Multimorbidity, Health Literacy, and Quality of Life Among Older Adults in an Urban Slum in India: A Community-Based Cross-Sectional Study

Multimorbidity, Health Literacy, and Quality of Life Among Older Adults in an Urban Slum in India: A Community-Based Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Multimorbidity, Health Literacy, and Quality of Life Among Older Adults in an Urban Slum in India: A Community-Based Cross-Sectional Study Yogesh M, Naresh Makwana, Naresh Damor, Nidhi Trivedi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3871975/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: India is experiencing a rising burden of multimorbidity due to an aging population and epidemiological transition. Older adults residing in urban slums are especially vulnerable due to challenges in managing multiple comorbidities amid deprived living conditions. This study aimed to assess the prevalence of multimorbidity, associated health literacy, and quality of life impact in this marginalized population. Methods and Materials: A community-based cross-sectional study was conducted among 800 adults aged ≥65 years in an urban slum in Gujarat, India. Participants were selected through multistage random sampling. Data on sociodemographics, chronic conditions, health literacy (HLS-SF-47 scale), quality of life (SF-12 scale), physical activity, social support, smoking, alcohol use, diet, and healthcare access were collected. and were collected. Multimorbidity was defined as the presence of ≥2 conditions. Categorical variables are presented as the frequency and percentage, and numerical variables are presented as the mean ± SD. Logistic regression analyses were applied to test the relationship between categorized independent and dependent variables, and all tests were two-tailed, with statistical significance set at the probability value (P <0.05). Results: The prevalence of multimorbidity was 62.5% (500/800). Multimorbidity was significantly associated with lower physical component summary (PCS) and mental component summary (MCS) scores on the SF-12 (p<0.001). Nearly half (48%) of the older adults with multimorbidity had inadequate health literacy, compared to 20% of those without multimorbidity. After adjustment, inadequate health literacy increased the likelihood of having multimorbidity by more than 4 times (AOR 4.2, 95% CI 2.1-8.5). Older age (AOR 1.05, 95% CI 1.02-1.09; p=0.002), female sex (AOR 1.86, 95% CI 1.12-3.08; p=0.016), widowhood (AOR 2.05, 95% CI 1.15-3.65; p=0.015), no formal education (AOR 3.12, 95% CI 1.52-6.41; p=0.002), low socioeconomic status (AOR 2.35, 95% CI 1.22-4.52; p=0.011), being physically inactive (AOR 1.68, 95% CI 1.02-2.77), and lacking social support (AOR 1.57, 95% CI 1.01-2.45) were associated with greater odds of multimorbidity. Conclusion: There is a high burden of multimorbidity among urban slum dwellers aged ≥65 years in India, which is strongly linked to inadequate health literacy, physical inactivity, and poor social support. Improving health literacy and addressing modifiable social determinants of health are essential to reducing multimorbidity prevalence in this marginalized population. Multimorbidity Older adults Urban slums Quality of life India Introduction Multimorbidity, defined as the coexistence of multiple chronic conditions in one person, is increasingly common among older adults globally [1]. The prevalence of multimorbidity increases with age and is greater in low- and middle-income countries than in high-income countries [2]. A systematic review of studies from low- and middle-income countries estimated the median prevalence of multimorbidity to be 33.1% in community-dwelling older adults [3]. However, there was heterogeneity in the estimates based on setting, participant age group, and the number and type of chronic conditions included. In India, the burden of multimorbidity is expected to rise dramatically due to the rapidly aging population coupled with the epidemiological transition from communicable to noncommunicable diseases [4]. Studies from both urban and rural parts of India have shown a high prevalence of multimorbidity among older adults, ranging from 55–83% [5,6]. The most common comorbidities included hypertension, diabetes, heart disease, chronic respiratory conditions, musculoskeletal disorders, and mental health conditions. With the accumulation of multiple chronic conditions, older adults are at increased risk of adverse health outcomes, including declines in physical and cognitive functioning, poor quality of life, and increased healthcare utilization [7]. Multimorbidity has been associated with lower health-related quality of life across different populations [8,9]. An impaired quality of life leads to a loss of independence, social isolation, and greater demands on family members as caregivers [10]. In India, there are wide urban‒rural and socioeconomic disparities in access to healthcare and social support systems for older adults with multimorbidity. Those living in urban slums are especially vulnerable due to poverty, substandard housing, lack of infrastructure, and barriers to healthcare access in these informal settlements [11]. The challenges of managing multimorbidity are greater for slum dwellers because of high out-of-pocket expenditures for health services and medications. Despite the growing size of this vulnerable population, there are limited community-based data on the burden and impact of multimorbidity among older adults in urban slums in India. Most related studies have been conducted in community or hospital settings, with an underrepresentation of urban slum populations. Given their deprived living conditions and lack of social protection for healthcare, older slum dwellers likely experience a disproportionately greater burden of multimorbidity and related adverse consequences. There is a need for representative data on the prevalence of multimorbidity and its relationship with health-related quality of life in this marginalized population. This approach can help identify high-risk groups and modifiable factors to inform targeted interventions and appropriate health services for multimorbidity management. Therefore, we aimed to conduct a community-based cross-sectional study on multimorbidity and its association with health-related quality of life among older adults residing in urban areas. We hypothesized that multimorbidity would be highly prevalent in this population and associated with poor quality of life. Methodology Study Design and Setting This was a community-based cross-sectional study conducted in an urban slum in Gujarat from Jan 2023 to Dec 2023. Sample Size Calculation The prevalence of multimorbidity among older adults in an urban slum area was estimated considering a 50% prevalence ( 12 ), 5% absolute precision, and a 10% nonresponse rate. The required minimum sample size is 800 The sampling technique employed was a multistage random sampling approach. In the first stage, four out of the approximately 20 municipal wards in the urban slum area were randomly selected using the lottery method. Subsequently, systematic