<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">OALibJ</journal-id><journal-title-group><journal-title>Open Access Library Journal</journal-title></journal-title-group><issn pub-type="epub">2333-9705</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/oalib.1111532</article-id><article-id pub-id-type="publisher-id">OALibJ-133157</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biomedical&amp;Life Sciences</subject><subject> Business&amp;Economics</subject><subject> Chemistry&amp;Materials Science</subject><subject> Computer Science&amp;Communications</subject><subject> Earth&amp;Environmental Sciences</subject><subject> Engineering</subject><subject> Medicine&amp;Healthcare</subject><subject> Physics&amp;Mathematics</subject><subject> Social Sciences&amp;Humanities</subject></subj-group></article-categories><title-group><article-title>
 
 
  Association between Multimorbidity and Quality of Life among Adults Attending Outpatient Clinics in the Ashanti Region: A Cross-Sectional Study
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jane</surname><given-names>Acquaye</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Joseph</surname><given-names>Kwasi Brenyah</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Isaac</surname><given-names>Asenso Brobbey-Kyei</given-names></name><xref ref-type="aff" rid="aff3"><sup>3</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Emmanuel</surname><given-names>Brobbey-Kyei</given-names></name><xref ref-type="aff" rid="aff4"><sup>4</sup></xref></contrib></contrib-group><aff id="aff2"><addr-line>Department of Global and International Health, School of Public Health, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana</addr-line></aff><aff id="aff4"><addr-line>Churchfield Home Services, Dublin, Ireland</addr-line></aff><aff id="aff3"><addr-line>Ghana Health Service, Kumasi, Ghana</addr-line></aff><aff id="aff1"><addr-line>National Institute of Health Research, Global Surgery Unit-Ghana Hub, Tamale, Ghana</addr-line></aff><pub-date pub-type="epub"><day>09</day><month>05</month><year>2024</year></pub-date><volume>11</volume><issue>05</issue><fpage>1</fpage><lpage>13</lpage><history><date date-type="received"><day>4,</day>	<month>April</month>	<year>2024</year></date><date date-type="rev-recd"><day>13,</day>	<month>May</month>	<year>2024</year>	</date><date date-type="accepted"><day>16,</day>	<month>May</month>	<year>2024</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  Multimorbidity, the coexistence of two or more chronic conditions in an individual, is increasingly prevalent worldwide, posing significant challenges for healthcare systems and patient well-being. This cross-sectional study aims to investigate the association between multimorbidity and quality of life (QoL) among adults attending outpatient clinics in health facilities within the Ashanti Region of Ghana. &lt;b&gt;Method&lt;/b&gt;: A sample of n = 400 participants were recruited using convenience sampling. Data were collected through structured interviews using the Short Form Health Survey (SF-36) to assess QoL and a checklist to ascertain multimorbidity status. Statistical analyses including correlation and regression analyses were performed to explore the relationship between multimorbidity and QoL, adjusting for potential confounders. &lt;b&gt;Results&lt;/b&gt;: The findings reveal that there is no statistically significant association between multi-morbidity and the perceived changes in current health compared to one year ago (X&lt;sup&gt;2&lt;/sup&gt; = 4.814, p = 0.307), with 11.76% of those with multi-morbidity reporting better health, and 11.14% of gnon-multi-morbid individuals reporting the same. Similarly, role function, general health, and energy and fatigue did not demonstrate statistically significant associations with multi-morbidity. However, the emotional problem variable approached significance (X&lt;sup&gt;2&lt;/sup&gt; = 9.299, p = 0.054*), with 35.29% of individuals with multi-morbidity experiencing emotional issues compared to 25.90% among non-multi-morbid individuals. Notably, health change exhibited a significant association (X&lt;sup&gt;2&lt;/sup&gt; = 4.812, p = 0.028), indicating that 73.53% of those with multi-morbidity reported a worsening health change, compared to 59.34% of non-multi-morbid individuals. &lt;b&gt;Conclusion&lt;/b&gt;: This study sheds light on the nuanced relationship between multi-morbidity and various dimensions of perceived health. While no significant associations were found between multi-morbidity and certain aspects such as role function, general health perception, and energy/fatigue levels, notable findings emerged regarding emotional well-being and health changes over time.
 
</p></abstract><kwd-group><kwd>Multimorbidity</kwd><kwd> Quality of Life</kwd><kwd> Outpatient Clinics</kwd><kwd> Ashanti Region</kwd><kwd> Ghana</kwd><kwd> SF-36</kwd><kwd> Cross-Sectional Study</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>The evolving demographics and social dynamics accompanying aging populations are driving swift epidemiological shifts, notably marked by the surge of chronic non-communicable diseases (NCDs) and multimorbidity [<xref ref-type="bibr" rid="scirp.133157-ref1">1</xref>] . Multimorbidity denotes the coexistence of two or more long-term conditions, whether related or unrelated, within an individual [<xref ref-type="bibr" rid="scirp.133157-ref2">2</xref>] . Despite methodological variations in defining and assessing multimorbidity [<xref ref-type="bibr" rid="scirp.133157-ref3">3</xref>] , its global burden is demonstrably increasing [<xref ref-type="bibr" rid="scirp.133157-ref4">4</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref5">5</xref>] , extending to low- and middle-income countries (LMICs) [<xref ref-type="bibr" rid="scirp.133157-ref6">6</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref7">7</xref>] , and specifically, Ghana [<xref ref-type="bibr" rid="scirp.133157-ref8">8</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref9">9</xref>] . By 2035, multimorbidity prevalence is anticipated to double, with a forecast that the majority of individuals aged over 65 will contend with four or more chronic ailments [<xref ref-type="bibr" rid="scirp.133157-ref10">10</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref11">11</xref>] . These people may either be within the outpatients’ category or maybe on admission to a healthcare facility. This study therefore seeks to assess the association between multimorbidity and quality of life among adults attending outpatient clinics in the Ashanti Region in Ghana.