</surname><orcid>0000-0002-1703-3664</orcid><order>10</order></author><author><firstname>Krishnarajah</firstname><surname>Nirantharakumar</surname><order>11</order></author><author><firstname>Bruce</firstname><surname>Guthrie</surname><orcid>0000-0003-4191-4880</orcid><order>12</order></author></authors><documents><document><filename>59936__24135__3c236881eafb4bc1894204810e81eb6a.pdf</filename><originalFilename>59936.VOR.pdf</originalFilename><uploaded>2022-05-20T11:47:16.3639231</uploaded><type>Output</type><contentLength>1919044</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>Distributed under a creative commons attribution non commercial (CC-BY-NC 4.0) licence.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by-nc/4.0/</licence></document></documents><OutputDurs/></rfc1807> |
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2022-05-27T10:14:45.4501157 v2 59936 2022-05-02 Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies aa1b025ec0243f708bb5eb0a93d6fb52 0000-0003-0814-0801 Ashley Akbari Ashley Akbari true false 83efcf2a9dfcf8b55586999d3d152ac6 0000-0001-5225-000X Ronan Lyons Ronan Lyons true false 2022-05-02 HDAT Objective: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence. Methods: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics. Results: 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%). Conclusions: The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics. Journal Article BMJ Open 12 4 e057017 BMJ 2044-6055 2044-6055 Systematic Review 29 4 2022 2022-04-29 10.1136/bmjopen-2021-057017 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University Another institution paid the OA fee This study was funded by Health Data Research UK (CFC0110). 2022-05-27T10:14:45.4501157 2022-05-02T13:15:30.6040162 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Iris Szu-Szu Ho 0000-0003-4980-7780 1 Amaya Azcoaga-Lorenzo 0000-0003-3307-878x 2 Ashley Akbari 0000-0003-0814-0801 3 Jim Davies 4 Peter Hodgins 5 Kamlesh Khunti 0000-0003-2343-7099 6 Umesh Kadam 7 Ronan Lyons 0000-0001-5225-000X 8 Colin McCowan 9 Stewart W Mercer 0000-0002-1703-3664 10 Krishnarajah Nirantharakumar 11 Bruce Guthrie 0000-0003-4191-4880 12 59936__24135__3c236881eafb4bc1894204810e81eb6a.pdf 59936.VOR.pdf 2022-05-20T11:47:16.3639231 Output 1919044 application/pdf Version of Record true Distributed under a creative commons attribution non commercial (CC-BY-NC 4.0) licence. true eng http://creativecommons.org/licenses/by-nc/4.0/ |
title |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
spellingShingle |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies Ashley Akbari Ronan Lyons |
title_short |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
title_full |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
title_fullStr |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
title_full_unstemmed |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
title_sort |
Variation in the estimated prevalence of multimorbidity: systematic review and meta-analysis of 193 international studies |
author_id_str_mv |
aa1b025ec0243f708bb5eb0a93d6fb52 83efcf2a9dfcf8b55586999d3d152ac6 |
author_id_fullname_str_mv |
aa1b025ec0243f708bb5eb0a93d6fb52_***_Ashley Akbari 83efcf2a9dfcf8b55586999d3d152ac6_***_Ronan Lyons |
author |
Ashley Akbari Ronan Lyons |
author2 |
Iris Szu-Szu Ho Amaya Azcoaga-Lorenzo Ashley Akbari Jim Davies Peter Hodgins Kamlesh Khunti Umesh Kadam Ronan Lyons Colin McCowan Stewart W Mercer Krishnarajah Nirantharakumar Bruce Guthrie |
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BMJ Open |
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12 |
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e057017 |
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2022 |
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Swansea University |
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2044-6055 2044-6055 |
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10.1136/bmjopen-2021-057017 |
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BMJ |
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Faculty of Medicine, Health and Life Sciences |
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Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine |
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description |
Objective: (1) To estimate the pooled prevalence of multimorbidity in all age groups, globally. (2) To examine how measurement of multimorbidity impacted the estimated prevalence. Methods: In this systematic review and meta-analysis, we conducted searches in nine bibliographic databases (PsycINFO, Embase, Global Health, Medline, Scopus, Web of Science, Cochrane Library, CINAHL and ProQuest Dissertations and Theses Global) for prevalence studies published between database inception and 21 January 2020. Studies reporting the prevalence of multimorbidity (in all age groups and in community, primary care, care home and hospital settings) were included. Studies with an index condition or those that did not include people with no long-term conditions in the denominator were excluded. Retrieved studies were independently reviewed by two reviewers, and relevant data were extracted using predesigned pro forma. We used meta-analysis to pool the estimated prevalence of multimorbidity across studies, and used random-effects meta-regression and subgroup analysis to examine the association of heterogeneous prevalence estimates with study and measure characteristics. Results: 13 807 titles were screened, of which 193 met inclusion criteria for meta-analysis. The pooled prevalence of multimorbidity was 42.4% (95% CI 38.9% to 46.0%) with high heterogeneity (I2 >99%). In adjusted meta-regression models, participant mean age and the number of conditions included in a measure accounted for 47.8% of heterogeneity in effect sizes. The estimated prevalence of multimorbidity was significantly higher in studies with older adults and those that included larger numbers of conditions. There was no significant difference in estimated prevalence between low-income or middle-income countries (36.8%) and high-income countries (44.3%), or between self-report (40.0%) and administrative/clinical databases (52.7%). Conclusions: The pooled prevalence of multimorbidity was significantly higher in older populations and when studies included a larger number of baseline conditions. The findings suggest that, to improve study comparability and quality of reporting, future studies should use a common core conditions set for multimorbidity measurement and report multimorbidity prevalence stratified by sociodemographics. |
published_date |
2022-04-29T04:17:37Z |
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11.021648 |