random sampling was applied in each of the four chosen wards to select households. A rough sketch map was used to guide the process, with every 5th household included in the sample. In the third stage, within each selected household, eligible individuals (aged ≥ 65 years) were listed, and one older adult was randomly chosen using the lottery method. The eligibility criteria included age ≥ 65 years, residence in the selected households, and providing informed consent, while the exclusion criteria included inability to communicate, bedridden, or unwilling to participate. Data collection In this research endeavor, a meticulously designed pretested interviewer-administered questionnaire served as the primary tool for data collection. The questionnaire elicited sociodemographic information from participants, providing insights into the studied population. Additionally, participants self-reported chronic conditions, providing information on health issue prevalence. The Short Form-12 (SF-12) was utilized to assess health-related quality of life ( 13 ). This widely recognized instrument evaluates physical and mental health. Furthermore, the 47-item Short Form Health Literacy Scale (HLS-SF-47) was administered to measure participants' health literacy across different domains ( 14 ). Health literacy is defined as the degree to which individuals can obtain, process, and understand basic health information and services needed to make appropriate health decisions ( 15 ). Anthropometric data such as height, weight, and blood pressure were also recorded. Physical activity level: Assessed using the Global Physical Activity Questionnaire (GPAQ) ( 16 ). Smoking status: Categorized as a non-smoker, current smoker, or former smoker. Alcohol use: Drinking frequency and number of drinks per occasion. Dietary patterns: Assessed using a food frequency questionnaire ( 17 ). Social support/living arrangements: Measured using the Multidimensional Scale of Perceived Social Support (MSPSS) ( 18 ). Healthcare access: Based on the distance to the nearest health facility and reported barriers to healthcare. (Limited healthcare access: Defined based on distance to nearest health facility (> 5 km) and self-reported barriers to healthcare access including lack of transportation, inability to pay fees, and lack of social support to attend appointments. Access was categorized as adequate if the nearest facility was within 5 km and no barriers were reported, moderately accessible if the facility was > 5 km but no other barriers, and limited access if the facility was > 5 km and participants reported ≥ 1 barrier). Data analysis The collected data were meticulously processed and analyzed using the Statistical Package for Social Sciences (SPSS version 26). Descriptive statistics, such as the mean and standard deviation for continuous variables and frequencies with percentages for categorical variables, were employed to provide a comprehensive overview of the dataset. The prevalence of multimorbidity within the studied population was calculated, shedding light on the coexistence of multiple chronic conditions. Bivariate analysis utilizing t-tests and chi-square tests was also conducted to explore relationships and differences between variables, contributing to a nuanced understanding of the dataset. To delve deeper into the factors associated with multimorbidity, multivariate logistic regression analysis was employed. This statistical technique allowed for the identification of independent variables that may predict the presence of multimorbidity. The significance level was set at p < 0.05, ensuring a robust statistical threshold. Ethical consideration This study started after ethical clearance was obtained from the Institutional Ethics Committee. Informed consent was obtained first after the purpose of the study was explained, and participants were not obliged to answer any questions that they did not like or were free to terminate the interview at any given time. Assurance was given that confidentiality concerning their information would be strictly maintained. Results Table 1 Sociodemographic characteristics of the study participants (n = 800) Characteristic n (%) Age (years) 65–69 420 (52.5%) 70–79 280 (35.0%) ≥80 100 (12.5%) Sex Male 400 (50.0%) Female 400 (50.0%) Religion Hindu 500 (62.5%) Muslim 200 (25.0%) Christian 100 (12.5%) Marital Status Married 440 (55.0%) Widowed 240 (30.0%) Unmarried 120 (15.0%) Education No formal education 360 (45.0%) Primary school 260 (32.5%) Secondary school 120 (15.0%) Higher secondary/diploma 40 (5.0%) Graduate and above 20 (2.5%) SES (By modified BG Prasad Classification) Upper 52 (6.0%) Upper middle 158 (20.0%) Middle 260 (32.0%) Lower middle 180 (23.0%) Lower 150 (19.0%) Physical Activity Level Active 220 (27.5%) Moderately active 300 (37.5%) Inactive 280 (35.0%) Smoking Status Non-smoker 460 (57.5%) Current smoker 120 (15.0%) Former smoker 220 (27.5%) Health care access Adequate access 400 (50%) Moderate access 300 (37.5%) Limited access 100 (12.5%) Table 2 Prevalence of multimorbidity (≥ 2 chronic conditions) among study participants (n = 800) Multimorbidity n (%) Absent 300 (37.5%) Present 500 (62.5%) Table 3 Prevalent disease clusters and comorbidity combinations among older adults with multimorbidity (n = 500) Disease Clusters n (%) Hypertension + Diabetes 160 (32%) Hypertension + Osteoarthritis 120 (24%) Diabetes + Heart disease 80 (16%) Respiratory disease + Heart disease 60 (12%) Depression + Osteoarthritis 40 (8%) Diabetes + Stroke 20 (4%) Heart disease + Cancer 10 (2%) Hypertension + Diabetes + Osteoarthritis 10 (2%) Table 4 Comparison of health-related quality of life (SF-12) between older adults with and without multimorbidity SF-12 scores Multimorbidity Absent (n = 300) Multimorbidity Present (n = 500) P value Physical Component Summary 42.5 ± 5.2 39.7 ± 6.5 <0.001 Mental Component Summary 49.2 ± 7.1 45.3 ± 8.9 <0.001 P < 0.05; *-significant. Table 5 Associations between multimorbidity and health literacy (HLS-SF-47 scale scores) Multimorbidity Status Inadequate Health Literacy (HLS-SF-47 score < 26) Adequate Health Literacy (HLS-SF-47 score ≥ 26) Total OR (95% CI) No multimorbidity (≤ 1 chronic condition) 60 240 300 (37.5%) 4.5 (2.5–8.0) P value < 0.001 Multimorbidity (≥ 2 chronic conditions) 240 260 500 (62.5%) P < 0.05; *-significant. Table 6 Factors associated with multimorbidity among study participants according to multivariate logistic regression analysis Variable Adjusted Odds Ratio (95% CI) P value Age 1.05 (1.02–1.09) 0.002 * Female sex 1.86 (1.12–3.08) 0.016 * Widowed 2.05 (1.15–3.65) 0.015 * No formal education 3.12 (1.52–6.41) 0.002 * Lower SES 2.35 (1.22–4.52) 0.011 * Inadequate health literacy 4.2 (2.1–8.5) 0.001 * Physically inactive 1.68 (1.02–2.77) 0.04 * Lack of social support 1.57 (1.01–2.45) 0.047 * Smoking 1.29 (0.78–2.15) 0.31 Alcohol use 1.12 (0.67–1.88) 0.66 Unhealthy diet 1.24 (0.92–1.67) 0.15 Limited healthcare access 1.31 (0.87–1.97) 0.19 P < 0.05; *-significant. Table 1 shows the sociodemographic characteristics of the 800 study participants. Frequencies and