</p><p>The emergence of multimorbidity within the population has multiple contributors. Several factors, including advanced age [<xref ref-type="bibr" rid="scirp.133157-ref4">4</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref12">12</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref13">13</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref14">14</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref15">15</xref>] , socioeconomic disparities [<xref ref-type="bibr" rid="scirp.133157-ref15">15</xref>] , obesity [<xref ref-type="bibr" rid="scirp.133157-ref16">16</xref>] , gender (female) [<xref ref-type="bibr" rid="scirp.133157-ref17">17</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref18">18</xref>] , sedentary lifestyle [<xref ref-type="bibr" rid="scirp.133157-ref19">19</xref>] , tobacco and alcohol use [<xref ref-type="bibr" rid="scirp.133157-ref20">20</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref21">21</xref>] , and psychosocial elements like limited social networks and external locus of control [<xref ref-type="bibr" rid="scirp.133157-ref22">22</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref23">23</xref>] , are commonly associated with multimorbidity in global literature. Multimorbidity profoundly impacts various facets of patients’ lives, manifesting in diminished quality of life (QoL), heightened disability, functional deterioration, and escalated healthcare expenditures [<xref ref-type="bibr" rid="scirp.133157-ref19">19</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref24">24</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref25">25</xref>] . Its adverse effect on QoL is particularly pronounced among middle-aged and elderly populations, females, and those with comorbid mental health conditions [<xref ref-type="bibr" rid="scirp.133157-ref17">17</xref>] .</p><p>The experience of multimorbidity transcends the sum of individual chronic conditions [<xref ref-type="bibr" rid="scirp.133157-ref26">26</xref>] , with specific disease clusters exerting distinct effects on physical and psychological well-being [<xref ref-type="bibr" rid="scirp.133157-ref27">27</xref>] . Consequently, individuals with multimorbidity consistently report lower health-related QoL compared to those without [<xref ref-type="bibr" rid="scirp.133157-ref28">28</xref>] . The enduring presence of multimorbidity poses substantial challenges for healthcare systems [<xref ref-type="bibr" rid="scirp.133157-ref29">29</xref>] , given that individuals with diverse NCD combinations harbor varying needs and priorities [<xref ref-type="bibr" rid="scirp.133157-ref30">30</xref>] . Regrettably, insufficient attention is accorded to the preferences of individuals managing multiple health issues, especially among outpatients whose state of health conditions are not usually deemed critical [<xref ref-type="bibr" rid="scirp.133157-ref31">31</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref32">32</xref>] . Furthermore, prevailing care models and guidelines, predominantly rooted in single disease paradigms, often overlook the holistic needs and circumstances of complex care patients [<xref ref-type="bibr" rid="scirp.133157-ref33">33</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref34">34</xref>] . Consequently, individuals grappling with multiple conditions frequently interact with disparate healthcare professionals, resulting in fragmented, uncoordinated, and compartmentalized patient management [<xref ref-type="bibr" rid="scirp.133157-ref2">2</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref35">35</xref>] . The advent of infections such as COVID-19 exacerbates this complexity, exacerbating the burden on healthcare systems and worsening outcomes for those with pre-existing chronic diseases and multimorbidity [<xref ref-type="bibr" rid="scirp.133157-ref36">36</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref37">37</xref>] .</p><p>While numerous studies have explored the nexus between QoL and multimorbidity [<xref ref-type="bibr" rid="scirp.133157-ref38">38</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref39">39</xref>] , the majority have been conducted in high-income countries, employing disparate QoL measurement tools [<xref ref-type="bibr" rid="scirp.133157-ref40">40</xref>] . Notably, the Short Form Health Survey (SF-12) emerges as an efficient algorithm for reproducing the SF-36 tool to gauge health-related QoL [<xref ref-type="bibr" rid="scirp.133157-ref41">41</xref>] .</p><p>However, methodological disparities persist, extending to data analysis techniques [<xref ref-type="bibr" rid="scirp.133157-ref42">42</xref>] . Ordinal regression models emerge as a more sensitive and comprehensive approach, superior to conventional methods in analyzing ordered outcomes like QoL [<xref ref-type="bibr" rid="scirp.133157-ref43">43</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref44">44</xref>] .</p><p>Yet, challenges remain, as data often fail to meet the assumptions of proportional odds models [<xref ref-type="bibr" rid="scirp.133157-ref43">43</xref>] . In such instances, a more pragmatic approach, such as the partial proportional odds (PPO) model, proves effective, offering insights into unobserved heterogeneity and identifying correlates of negative health outcomes, including impaired QoL [<xref ref-type="bibr" rid="scirp.133157-ref42">42</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref44">44</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref45">45</xref>] .</p><p>Addressing the QoL concerns of individuals with multimorbidity represents a pivotal challenge for contemporary healthcare and social systems [<xref ref-type="bibr" rid="scirp.133157-ref25">25</xref>] . Consequently, there’s a growing call to tailor multimorbidity management to account for its impact on individuals’ QoL and their unique priorities [<xref ref-type="bibr" rid="scirp.133157-ref46">46</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref47">47</xref>] . However, understanding the impact of multimorbidity on health-related QoL in Ghana remains scant, necessitating comprehensive assessments to inform targeted interventions.</p></sec><sec id="s2"><title>2. Methodology</title><sec id="s2_1"><title>2.1. Study Design</title><p>This cross-sectional study design employed a quantitative research approach to investigate the association between multimorbidity and QoL among outpatient adults.</p></sec><sec id="s2_2"><title>2.2. Study Setting and Participants</title><p>The study was conducted at Komfo Anokye Teaching Hospital (the second largest hospital in Ghana) from April 2023 to July 2024 involving outpatient adults attending outpatient clinics. The facility serves as a referral centre notably in southern and northern Ghana.</p></sec><sec id="s2_3"><title>2.3. Sample Size Calculation</title><p>The sample size was calculated using the formula for estimating a single proportion, with a 95% confidence level and a margin of error of 5%. Considering an anticipated prevalence of multimorbidity of 50% and assuming a non-response rate of 10%, the estimated sample size was n = 400.</p></sec><sec id="s2_4"><title>2.3. Data Collection</title><p>Data was collected with structured questionnaire by trained research assistants. The Short Form Health Survey (SF-36) was also used to assess QoL, while the questionnaire contained a checklist that was used to ascertain the presence of multimorbidity. Information on sociodemographic characteristics, medical history, and healthcare utilization were also solicited.</p></sec><sec id="s2_5"><title>2.4. Data Analysis</title><p>Descriptive statistics was used to summarize the characteristics of the study population. The association between multimorbidity and QoL was examined using correlation and regression analyses, adjusting for potential confounders such as age, sex, socioeconomic status, and comorbidity burden.</p></sec><sec id="s2_6"><title>2.5. Ethical Considerations</title><p>Ethical approval was obtained from the Komfo Anokye Teaching Hospital Institutional Review Board or ethics committees. Written informed consents were obtained from all study participants, and measures were taken to ensure confidentiality and privacy throughout the study.</p></sec></sec><sec id="s3"><title>3. Results</title><p><xref ref-type="table" rid="table1">Table 1</xref> shows the sociodemographic characteristics of respondents is presented in <xref ref-type="table" rid="table1">Table 1</xref>. With a median age of 43 years, the respondents span a wide age range, from below 20 years to those aged 60 years and above. Notably, individuals aged 60 years and above constitute the largest group, making up 27.50% of the sample. The study maintains a balanced gender representation, with females comprising 52.00% and males 48.00% of the respondents. Occupationally, the unemployed form the largest group at 37.75%, followed by traders, students, and farmers. Educational diversity is evident, with categories ranging from no formal education to tertiary education, allowing for a nuanced exploration of multi-morbidity across educational strata. The residence type exhibits a mix of urban and rural settings, with 43.00% per urban, 14.25% urban, and 42.75% rural. Ethnically, the majority of respondents identify as Akan (91.00%), while smaller proportions represent Ga, Northerner, and Voltarian ethnicities. This sociodemographic framework provides a solid foundation for analyzing multi-morbidity trends, ensuring a comprehensive and inclusive examination of health patterns among diverse demographic segments in the Ashanti Region.</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Distribution of respondents’ sociodemographic information</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Variable</th><th align="center" valign="middle" >Frequency (n = 400)</th><th align="center" valign="middle" >Percentage (%)</th></tr></thead><tr><td align="center" valign="middle" >Age</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >median age = 43years</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >25% quartile = 25 years</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >75% quartile = 61years</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Age groups</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Below 20 years</td><td align="center" valign="middle" >54</td><td align="center" valign="middle" >13.50</td></tr><tr><td align="center" valign="middle" >20 - 29 years</td><td align="center" valign="middle" >74</td><td align="center" valign="middle" >18.50</td></tr><tr><td align="center" valign="middle" >30 - 39 years</td><td align="center" valign="middle" >60</td><td align="center" valign="middle" >15.00</td></tr><tr><td align="center" valign="middle" >40 - 49 years</td><td align="center" valign="middle" >44</td><td align="center" valign="middle" >11.00</td></tr><tr><td align="center" valign="middle" >50 - 59 years</td><td align="center" valign="middle" >58</td><td align="center" valign="middle" >14.50</td></tr><tr><td align="center" valign="middle" >60 years and above</td><td align="center" valign="middle" >110</td><td align="center" valign="middle" >27.50</td></tr><tr><td align="center" valign="middle" >Gender</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Female</td><td align="center" valign="middle" >206</td><td align="center" valign="middle" >52.00</td></tr><tr><td align="center" valign="middle" >Male</td><td align="center" valign="middle" >192</td><td align="center" valign="middle" >48.00</td></tr><tr><td align="center" valign="middle" >Occupation</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Unemployed</td><td align="center" valign="middle" >151</td><td align="center" valign="middle" >37.75</td></tr><tr><td align="center" valign="middle" >Farmer</td><td align="center" valign="middle" >40</td><td align="center" valign="middle" >10.00</td></tr><tr><td align="center" valign="middle" >Trader</td><td align="center" valign="middle" >87</td><td align="center" valign="middle" >21.75</td></tr><tr><td align="center" valign="middle" >Student</td><td align="center" valign="middle" >43</td><td align="center" valign="middle" >10.75</td></tr><tr><td align="center" valign="middle" >Artisan</td><td align="center" valign="middle" >19</td><td align="center" valign="middle" >4.75</td></tr><tr><td align="center" valign="middle" >Civil Servant</td><td align="center" valign="middle" >32</td><td align="center" valign="middle" >8.00</td></tr><tr><td align="center" valign="middle" >Retired</td><td align="center" valign="middle" >28</td><td align="center" valign="middle" >7.00</td></tr><tr><td align="center" valign="middle" >Educational level</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >No formal education</td><td align="center" valign="middle" >106</td><td align="center" valign="middle" >26.50</td></tr><tr><td align="center" valign="middle" >Primary/JSS</td><td align="center" valign="middle" >127</td><td align="center" valign="middle" >31.75</td></tr><tr><td align="center" valign="middle" >Secondary</td><td align="center" valign="middle" >89</td><td align="center" valign="middle" >22.25</td></tr><tr><td align="center" valign="middle" >Tertiary</td><td align="center" valign="middle" >78</td><td align="center" valign="middle" >19.50</td></tr><tr><td align="center" valign="middle" >Residence type</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Per Urban</td><td align="center" valign="middle" >172</td><td align="center" valign="middle" >43.00</td></tr><tr><td align="center" valign="middle" >Urban</td><td align="center" valign="middle" >57</td><td align="center" valign="middle" >14.25</td></tr><tr><td align="center" valign="middle" >Rural</td><td align="center" valign="middle" >171</td><td align="center" valign="middle" >42.75</td></tr><tr><td align="center" valign="middle" >Ethnicity</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Akan</td><td align="center" valign="middle" >364</td><td