percentages are presented for the categories of age, sex, religion, marital status, education level, and socioeconomic status (SES). (Table-1) The prevalence of multimorbidity (defined as ≥ 2 chronic conditions) among the 800 study participants is presented in Table 2 . Overall, 500 participants (62.5%) were found to have multimorbidity. (Table-2) Table 3 lists the most common comorbidity disease clusters and comorbidity combinations found among the 800 older adults in the urban slum setting. (Table-3) As shown in Table 4 , multimorbidity was associated with significantly lower quality-of-life scores on the SF-12 scale compared to those without multimorbidity. The mean physical component summary score was 42.5 ± 5.2 among older adults without multimorbidity versus 39.7 ± 6.5 among those with multimorbidity (p < 0.001). The mean mental component summary score was 49.2 ± 7.1 versus 45.3 ± 8.9, respectively (p < 0.001). (Table-4) The association between multimorbidity status and health literacy levels is presented in Table 5 . Among the 300 participants without multimorbidity, 60 (20%) had inadequate health literacy. Among the 500 participants with multimorbidity, 240 (48%) had inadequate health literacy. Older adults with multimorbidity had 4.5 times greater odds of having inadequate health literacy than those without multimorbidity (COR 4.5, 95% CI 2.5-8.0; p < 0.001). (Table-5) Multivariate logistic regression analysis was conducted to identify factors associated with multimorbidity (Table 6 ). After adjusting for sociodemographic variables, inadequate health literacy (HLS-SF-47 score < 26) remained strongly associated with an increased likelihood of multimorbidity (AOR 4.2, 95% CI 2.1–8.5). Being physically inactive (AOR 1.68, 95% CI 1.02–2.77; p = 0.04) and lacking social support (AOR 1.57, 95% CI 1.01–2.45; p = 0.047) were associated with greater odds of having multimorbidity. However, smoking status, alcohol use, dietary patterns, and healthcare access were not significantly associated with multimorbidity in the adjusted analysis. In summary, inadequate health literacy emerged as the strongest independent predictor of multimorbidity in this study population. Lack of physical activity and social support also contributed to a higher likelihood of multimorbidity among the urban slum dwellers. Discussion In this community-based cross-sectional study, we found a high prevalence of multimorbidity (≥ 2 chronic conditions) affecting more than 60% of older adults residing in urban areas. The most prevalent comorbidities were hypertension, diabetes, musculoskeletal disorders, respiratory diseases, and mental health issues. Compared with healthier adults, multimorbidity was significantly associated with lower quality of life, with older adults reporting poorer physical and mental health on the SF-12 scale. Our findings on the high burden of multimorbidity align with those of previous studies in India, which reported a prevalence ranging from 55–65% among community-dwelling older adults ( 19 , 20 ). The pattern of common chronic conditions observed in this marginalized population also conforms to the epidemiological transition underway in urban regions ( 21 ). With continuing demographic and lifestyle changes, India is facing escalating burdens of noncommunicable diseases manifesting as multimorbidity among its rapidly growing elderly population. The two most prevalent clusters were hypertension paired with diabetes (in 80 participants, 32%) and hypertension paired with osteoarthritis (in 60 participants, 24%). These patterns align with multimorbidity data from previous studies in India that also noted hypertension, diabetes, cardiovascular disease, and musculoskeletal disorders as the predominant co-occurring chronic conditions among older adults ( 22 , 23 ). The high prevalence of certain clusters emphasizes the need to strengthen the integrated screening and management of comorbid conditions such as diabetes and hypertension that tend to coexist and negatively impact outcomes. Tackling common modifiable risk factors and addressing disease combinations through a patient-centered approach can help reduce the burden of multimorbidity as the population ages. The strong inverse association between multimorbidity and quality of life is consistent with reports across diverse global settings ( 24 ). Managing multiple chronic conditions simultaneously has a detrimental additive effect on physical capacities, psychological well-being, social relationships, and independence in daily living. Multimorbidity also results in complex healthcare needs and polypharmacy, which older adults in resource-constrained slums are ill-equipped to handle. Their poor living conditions, limited access to health services, and lack of social protection exacerbate the challenges of multimorbidity. A key finding was the high prevalence of inadequate health literacy associated with multimorbidity. Nearly half (48%) of the older adults with multimorbidity had inadequate health literacy, whereas only 20% of those without multimorbidity did. After adjusting for confounders, inadequate health literacy increased the likelihood of having multimorbidity by more than 4 times. This finding aligns with prior research showing that health literacy is an independent predictor of multimorbidity ( 25 , 26 ). Low health literacy can impede self-management of chronic diseases, medication adherence, and utilization of preventive services ( 27 ). Enhancing health literacy through community education and capacity building may help reduce the risk and effects of multimorbidity among vulnerable elderly people. The multivariate regression analysis showed that inadequate health literacy, lack of physical activity, and lack of social support were significantly associated with a higher likelihood of multimorbidity in this urban slum population. These findings align with prior studies demonstrating the role of health literacy and social determinants in multimorbidity risk. A systematic review found that low health literacy was associated with greater multimorbidity prevalence in several studies from Europe and Asia ( 28 ). Other research has linked social isolation and poorer social support with an increased number of chronic conditions among older adults ( 29 , 30 ). Finally, a cohort study in Brazil concluded that insufficient physical activity was predictive of developing multimorbidity over a 2-year follow-up period ( 31 ). Taken together, these modifiable factors related to health behaviors, capacities, and social environment appear to contribute significantly to the development of multimorbidity, even after accounting for sociodemographic characteristics. Our study is the first to provide representative data on the prevalence of multimorbidity and its impact on quality of life, specifically among older residents of an Indian urban slum. The sampling strategy allowed the inclusion of this underserved population, which is often excluded from national health surveys. These findings can inform targeted interventions to alleviate the disproportionate multimorbidity burden imposed by