align="center" valign="middle" >91.00</td></tr><tr><td align="center" valign="middle" >Ga</td><td align="center" valign="middle" >4</td><td align="center" valign="middle" >1.00</td></tr><tr><td align="center" valign="middle" >Northerner</td><td align="center" valign="middle" >25</td><td align="center" valign="middle" >6.25</td></tr><tr><td align="center" valign="middle" >Voltarian</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >1.75</td></tr></tbody></table></table-wrap><p><xref ref-type="table" rid="table2">Table 2</xref> shows the exploration of the relationship between multi-morbidity and the quality of life (QOL) among adults attending outpatient clinics in the Ashanti Region involves a detailed analysis of a 400-respondent sample. The study considers variables such as current health compared to one year ago, role function, general health, energy and fatigue, emotional problems, health change, pain, and social activity. The findings reveal that there is no statistically significant association between multi-morbidity and the perceived changes in current health compared to one year ago (X<sup>2</sup> = 4.814, p = 0.307), with 11.76% of those with multi-morbidity reporting better health, and 11.14% of non-multi-morbid individuals reporting the same. Similarly, role function, general health, and energy and fatigue did not demonstrate statistically significant associations with multi-morbidity. However, the emotional problem variable approached significance (X<sup>2</sup> = 9.299, p = 0.054*), with 35.29% of individuals with multi-morbidity experiencing emotional issues compared to 25.90% among non-multi-morbid individuals. Notably, health change exhibited a significant association (X<sup>2</sup> = 4.812, p = 0.028), indicating that 73.53% of those with multi-morbidity reported a worsening health change, compared to 59.34% of non-multi-morbid individuals. The variables of pain and social activity affected did not show significant associations with multi-morbidity. This in-depth analysis of the relationship between multi-morbidity and various dimensions of QOL emphasizes the need to address both physical health and emotional well-being in managing individuals with multiple health conditions.</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Association between multi-morbidity and quality of life among adults attending the outpatient clinic</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="3"  >Variable</th><th align="center" valign="middle"  colspan="2"  >Frequency (n = 400) (%)</th><th align="center" valign="middle"  rowspan="3"  >X<sup>2</sup></th><th align="center" valign="middle"  rowspan="3"  >P-value</th></tr></thead><tr><td align="center" valign="middle"  colspan="2"  >Morbidity</td></tr><tr><td align="center" valign="middle" >Multi-morbidity</td><td align="center" valign="middle" >Non-multi morbidity</td></tr><tr><td align="center" valign="middle" >Current health compared to 1 year</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Better</td><td align="center" valign="middle" >8 (11.76)</td><td align="center" valign="middle" >37 (11.14)</td><td align="center" valign="middle" >4.814</td><td align="center" valign="middle" >0.307</td></tr><tr><td align="center" valign="middle" >Good</td><td align="center" valign="middle" >10 (14.71)</td><td align="center" valign="middle" >80 (24.10</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Equal</td><td align="center" valign="middle" >17 (25.00)</td><td align="center" valign="middle" >85 (25.60)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Worse</td><td align="center" valign="middle" >12 (17.65)</td><td align="center" valign="middle" >61 (18.60)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Much worse</td><td align="center" valign="middle" >21 (30.88)</td><td align="center" valign="middle" >69 (20.78)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Role function</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Limited</td><td align="center" valign="middle" >34 (50.00)</td><td align="center" valign="middle" >158 (47.59)</td><td align="center" valign="middle" >0.131</td><td align="center" valign="middle" >0.717</td></tr><tr><td align="center" valign="middle" >Not limited</td><td align="center" valign="middle" >34 (50.00)</td><td align="center" valign="middle" >174 (52.41)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >General Health</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Poor health</td><td align="center" valign="middle" >23 (33.82)</td><td align="center" valign="middle" >129 (38.86)</td><td align="center" valign="middle" >0.606</td><td align="center" valign="middle" >0.436</td></tr><tr><td align="center" valign="middle" >Good Health</td><td align="center" valign="middle" >45 (66.18)</td><td align="center" valign="middle" >203 (61.14)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Energy and Fatigue</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Have challenges during work</td><td align="center" valign="middle" >33 (48.53)</td><td align="center" valign="middle" >151 (45.48)</td><td align="center" valign="middle" >0.211</td><td align="center" valign="middle" >0.646</td></tr><tr><td align="center" valign="middle" >Have no challenge during work</td><td align="center" valign="middle" >35 (51.47)</td><td align="center" valign="middle" >181 (54.52)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Emotional Problem</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Moderately severe</td><td align="center" valign="middle" >12 (34.00)</td><td align="center" valign="middle" >86 (37.90)</td><td align="center" valign="middle" >9.299</td><td align="center" valign="middle" >0.054*</td></tr><tr><td align="center" valign="middle" >Not at all slightly</td><td align="center" valign="middle" >24 (52.00)</td><td align="center" valign="middle" >86 (37.90)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Very severe</td><td align="center" valign="middle" >7 (14.00)</td><td align="center" valign="middle" >62 (24.20)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Health Change</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Get worse</td><td align="center" valign="middle" >50 (73.53)</td><td align="center" valign="middle" >197 (59.34)</td><td align="center" valign="middle" >4.812</td><td align="center" valign="middle" >0.028</td></tr><tr><td align="center" valign="middle" >Excellent</td><td align="center" valign="middle" >18 (26.47)</td><td align="center" valign="middle" >135 (40.66)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Pain</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Pain affects work</td><td align="center" valign="middle" >43 (63.24)</td><td align="center" valign="middle" >195 (58.73</td><td align="center" valign="middle" >0.474</td><td align="center" valign="middle" >0.491</td></tr><tr><td align="center" valign="middle" >Pain