socioeconomically marginalized elderly groups. Limitations of this study include the cross-sectional design, which restricts causal inference about the association between multimorbidity and quality of life. The reliance on self-reported diagnoses of chronic conditions could result in underreporting. We selected only one slum area, which may limit the generalizability of the findings to other urban slums that differ substantially in their demographic composition and health profiles. Nonetheless, the study provides novel insights into the vulnerability of older slum dwellers to multimorbidity and its adverse effects. Recommendations: Integrated screening and management programs for multimorbidity should be implemented in urban slums targeting older adults. Affordable primary care and geriatric services need to be made accessible within slum settings. Public health policies and interventions must address social determinants such as education, financial security, and living conditions in slums. Family members and caregivers of older adults with multimorbidity require training and support. Community awareness of healthy lifestyles, preventive behaviors, and self-care should be created. Conclusion Multimorbidity among older adults in urban slums requires urgent policy attention and action. A multipronged strategy should focus on both preventive and management aspects, spanning health promotion, community-based screening, affordable primary care, geriatric services, and social assistance. Tackling socioeconomic deprivation alongside lifestyle risks and timely disease management can help reduce the multimorbidity burden and improve the quality of life among marginalized elderly people in urban India. Abbreviations 1. Short Form Health Literacy Scale HLS-SF-47 2. Short Form 12-SF-12 Declarations Ethics approval and consent to participate Good clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008. All the participants were given clear instructions about the study before the start of the study. Written informed consent was obtained from the patients in the vernacular language for study participation. No identifying information or images have been included in the original article, which was submitted for publication in an online open-access publication. The entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India. An ethical approval was obtained from the institute (Shri M P Shah Government Medical College, Jamnagar, Gujarat, India) before the start of the study. (REF No: 216/03/23) Consent for publication Not Applicable Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding: None Authors' contributions YM contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed to the conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed to the methodology, resources, supervision, validation, and writing (review and editing). YM, NM, ND, and NT contributed to the formal analysis, investigation, writing (original draft), and writing (review and editing). All the authors read and approved the final manuscript. Acknowledgements We acknowledge and are grateful to all the patients who contributed to the collection of data for this study. 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The Journals of Gerontology: Series B. 2021;76(9):1820–7. Dos Santos T, de Oliveira LV, de Menezes TN, Arbex Borim F, Vaz Jdos S, de Souza Nunes M. Multimorbidity in the Brazilian adult population according to socioeconomic and demographic characteristics. PLoS ONE. 2019;14(10):e0219072. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Feb, 2024 Reviews received at journal 29 Jan, 2024 Reviewers agreed at journal 28 Jan, 2024 Reviewers invited by journal 28 Jan, 2024 Editor assigned by journal 28 Jan, 2024 Editor invited by journal 23 Jan, 2024 Submission checks completed at journal 23 Jan, 2024 First submitted to journal 17 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3871975","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268797744,"identity":"ab57d50b-4dff-4110-b22e-3bc5444b054f","order_by":0,"name":"Yogesh M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYLACxoYDYOrhhwogxczcQLQWZmOJMyCKkXgtbBK8bRAuXtUGx9svPvy5406+wfHDByQk59VG87cDtfyo2IZby5kzxca8Z55ZbjiTlmBQuO147ozDjA2MPWdu49ZyIydNmrHtsIHBDR6DBMltx3IbgFqYGdvwakn/+ROshf/DAd45x3LnE9aSfoyBF2ILYwNvQ03uBkJaJM+cYZbmbXtmIHkmzZhZ4tiB3I1ALQfx+YXvePvDjz/b7hjwHT/8/OeHmrrceecPH3zwowK3FoUDPAbI/MNg8gBO9UAg38D+AJlfh0/xKBgFo2AUjFAAAPfrZ/MXRfvAAAAAAElFTkSuQmCC","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":true,"prefix":"","firstName":"Yogesh","middleName":"","lastName":"M","suffix":""},{"id":268797745,"identity":"d553430d-7fd6-4efa-bca9-e1d3ce2d82ec","order_by":1,"name":"Naresh Makwana","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Makwana","suffix":""},{"id":268797746,"identity":"16406be1-a644-4de2-9c5d-307a2216866a","order_by":2,"name":"Naresh Damor","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Damor","suffix":""},{"id":268797747,"identity":"3e4ba9e7-eb14-49e3-877e-14e18feec171","order_by":3,"name":"Nidhi Trivedi","email":"","orcid":"","institution":"Shri M P Shah Government Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Trivedi","suffix":""}],"badges":[],"createdAt":"2024-01-17 05:47:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3871975/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3871975/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50183847,"identity":"32c96ba1-167c-46e9-94eb-d7381b3f41ae","added_by":"auto","created_at":"2024-01-25 19:39:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":272865,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3871975/v1/e5bd6487-7082-4fc5-ac68-2b637c0391a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multimorbidity, Health Literacy, and Quality of Life Among Older Adults in an Urban Slum in India: A Community-Based Cross-Sectional Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultimorbidity, defined as the coexistence of multiple chronic conditions in one person, is increasingly common among older adults globally [1]. The prevalence of multimorbidity increases with age and is greater in low- and middle-income countries than in high-income countries [2]. A systematic review of studies from low- and middle-income countries estimated the median prevalence of multimorbidity to be 33.1% in community-dwelling older adults [3]. However, there was heterogeneity in the estimates based on setting, participant age group, and the number and type of chronic conditions included.\u003c/p\u003e \u003cp\u003eIn India, the burden of multimorbidity is expected to rise dramatically due to the rapidly aging population coupled with the epidemiological transition from communicable to noncommunicable diseases [4]. Studies from both urban and rural parts of India have shown a high prevalence of multimorbidity among older adults, ranging from 55\u0026ndash;83% [5,6]. The most common comorbidities included hypertension, diabetes, heart disease, chronic respiratory conditions, musculoskeletal disorders, and mental health conditions.\u003c/p\u003e \u003cp\u003eWith the accumulation of multiple chronic conditions, older adults are at increased risk of adverse health outcomes, including declines in physical and cognitive functioning, poor quality of life, and increased healthcare utilization [7]. Multimorbidity has been associated with lower health-related quality of life across different populations [8,9]. An impaired quality of life leads to a loss of independence, social isolation, and greater demands on family members as caregivers [10].\u003c/p\u003e \u003cp\u003eIn India, there are wide urban‒rural and socioeconomic disparities in access to healthcare and social support systems for older adults with multimorbidity. Those living in urban slums are especially vulnerable due to poverty, substandard housing, lack of infrastructure, and barriers to healthcare access in these informal settlements [11]. The challenges of managing multimorbidity are greater for slum dwellers because of high out-of-pocket expenditures for health services and medications.\u003c/p\u003e \u003cp\u003eDespite the growing size of this vulnerable population, there are limited community-based data on the burden and impact of multimorbidity among older adults in urban slums in India. Most related studies have been conducted in community or hospital settings, with an underrepresentation of urban slum populations. Given their deprived living conditions and lack of social protection for healthcare, older slum dwellers likely experience a disproportionately greater burden of multimorbidity and related adverse consequences.\u003c/p\u003e \u003cp\u003eThere is a need for representative data on the prevalence of multimorbidity and its relationship with health-related quality of life in this marginalized population. This approach can help identify high-risk groups and modifiable factors to inform targeted interventions and appropriate health services for multimorbidity management. Therefore, we aimed to conduct a community-based cross-sectional study on multimorbidity and its association with health-related quality of life among older adults residing in urban areas. We hypothesized that multimorbidity would be highly prevalent in this population and associated with poor quality of life.\u003c/p\u003e"},{"header":"Methodology","content":" \u003cp\u003eStudy Design and Setting\u003c/p\u003e \u003cp\u003eThis was a community-based cross-sectional study conducted in an urban slum in Gujarat from Jan 2023 to Dec 2023.\u003c/p\u003e \u003cp\u003eSample Size Calculation\u003c/p\u003e \u003cp\u003eThe prevalence of multimorbidity among older adults in an urban slum area was estimated considering a 50% prevalence (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), 5% absolute precision, and a 10% nonresponse rate. The required minimum sample size is 800\u003c/p\u003e \u003cp\u003eThe sampling technique employed was a multistage random sampling approach. In the first stage, four out of the approximately 20 municipal wards in the urban slum area were randomly selected using the lottery method. Subsequently, systematic random sampling was applied in each of the four chosen wards to select households. A rough sketch map was used to guide the process, with every 5th household included in the sample. In the third stage, within each selected household, eligible individuals (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) were listed, and one older adult was randomly chosen using the lottery method. The eligibility criteria included age\u0026thinsp;\u0026ge;\u0026thinsp;65 years, residence in the selected households, and providing informed consent, while the exclusion criteria included inability to communicate, bedridden, or unwilling to participate.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eIn this research endeavor, a meticulously designed pretested interviewer-administered questionnaire served as the primary tool for data collection. The questionnaire elicited sociodemographic information from participants, providing insights into the studied population. Additionally, participants self-reported chronic conditions, providing information on health issue prevalence.\u003c/p\u003e \u003cp\u003eThe Short Form-12 (SF-12) was utilized to assess health-related quality of life (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This widely recognized instrument evaluates physical and mental health. Furthermore, the 47-item Short Form Health Literacy Scale (HLS-SF-47) was administered to measure participants' health literacy across different domains (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Health literacy is defined as the degree to which individuals can obtain, process, and understand basic health information and services needed to make appropriate health decisions (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Anthropometric data such as height, weight, and blood pressure were also recorded. Physical activity level: Assessed using the Global Physical Activity Questionnaire (GPAQ) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Smoking status: Categorized as a non-smoker, current smoker, or former smoker. Alcohol use: Drinking frequency and number of drinks per occasion. Dietary patterns: Assessed using a food frequency questionnaire (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Social support/living arrangements: Measured using the Multidimensional Scale of Perceived Social Support (MSPSS) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Healthcare access: Based on the distance to the nearest health facility and reported barriers to healthcare. (Limited healthcare access: Defined based on distance to nearest health facility (\u0026gt;\u0026thinsp;5 km) and self-reported barriers to healthcare access including lack of transportation, inability to pay fees, and lack of social support to attend appointments. Access was categorized as adequate if the nearest facility was within 5 km and no barriers were reported, moderately accessible if the facility was \u0026gt;\u0026thinsp;5 km but no other barriers, and limited access if the facility was \u0026gt;\u0026thinsp;5 km and participants reported\u0026thinsp;\u0026ge;\u0026thinsp;1 barrier).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe collected data were meticulously processed and analyzed using the Statistical Package for Social Sciences (SPSS version 26). Descriptive statistics, such as the mean and standard deviation for continuous variables and frequencies with percentages for categorical variables, were employed to provide a comprehensive overview of the dataset.\u003c/p\u003e \u003cp\u003eThe prevalence of multimorbidity within the studied population was calculated, shedding light on the coexistence of multiple chronic conditions. Bivariate analysis utilizing t-tests and chi-square tests was also conducted to explore relationships and differences between variables, contributing to a nuanced understanding of the dataset.\u003c/p\u003e \u003cp\u003eTo delve deeper into the factors associated with multimorbidity, multivariate logistic regression analysis was employed. This statistical technique allowed for the identification of independent variables that may predict the presence of multimorbidity. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ensuring a robust statistical threshold.\u003c/p\u003e \u003cp\u003eEthical consideration\u003c/p\u003e \u003cp\u003e This study started after ethical clearance was obtained from the Institutional Ethics Committee. Informed consent was obtained first after the purpose of the study was explained, and participants were not obliged to answer any questions that they did not like or were free to terminate the interview at any given time. Assurance was given that confidentiality concerning their information would be strictly maintained.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic characteristics of the study participants (n\u0026thinsp;=\u0026thinsp;800)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e420 (52.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHindu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuslim\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e440 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360 (45.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher secondary/diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSES\u003c/p\u003e \u003cp\u003e(By modified BG Prasad Classification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e460 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (15.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth care access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdequate access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of multimorbidity (\u0026ge;\u0026thinsp;2 chronic conditions) among study participants (n\u0026thinsp;=\u0026thinsp;800)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimorbidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalent disease clusters and comorbidity combinations among older adults with multimorbidity (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease Clusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120 (24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u0026thinsp;+\u0026thinsp;Heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (16%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory disease\u0026thinsp;+\u0026thinsp;Heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u0026thinsp;+\u0026thinsp;Osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u0026thinsp;+\u0026thinsp;Stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart disease\u0026thinsp;+\u0026thinsp;Cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u0026thinsp;+\u0026thinsp;Diabetes\u0026thinsp;+\u0026thinsp;Osteoarthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of health-related quality of life (SF-12) between older adults with and without multimorbidity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSF-12 scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultimorbidity Absent (n\u0026thinsp;=\u0026thinsp;300)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultimorbidity Present (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Component Summary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Component Summary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *-significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between multimorbidity and health literacy (HLS-SF-47 scale scores)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimorbidity Status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInadequate Health Literacy (HLS-SF-47 score\u0026thinsp;\u0026lt;\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdequate Health Literacy (HLS-SF-47 score\u0026thinsp;\u0026ge;\u0026thinsp;26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo multimorbidity (\u0026le;\u0026thinsp;1 chronic condition)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e300 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.5 (2.5\u0026ndash;8.0)\u003c/p\u003e \u003cp\u003eP value\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultimorbidity (\u0026ge;\u0026thinsp;2 chronic conditions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500 (62.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *-significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors associated with multimorbidity among study participants according to multivariate logistic regression analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted Odds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.05 (1.02\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.86 (1.12\u0026ndash;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05 (1.15\u0026ndash;3.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12 (1.52\u0026ndash;6.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.35 (1.22\u0026ndash;4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInadequate health literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.2 (2.1\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysically inactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.68 (1.02\u0026ndash;2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.57 (1.01\u0026ndash;2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (0.78\u0026ndash;2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.67\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.92\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited healthcare access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.87\u0026ndash;1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05; *-significant.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the sociodemographic characteristics of the 800 study participants. Frequencies and percentages are presented for the categories of age, sex, religion, marital status, education level, and socioeconomic status (SES). (Table-1)\u003c/p\u003e \u003cp\u003eThe prevalence of multimorbidity (defined as \u0026ge;\u0026thinsp;2 chronic conditions) among the 800 study participants is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, 500 participants (62.5%) were found to have multimorbidity. (Table-2)\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the most common comorbidity disease clusters and comorbidity combinations found among the 800 older adults in the urban slum setting. (Table-3)\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, multimorbidity was associated with significantly lower quality-of-life scores on the SF-12 scale compared to those without multimorbidity. The mean physical component summary score was 42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 among older adults without multimorbidity versus 39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 among those with multimorbidity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mean mental component summary score was 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 versus 45.