does not affect work</td><td align="center" valign="middle" >25 (36.76)</td><td align="center" valign="middle" >137 (41.27)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Social activity affected</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Always</td><td align="center" valign="middle" >27 (39.71)</td><td align="center" valign="middle" >126 (37.95)</td><td align="center" valign="middle" >0.255</td><td align="center" valign="middle" >0.968</td></tr><tr><td align="center" valign="middle" >Most of the time</td><td align="center" valign="middle" >4 (5.88)</td><td align="center" valign="middle" >16 (4.82)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Sometime</td><td align="center" valign="middle" >3 (4.41)</td><td align="center" valign="middle" >16 (4.82)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >None of the time</td><td align="center" valign="middle" >34 (50.00)</td><td align="center" valign="middle" >174 (52.41)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap></sec><sec id="s4"><title>5. Discussion</title><p>The exploration of the relationship between multi-morbidity and the quality of life (QOL) among adults attending outpatient clinics in the Ashanti Region provides valuable insights into the complex interplay between health conditions and overall well-being. The detailed analysis of a 400-respondent sample offers nuanced findings across multiple dimensions of QOL, shedding light on both similarities and disparities between individuals with multi-morbidity and those without.</p><p>Firstly, the study reveals that there is no statistically significant association between multi-morbidity and perceived changes in current health compared to one year ago suggests that having multiple health conditions does not necessarily dictate a decline in self-reported health over time. This finding challenges assumptions about the inevitability of health deterioration in the context of multi-morbidity and underscores the importance of individual variations in health outcomes. However, earlier research has highlighted the difficulties associated with managing multimorbidity [<xref ref-type="bibr" rid="scirp.133157-ref8">8</xref>] . Evidence suggests that prevalent risk factors driving the escalating burden of multimorbidity include advanced age, obesity, sedentary lifestyle, socioeconomic disadvantage, and the consumption of tobacco and alcohol [<xref ref-type="bibr" rid="scirp.133157-ref21">21</xref>] . These findings imply that a significant proportion of the risk factors for multimorbidity are amenable to modification [<xref ref-type="bibr" rid="scirp.133157-ref6">6</xref>] .</p><p>Similarly, the absence of significant associations between multi-morbidity and variables such as role function, general health, and energy and fatigue implies that the impact of multiple health conditions on these aspects of QOL may be more nuanced or influenced by other factors beyond the presence of co-morbidities. Numerous studies have indicated that multimorbidity significantly diminishes quality of life (QoL) [<xref ref-type="bibr" rid="scirp.133157-ref38">38</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref39">39</xref>] [<xref ref-type="bibr" rid="scirp.133157-ref43">43</xref>] . While direct comparisons with prior research may be challenging due to methodological differences, the authors note a consistent finding: individuals grappling with multimorbidity exhibit markedly lower QoL compared to those managing a single chronic condition. These results highlight the complexity of assessing QOL in individuals with multi-morbidity and the need for comprehensive approaches that consider diverse factors contributing to well-being.</p><p>However, the study’s identification of a near-significant association between multi-morbidity and emotional problems suggests that individuals with multiple health conditions may be more vulnerable to experiencing psychological distress compared to their counterparts with fewer health issues. This finding underscores the importance of addressing mental health concerns alongside physical health management in the care of individuals with multi-morbidity.</p><p>Furthermore, the significant association between multi-morbidity and reported worsening health change underscores the substantial impact that co-morbidities can have on individuals’ perceived health trajectories. This finding underscores the urgency of proactive interventions aimed at mitigating the progression of health decline in individuals with multi-morbidity and promoting strategies for maintaining or improving overall well-being.</p><p>Although variables such as pain and social activity affected did not demonstrate significant associations with multi-morbidity in this study, their inclusion in the analysis provides valuable insights into additional dimensions of QOL that may be influenced by the presence of multiple health conditions.</p></sec><sec id="s5"><title>6. Conclusion</title><p>The study highlights the complexity of the relationship between multi-morbidity and QOL, emphasizing the need for tailored interventions that address both physical and psychological aspects of health in individuals with multiple health conditions. Having multiple health conditions does not inevitably lead to a decline in self-reported health over time, highlighting the importance of individual variations in health outcomes. the impact of multi-morbidity on different aspects of QOL may vary, emphasizing the need for comprehensive approaches to well-being assessment. This highlights the importance of addressing mental health concerns alongside physical health management in the care of individuals with multi-morbidity.</p></sec><sec id="s6"><title>7. Recommendation</title><p>This study’s inclusive analysis of the relationship between multi-morbidity and various dimensions of QOL highlights the need for holistic approaches to healthcare that address both physical and emotional aspects of well-being in individuals with multiple health conditions. By recognizing the diverse factors contributing to QOL outcomes in this population, healthcare providers can tailor interventions to meet the complex needs of individuals with multi-morbidity and improve their overall quality of life.</p></sec><sec id="s7"><title>Acknowledgments</title><p>The authors thank the management, research unit and outpatients department of Komfo Anokye Teaching Hospital for their contribution accepting the conduct of the study. The authors are also grateful to the outpatients (study participants) who were sampled for the study.</p></sec><sec id="s8"><title>Funding</title><p>This study was funded by the authors.</p></sec><sec id="s9"><title>Availability of Data and Materials</title><p>The data supporting this study’s findings are available from the corresponding author upon request.