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table-4)\u003c/p\u003e \u003cp\u003eThe association between multimorbidity status and health literacy levels is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Among the 300 participants without multimorbidity, 60 (20%) had inadequate health literacy. Among the 500 participants with multimorbidity, 240 (48%) had inadequate health literacy. Older adults with multimorbidity had 4.5 times greater odds of having inadequate health literacy than those without multimorbidity (COR 4.5, 95% CI 2.5-8.0; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table-5)\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis was conducted to identify factors associated with multimorbidity (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). After adjusting for sociodemographic variables, inadequate health literacy (HLS-SF-47 score\u0026thinsp;\u0026lt;\u0026thinsp;26) remained strongly associated with an increased likelihood of multimorbidity (AOR 4.2, 95% CI 2.1\u0026ndash;8.5). Being physically inactive (AOR 1.68, 95% CI 1.02\u0026ndash;2.77; p\u0026thinsp;=\u0026thinsp;0.04) and lacking social support (AOR 1.57, 95% CI 1.01\u0026ndash;2.45; p\u0026thinsp;=\u0026thinsp;0.047) were associated with greater odds of having multimorbidity. However, smoking status, alcohol use, dietary patterns, and healthcare access were not significantly associated with multimorbidity in the adjusted analysis.\u003c/p\u003e \u003cp\u003eIn summary, inadequate health literacy emerged as the strongest independent predictor of multimorbidity in this study population. Lack of physical activity and social support also contributed to a higher likelihood of multimorbidity among the urban slum dwellers.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this community-based cross-sectional study, we found a high prevalence of multimorbidity (\u0026ge;\u0026thinsp;2 chronic conditions) affecting more than 60% of older adults residing in urban areas. The most prevalent comorbidities were hypertension, diabetes, musculoskeletal disorders, respiratory diseases, and mental health issues. Compared with healthier adults, multimorbidity was significantly associated with lower quality of life, with older adults reporting poorer physical and mental health on the SF-12 scale.\u003c/p\u003e \u003cp\u003eOur findings on the high burden of multimorbidity align with those of previous studies in India, which reported a prevalence ranging from 55\u0026ndash;65% among community-dwelling older adults (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The pattern of common chronic conditions observed in this marginalized population also conforms to the epidemiological transition underway in urban regions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). With continuing demographic and lifestyle changes, India is facing escalating burdens of noncommunicable diseases manifesting as multimorbidity among its rapidly growing elderly population.\u003c/p\u003e \u003cp\u003eThe two most prevalent clusters were hypertension paired with diabetes (in 80 participants, 32%) and hypertension paired with osteoarthritis (in 60 participants, 24%).\u003c/p\u003e \u003cp\u003eThese patterns align with multimorbidity data from previous studies in India that also noted hypertension, diabetes, cardiovascular disease, and musculoskeletal disorders as the predominant co-occurring chronic conditions among older adults (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high prevalence of certain clusters emphasizes the need to strengthen the integrated screening and management of comorbid conditions such as diabetes and hypertension that tend to coexist and negatively impact outcomes. Tackling common modifiable risk factors and addressing disease combinations through a patient-centered approach can help reduce the burden of multimorbidity as the population ages.\u003c/p\u003e \u003cp\u003eThe strong inverse association between multimorbidity and quality of life is consistent with reports across diverse global settings (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Managing multiple chronic conditions simultaneously has a detrimental additive effect on physical capacities, psychological well-being, social relationships, and independence in daily living. Multimorbidity also results in complex healthcare needs and polypharmacy, which older adults in resource-constrained slums are ill-equipped to handle. Their poor living conditions, limited access to health services, and lack of social protection exacerbate the challenges of multimorbidity.\u003c/p\u003e \u003cp\u003eA key finding was the high prevalence of inadequate health literacy associated with multimorbidity. Nearly half (48%) of the older adults with multimorbidity had inadequate health literacy, whereas only 20% of those without multimorbidity did. After adjusting for confounders, inadequate health literacy increased the likelihood of having multimorbidity by more than 4 times. This finding aligns with prior research showing that health literacy is an independent predictor of multimorbidity (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Low health literacy can impede self-management of chronic diseases, medication adherence, and utilization of preventive services (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Enhancing health literacy through community education and capacity building may help reduce the risk and effects of multimorbidity among vulnerable elderly people.\u003c/p\u003e \u003cp\u003eThe multivariate regression analysis showed that inadequate health literacy, lack of physical activity, and lack of social support were significantly associated with a higher likelihood of multimorbidity in this urban slum population. These findings align with prior studies demonstrating the role of health literacy and social determinants in multimorbidity risk. A systematic review found that low health literacy was associated with greater multimorbidity prevalence in several studies from Europe and Asia (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Other research has linked social isolation and poorer social support with an increased number of chronic conditions among older adults (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Finally, a cohort study in Brazil concluded that insufficient physical activity was predictive of developing multimorbidity over a 2-year follow-up period (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Taken together, these modifiable factors related to health behaviors, capacities, and social environment appear to contribute significantly to the development of multimorbidity, even after accounting for sociodemographic characteristics.\u003c/p\u003e \u003cp\u003eOur study is the first to provide representative data on the prevalence of multimorbidity and its impact on quality of life, specifically among older residents of an Indian urban slum. The sampling strategy allowed the inclusion of this underserved population, which is often excluded from national health surveys. These findings can inform targeted interventions to alleviate the disproportionate multimorbidity burden imposed by socioeconomically marginalized elderly groups.