</p></sec><sec id="s10"><title>Authors’ Contributions</title><p>JA led the background development, fieldwork, and analysis of the paper. JKB was involved in analyses and report writing. All authors were involved in editing and proofreading the manuscript. IBK was the local collaborator at the study site. EBK final editing and typesetting</p></sec><sec id="s11"><title>Ethics Approval and Consent to Participate</title><p>The study was approved by the Research and Development Unit of Komfo Anokye Teaching Hospital, Kumasi-Ghana. Respondents were selected based on their consent. Again, all participants were provided with written informed consent to participate. The study followed all the ethical considerations about respondents’ selection, interview process, confidentiality, and data analysis protocols.</p></sec><sec id="s12"><title>Consent for Publication</title><p>All Authors’ have fully consented for this paper to be published.</p></sec><sec id="s13"><title>Conflicts of Interest</title><p>The authors declare that they have no conflict of interest.</p></sec><sec id="s14"><title>License</title><p>This article is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Public Domain.</p></sec><sec id="s15"><title>Cite this paper</title><p>Acquaye, J., Brenyah, J.K., Brobbey-Kyei, I.A. and Brobbey-Kyei, E. (2024) Association between Multimorbidity and Quality of Life among Adults Attending Outpatient Clinics in the Ashanti Region: A Cross-Sectional Study. Open Access Library Journal, 11: e11532. https://doi.org/10.4236/oalib.1111532</p></sec><sec id="s16"><title>List of Abbreviation</title><p>QoL Quality of Life</p><p>LMICs Low- and middle-income countries</p><p>SF Short form</p><p>PPO Partial Proportional Odds</p></sec></body><back><ref-list><title>References</title><ref id="scirp.133157-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">WHO (2016) World Health Statistics 2016: Monitoring Health for the SDGs, Sustainable Development Goals 2016.</mixed-citation></ref><ref id="scirp.133157-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Mercer, S., Salisbury, C. and Fortin, M. (2014) ABC of Multimorbidity. John Wiley &amp; Sons, Ltd., Hoboken.</mixed-citation></ref><ref id="scirp.133157-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Ho, I.S.-S., Azcoaga-Lorenzo, A., Akbari, A., Black, C., Davies, J., Hodgins, P., &lt;i&gt;et al&lt;/i&gt;. (2021) Examining Variation in the Measurement of Multimorbidity in Research: A Systematic Review of 566 Studies. &lt;i&gt;Lancet Public Health&lt;/i&gt;, 6, e587-e597. &lt;br&gt;https://doi.org/10.1016/S2468-2667(21)00107-9</mixed-citation></ref><ref id="scirp.133157-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Xu, X., Mishra, G.D. and Jones, M. (2017) Mapping the Global Research Landscape and Knowledge Gaps on Multimorbidity: A Bibliometric Study. &lt;i&gt;Journal of Global Health&lt;/i&gt;, 7, Article 010414. www.icmje.org/coi_disclosure.pdf&lt;br&gt;https://doi.org/10.7189/jogh.07.010414</mixed-citation></ref><ref id="scirp.133157-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Zemedikun, D.T., Gray, L.J., Khunti, K., Davies, M.J. and Dhalwani, N.N. (2018) Patterns of Multimorbidity in Middle-Aged and Older Adults: An Analysis of the UK Biobank Data. &lt;i&gt;Mayo Clinic Proceedings&lt;/i&gt;, 93, 857-866. &lt;br&gt;https://doi.org/10.1016/j.mayocp.2018.02.012</mixed-citation></ref><ref id="scirp.133157-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Abebe, F., Schneider, M., Asrat, B. and Ambaw, F. (2020) Multimorbidity of Chronic Non-Communicable Diseases in Low-and Middle-Income Countries: A Scoping Review. &lt;i&gt;Journal of Multimorbidity and Comorbidity&lt;/i&gt;, 10, 1-13. &lt;br&gt;https://doi.org/10.1177/2235042X20961919</mixed-citation></ref><ref id="scirp.133157-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Asogwa, O.A., Boateng, D., Marz&amp;#224;-Florensa, A., Peters, S., Levitt, N., Olmen, J.V., &lt;i&gt;et al&lt;/i&gt;. (2022) Multimorbidity of Non-Communicable Diseases in Low-Income and Middleincome Countries: A Systematic Review and Meta-Analysis. &lt;i&gt;BMJ Open&lt;/i&gt;, 12, e049133. &lt;br&gt;https://doi.org/10.1136/bmjopen-2021-049133</mixed-citation></ref><ref id="scirp.133157-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Bhagavathula, A.S., Gebreyohannes, E.A., Seid, M.A., Adane, A., Brkic, J. and Fialov&amp;#225;, D. (2021) Prevalence and Determinants of Multimorbidity, Polypharmacy, and Potentially Inappropriate Medication Use in the Older Outpatients: Findings from EuroAgeism H2020 ESR7 Project in Ethiopia. &lt;i&gt;Pharmaceuticals&lt;/i&gt;, 14, Article 844. &lt;br&gt;https://doi.org/10.3390/ph14090844</mixed-citation></ref><ref id="scirp.133157-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Eyowas, F.A., Schneider, M., Alemu, S., Pati, S. and Getahun, F.A. (2022) Magnitude, Pattern and Correlates of Multimorbidity among Patients Attending Chronic Outpatient Medical Care in Bahir Dar, Northwest Ethiopia: The Application of Latent Class Analysis Model. &lt;i&gt;PLOS ONE&lt;/i&gt;, 17, e0267208. &lt;br&gt;https://doi.org/10.1371/journal.pone.0267208</mixed-citation></ref><ref id="scirp.133157-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Calderon-Larranaga, A., Vetrano, D.L., Ferrucci, L., Mercer, S.W., Marengoni, A., Onder, G., &lt;i&gt;et al&lt;/i&gt;. (2018) Multimorbidity and Functional Impairment&amp;#8212;Bidirectional Interplay, Synergistic Effects and Common Pathways. &lt;i&gt;Journal of Internal Medicine&lt;/i&gt;, 285, 255-271. &lt;br&gt;https://doi.org/10.1111/joim.12843</mixed-citation></ref><ref id="scirp.133157-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Kingston, A., Robinson, L., Booth, H., Knapp, M. and Jagger, C. (2018) Projections of Multi-Morbidity in the Older Population in England to 2035: Estimates from the Population Ageing and Care Simulation (PACSim) Model. &lt;i&gt;Age and Ageing&lt;/i&gt;, 47, 374-380. &lt;br&gt;https://doi.org/10.1093/ageing/afx201</mixed-citation></ref><ref id="scirp.133157-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Mounce, L.T.A., Campbell, J.L., Henley, W.E., Tejerina Arreal, M.C., Porter, I. and Valderas, J.M. (2018) Predicting Incident Multimorbidity. &lt;i&gt;Annals of Family Med&lt;/i&gt;&lt;i&gt;i&lt;/i&gt;&lt;i&gt;cine&lt;/i&gt;, 16, 322-329. &lt;br&gt;https://doi.org/10.1370/afm.2271</mixed-citation></ref><ref id="scirp.133157-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Ornstein, S.M., Nietert, P.J., Jenkins, R.G. and Litvin, C.B. (2013) The Prevalence of Chronic Diseases and Multimorbidity in Primary Care Practice: A PPRNet Report. &lt;i&gt;Journal of the American Board of Family Medicine&lt;/i&gt;, 26, 518-524. &lt;br&gt;https://doi.org/10.3122/jabfm.2013.05.130012</mixed-citation></ref><ref id="scirp.133157-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Willadsen, T.G., Jarb&amp;#248;l, D.E., Reventlow, S., Mercer, S.W., &lt;i&gt;et al&lt;/i&gt;. (2018) Multimorbidity and Mortality: A 15-Year Longitudinal Registry-Based Nationwide Danish Population Study. &lt;i&gt;Journal of&lt;/i&gt;&lt;i&gt; &lt;/i&gt;&lt;i&gt;Comorbidity&lt;/i&gt;, 8, 1-9. &lt;br&gt;https://doi.org/10.1177/2235042X18804063</mixed-citation></ref><ref id="scirp.133157-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Barnett, K., Mercer, S.W., Norbury, M., Watt, G., Wyke, S. and Guthrie, B. (2012) Epidemiology of Multimorbidity and Implications for Health Care, Research, and Medical Education: A Cross-Sectional Study. &lt;i&gt;The Lancet&lt;/i&gt;, 380, 37-43. &lt;br&gt;https://doi.org/10.1016/S0140-6736(12)60240-2</mixed-citation></ref><ref id="scirp.133157-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Romano, E., Ma, R., Vancampfort, D., Firth, J., Felez-Nobrega, M., Haro, J.M., &lt;i&gt;et al&lt;/i&gt;. (2021) Multimorbidity and Obesity in Older Adults from Six Low-and Middle-Income Countries. &lt;i&gt;Preventive Medicine&lt;/i&gt;, 153, Article 106816. &lt;br&gt;https://doi.org/10.1016/j.ypmed.2021.106816</mixed-citation></ref><ref id="scirp.133157-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Violan, C., Foguet-Boreu, Q., Flores-Mateo, G., Salisbury, C., Blom, J., Freitag, M., &lt;i&gt;et al&lt;/i&gt;. (2014) Prevalence, Determinants and Patterns of Multimorbidity in Primary Care: A Systematic Review of Observational Studies. &lt;i&gt;PLOS ONE&lt;/i&gt;, 9, e102149. &lt;br&gt;https://doi.org/10.1371/journal.pone.0102149</mixed-citation></ref><ref id="scirp.133157-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Alimohammadian, M., Majidi, A., Yaseri, M., Ahmadi, B., Islami, F., Derakhshan, M., &lt;i&gt;et al&lt;/i&gt;. (2017) Multimorbidity as an Important Issue among Women: Results of a Gender Difference Investigation in a Large Population-Based Cross-Sectional Study in West Asia. &lt;i&gt;BMJ Open&lt;/i&gt;, 7, e013548. &lt;br&gt;https://doi.org/10.1136/bmjopen-2016-013548</mixed-citation></ref><ref id="scirp.133157-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">Skou, S.T., Mair, F.S., Fortin, M., Guthrie, B., Nunes, B.P., Miranda, J.J., &lt;i&gt;et al&lt;/i&gt;. (2022) Multimorbidity. &lt;i&gt;Nature Reviews Disease Primers&lt;/i&gt;, 8, Article No. 48. &lt;br&gt;https://doi.org/10.1038/s41572-022-00376-4</mixed-citation></ref><ref id="scirp.133157-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">AMS (2018) Multimorbidity: A Priority for Global Health Research.</mixed-citation></ref><ref id="scirp.133157-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">Freisling, H., Viallon, V., Lennon, H., Bagnardi, V., Ricci, C., Butterworth, A.S., &lt;i&gt;et al&lt;/i&gt;. (2020) Lifestyle Factors and Risk of Multimorbidity of Cancer and Cardiometabolic Diseases: Amultinational Cohort Study. &lt;i&gt;BMC Medicine&lt;/i&gt;, 18, Article No. 5. &lt;br&gt;https://doi.org/10.1186/s12916-019-1474-7</mixed-citation></ref><ref id="scirp.133157-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">van den Akker, M., Buntinx, F., Metsemakers, J.F.M., Roos, S. and Knottnerus, J.A. (1998) Multimorbidity in General Practice: Prevalence, Incidence, and Determinants of Co-Occurring Chronic and Recurrent Diseases. &lt;i&gt;Journal of Clinical Epid&lt;/i&gt;&lt;i&gt;e&lt;/i&gt;&lt;i&gt;miology&lt;/i&gt;, 51, 367-375. &lt;br&gt;https://doi.org/10.1016/S0895-4356(97)00306-5</mixed-citation></ref><ref id="scirp.133157-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">France, E.F., Wyke, S., Gunn, J.M., Mair, F.S., McLean, G. and Mercer, S.W. (2012) Multimorbidity in Primary Care: A Systematic Review of Prospective Cohort Studies. &lt;i&gt;The British Journal of General Practice&lt;/i&gt;, 62, e297-307. &lt;br&gt;https://doi.org/10.3399/bjgp12X636146</mixed-citation></ref><ref id="scirp.133157-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Marengoni, A., Angleman, S., Melis, R., Mangialasche, F., Karp, A. and Garmen, A., &lt;i&gt;et al&lt;/i&gt;. (2011) Aging with Multimorbidity: A Systematic Review of the Literature. &lt;i&gt;Ageing Research Reviews&lt;/i&gt;, 10, 430-439. &lt;br&gt;https://doi.org/10.1016/j.arr.2011.03.003</mixed-citation></ref><ref id="scirp.133157-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">Aiden, H. (2018) Multimorbidity. Understanding the Challenge. A Report for the Richmond Group of Charities. </mixed-citation></ref><ref id="scirp.133157-ref26"><label>26</label><mixed-citation publication-type="other" xlink:type="simple">Harrison, C., Henderson, J., Miller, G. and Britt, H. (2017) The Prevalence of Diagnosed Chronic Conditions and Multimorbidity in Australia: A Method for Estimating Population Prevalence from General Practice Patient Encounter Data. &lt;i&gt;PLOS ONE&lt;/i&gt;, 12, e0172935. &lt;br&gt;https://doi.org/10.1371/journal.pone.0172935</mixed-citation></ref><ref id="scirp.133157-ref27"><label>27</label><mixed-citation publication-type="other" xlink:type="simple">Hunter, M.L., Knuiman, M.W., Musk, B.A.W., Hui, J., Murray, K., Beilby, J.P., &lt;i&gt;et al&lt;/i&gt;. (2021) Prevalence and Patterns of Multimorbidity in Australian Baby Boomers: The Busselton Healthy Ageing Study. &lt;i&gt;BMC Public Health&lt;/i&gt;, 21, Article No. 1539. &lt;br&gt;https://doi.org/10.1186/s12889-021-11578-y</mixed-citation></ref><ref id="scirp.133157-ref28"><label>28</label><mixed-citation publication-type="other" xlink:type="simple">Hunger, M., Thorand, B., Schunk, M., Doring, A., Menn, P., Peters, A., &lt;i&gt;et al&lt;/i&gt;. (2011) Multimorbidity and Health-Related Quality of Life in the Older Population: Results from the German KORA-Age Study. &lt;i&gt;Health and Quality of Life Outcomes&lt;/i&gt;, 9, Article No. 53. &lt;br&gt;https://doi.org/10.1186/1477-7525-9-53</mixed-citation></ref><ref id="scirp.133157-ref29"><label>29</label><mixed-citation publication-type="other" xlink:type="simple">NICE (2016) Multimorbidity: Clinical Assessment and Management: Multimorbidity: Assessment, Prioritisation and Management of Care for People with Commonly Occurring Multimorbidity. NICE Guideline NG56: National Institute for Health and Care Excellence. </mixed-citation></ref><ref id="scirp.133157-ref30"><label>30</label><mixed-citation publication-type="other" xlink:type="simple">Leijten, F.R.M., Struckmann, V., van Ginneken, E., Czypionka, T., Kraus, M., Reiss, M., &lt;i&gt;et al&lt;/i&gt;. (2018) The SELFIE Framework for Integrated Care for Multi-Morbidity: Development and Description. &lt;i&gt;Health Policy&lt;/i&gt;, 122, 12-22. &lt;br&gt;https://doi.org/10.1016/j.healthpol.2017.06.002</mixed-citation></ref><ref id="scirp.133157-ref31"><label>31</label><mixed-citation publication-type="other" xlink:type="simple">The Richmond Group of Charities (2018) &amp;#8220;Just One Thing after Another&amp;#8221;: Living with Multiple Conditions: A Report from the Taskforce on Multiple Conditions. </mixed-citation></ref><ref id="scirp.133157-ref32"><label>32</label><mixed-citation publication-type="other" xlink:type="simple">Bayliss, E.A., Bonds, D.E., Boyd, C.M., Davis, M.M., Finke, B., Fox, M.H., &lt;i&gt;et al&lt;/i&gt;. (2014) Understanding the Context of Health for Persons with Multiple Chronic Conditions: Moving from What Is the Matter to What Matters. &lt;i&gt;Annals of Family Medicine&lt;/i&gt;, 12, 260-269. &lt;br&gt;https://doi.org/10.1370/afm.1643</mixed-citation></ref><ref id="scirp.133157-ref33"><label>33</label><mixed-citation publication-type="other" xlink:type="simple">Guthrie, B., Payne, K., Alderson, P., McMurdo, M.E.T. and Mercer, S.W. (2012) Adapting Clinical Guidelines to Take Account of Multimorbidity. &lt;i&gt;BMJ&lt;/i&gt;, 345, e6341. &lt;br&gt;https://doi.org/10.1136/bmj.e6341</mixed-citation></ref><ref id="scirp.133157-ref34"><label>34</label><mixed-citation publication-type="other" xlink:type="simple">Young, C.E., Boyle, F.M. and Mutch, A.J. (2016) Are Care Plans Suitable for the Management of Multiple Conditions? &lt;i&gt;Journal of Comorbidity&lt;/i&gt;, 6, 103-113. &lt;br&gt;https://doi.org/10.15256/joc.2016.6.79</mixed-citation></ref><ref id="scirp.133157-ref35"><label>35</label><mixed-citation publication-type="other" xlink:type="simple">Salisbury, C., Man, M.S., Bower, P., Guthrie, B., Chaplin, K., Gaunt, D.M., &lt;i&gt;et al&lt;/i&gt;. (2018) Management of Multimorbidity Using a Patient-Centred Care Model: A Pragmatic Cluster-Randomised Trial of the 3D Approach. &lt;i&gt;The &lt;/i&gt;&lt;i&gt;Lancet&lt;/i&gt;, 392, 41-50. &lt;br&gt;https://doi.org/10.1016/S0140-6736(18)31308-4</mixed-citation></ref><ref id="scirp.133157-ref36"><label>36</label><mixed-citation publication-type="other" xlink:type="simple">Ailabouni, N.J., Hilmer, S.N., Kalisch, L., Braund, R. and Reeve, E. (2020) COVID-19 Pandemic: Considerations for Safe Medication Use in Older Adults with Multimorbidity and Polypharmacy. &lt;i&gt;Journals of Gerontology Series A&lt;/i&gt;:&lt;i&gt; Biological Sciences&lt;/i&gt;&lt;i&gt; &lt;/i&gt;&lt;i&gt;and Medical Sciences&lt;/i&gt;, 76, 1068-1073. &lt;br&gt;https://doi.org/10.1093/gerona/glaa104</mixed-citation></ref><ref id="scirp.133157-ref37"><label>37</label><mixed-citation publication-type="other" xlink:type="simple">Guan, W.-J., Liang, W.-H., Zhao, Y., Liang, H.-R., Chen, Z.-S., Li, Y.-M., &lt;i&gt;et al&lt;/i&gt;. (2020) Comorbidity and Its Impact on 1590 Patients with COVID-19 in China: A Nationwide Analysis. &lt;i&gt;European Respiratory Journal&lt;/i&gt;, 55, Article 2001227. &lt;br&gt;https://doi.org/10.1183/13993003.01227-2020</mixed-citation></ref><ref id="scirp.133157-ref38"><label>38</label><mixed-citation publication-type="other" xlink:type="simple">Fortin, M., Dubois, M.F., Hudon, C., Soubhi, H. and Almirall, J. (2007) Multimorbidity and Quality of Life: A Closer Look. &lt;i&gt;Health and Quality of Life Outcomes&lt;/i&gt;, 5, Article No. 52. &lt;br&gt;https://doi.org/10.1186/1477-7525-5-52</mixed-citation></ref><ref id="scirp.133157-ref39"><label>39</label><mixed-citation publication-type="other" xlink:type="simple">Fortin, M., Lapointe, L., Hudon, C., Vanasse, A., Ntetu, A.L. and Maltais, D. (2004) Multimorbidity and Quality of Life in Primary Care: A Systematic Review. &lt;i&gt;Health and Quality of Life Outcomes&lt;/i&gt;, 2, Article No. 51. &lt;br&gt;https://doi.org/10.1186/1477-7525-2-51</mixed-citation></ref><ref id="scirp.133157-ref40"><label>40</label><mixed-citation publication-type="other" xlink:type="simple">Makovski, T.T., Schmitz, S., Zeegers, M.P., Stranges, S. and van den Akker, M. (2019) Multimorbidity and Quality of Life: Systematic Literature Review and Meta-Analysis. &lt;i&gt;Ageing Research Reviews&lt;/i&gt;, 53, Article 100903. &lt;br&gt;https://doi.org/10.1016/j.arr.2019.04.005</mixed-citation></ref><ref id="scirp.133157-ref41"><label>41</label><mixed-citation publication-type="other" xlink:type="simple">Hagell, P., Westergren, A. and &amp;#197;restedt, K. (2017) Beware of the Origin of Numbers: Standard Scoring of the SF-12 and SF-36 Summary Measures Distorts Measurement and Score Interpretations. &lt;i&gt;Research in Nursing &amp; Health&lt;/i&gt;, 40, 378-386. &lt;br&gt;https://doi.org/10.1002/nur.21806</mixed-citation></ref><ref id="scirp.133157-ref42"><label>42</label><mixed-citation publication-type="other" xlink:type="simple">Lall, R., Campbell, M.J., Walters, S.J. and Morgan, K. (2002) A Review of Ordinal Regression Models Applied on Health-Related Quality of Life Assessments. &lt;i&gt;Statist&lt;/i&gt;&lt;i&gt;i&lt;/i&gt;&lt;i&gt;cal&lt;/i&gt;&lt;i&gt; &lt;/i&gt;&lt;i&gt;Methods in Medical Research&lt;/i&gt;, 11, 49-67. &lt;br&gt;https://doi.org/10.1191/0962280202sm271ra</mixed-citation></ref><ref id="scirp.133157-ref43"><label>43</label><mixed-citation publication-type="other" xlink:type="simple">Williams, R. (2016) Understanding and Interpreting Generalized Ordered Logit Models. &lt;i&gt;The Journal of Mathematical Sociology&lt;/i&gt;, 40, 7-20. &lt;br&gt;https://doi.org/10.1080/0022250X.2015.1112384</mixed-citation></ref><ref id="scirp.133157-ref44"><label>44</label><mixed-citation publication-type="other" xlink:type="simple">Peterson, B., Frank, E. and Harrell, J. (1990) Partial Proportional Odds Models for Ordinal Response Variables. &lt;i&gt;Journal of the Royal Statistical Society Series C&lt;/i&gt;, 39, 205-217. &lt;br&gt;https://doi.org/10.2307/2347760</mixed-citation></ref><ref id="scirp.133157-ref45"><label>45</label><mixed-citation publication-type="other" xlink:type="simple">Abreu, M.N.S., Siqueira, A.L., Cardoso, C.S. and Caiaffa, W.T. (2008) Ordinal Logistic Regression Models: Application in Quality of Life Studies. &lt;i&gt;Cad Sa&amp;#250;de P&amp;#250;blica&lt;/i&gt;,&lt;i&gt; &lt;/i&gt;24, S581-S91. &lt;br&gt;https://doi.org/10.1590/S0102-311X2008001600010</mixed-citation></ref><ref id="scirp.133157-ref46"><label>46</label><mixed-citation publication-type="other" xlink:type="simple">Austad, B., Hetlevik, I., Mjolstad, B.P. and Helvik, A.S. (2016) Applying Clinical Guidelines in General Practice: A Qualitative Study of Potential Complications. &lt;i&gt;BMC Family Practice&lt;/i&gt;, 17, Article No. 92. &lt;br&gt;https://doi.org/10.1186/s12875-016-0490-3</mixed-citation></ref><ref id="scirp.133157-ref47"><label>47</label><mixed-citation publication-type="other" xlink:type="simple">Turner, A., Mulla, A., Booth, A., Aldridge, S., Stevens, S., Begum, M., &lt;i&gt;et al&lt;/i&gt;. (2018) The International Knowledge Base for New Care Models Relevant to Primary Care-Led Integrated Models: A Realist Synthesis. &lt;i&gt;Health Services and Delivery R&lt;/i&gt;&lt;i&gt;e&lt;/i&gt;&lt;i&gt;search&lt;/i&gt;, 6. &lt;br&gt;https://doi.org/10.3310/hsdr06250</mixed-citation></ref></ref-list></back></article>