\u003c/p\u003e \u003cp\u003eLimitations of this study include the cross-sectional design, which restricts causal inference about the association between multimorbidity and quality of life. The reliance on self-reported diagnoses of chronic conditions could result in underreporting. We selected only one slum area, which may limit the generalizability of the findings to other urban slums that differ substantially in their demographic composition and health profiles. Nonetheless, the study provides novel insights into the vulnerability of older slum dwellers to multimorbidity and its adverse effects.\u003c/p\u003e \u003cp\u003eRecommendations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIntegrated screening and management programs for multimorbidity should be implemented in urban slums targeting older adults.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAffordable primary care and geriatric services need to be made accessible within slum settings.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePublic health policies and interventions must address social determinants such as education, financial security, and living conditions in slums.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFamily members and caregivers of older adults with multimorbidity require training and support.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCommunity awareness of healthy lifestyles, preventive behaviors, and self-care should be created.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMultimorbidity among older adults in urban slums requires urgent policy attention and action. A multipronged strategy should focus on both preventive and management aspects, spanning health promotion, community-based screening, affordable primary care, geriatric services, and social assistance. Tackling socioeconomic deprivation alongside lifestyle risks and timely disease management can help reduce the multimorbidity burden and improve the quality of life among marginalized elderly people in urban India.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e1. Short Form Health Literacy Scale\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHLS-SF-47\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e2. Short Form\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e12-SF-12\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003eEthics approval and consent to participate\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eGood clinical care guidelines were followed, and the guidelines were established as per the Helsinki Declaration 2008.\u003c/p\u003e\n\u003cp\u003eAll the participants were given clear instructions about the study before the start of the study.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from the patients in the vernacular language for study participation. No identifying information or images have been included in the original article, which was submitted for publication in an online open-access publication.\u003c/p\u003e\n\u003cp\u003eThe entire methodology and protocol were approved by the Institutional Ethical Committee of Shri M P Shah Government Medical College, Jamnagar, Gujarat, India.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;An ethical approval was obtained from the institute (Shri M P Shah Government Medical College, Jamnagar, Gujarat, India) before the start of the study. (REF No: 216/03/23)\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eConsent for publication\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAvailability of data and materials\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available to protect the privacy of the study participants but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCompeting interests\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eFunding: None\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAuthors\u0026apos; contributions\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eYM contributed to\u0026nbsp;the conceptualization, data curation, formal analysis, investigation, methodology, resources, supervision, validation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed to the conceptualization, data curation, formal analysis, investigation, writing (original draft), and writing (review and editing). YM, NM, ND, and NT contributed\u0026nbsp;to the methodology, resources, supervision, validation, and writing (review and editing). YM, NM, ND, and NT contributed to\u0026nbsp;the formal analysis, investigation, writing (original draft), and writing (review and editing). All the\u0026nbsp;authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge and are grateful to all the patients who contributed to the collection of data for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairperson of MDRU), Dr. Dipesh Parmar (Professor and Head, the Department of Community Medicine), Shri M P Shah Government Medical College, Jamnagar, India.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFortin M, Stewart M, Poitras M-E, Almirall J, Maddocks H. A systematic review of prevalence studies on multimorbidity: Toward a more uniform methodology. The Annals of Family Medicine. 2012;10(2):142\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1370/afm.1337\u003c/span\u003e\u003cspan address=\"10.1370/afm.1337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfshar S, Roderick PJ, Kowal P, Dimitrov BD, Hill AG. 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PLoS ONE. 2019;14(10):e0219072.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Multimorbidity, Older adults, Urban slums, Quality of life, India","lastPublishedDoi":"10.21203/rs.3.rs-3871975/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3871975/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: India is experiencing a rising burden of multimorbidity due to an aging population and epidemiological transition. Older adults residing in urban slums are especially vulnerable due to challenges in managing multiple comorbidities amid deprived living conditions. This study aimed to assess the prevalence of multimorbidity, associated health literacy, and quality of life impact in this marginalized population.\u003c/p\u003e\n\u003cp\u003eMethods and Materials: A community-based cross-sectional study was conducted among 800 adults aged ≥65 years in an urban slum in Gujarat, India. Participants were selected through multistage random sampling. Data on sociodemographics, chronic conditions, health literacy (HLS-SF-47 scale), quality of life (SF-12 scale), physical activity, social support, smoking, alcohol use, diet, and healthcare access were collected. \u0026nbsp;and were collected. Multimorbidity was defined as the presence of ≥2 conditions. Categorical variables are presented as the frequency and percentage, and numerical variables are presented as the mean ± SD. Logistic regression analyses were applied to test the relationship between categorized independent and dependent variables, and all tests were two-tailed, with statistical significance set at the probability value (P \u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eResults: The prevalence of multimorbidity was 62.5% (500/800). Multimorbidity was significantly associated with lower physical component summary (PCS) and mental component summary (MCS) scores on the SF-12 (p\u0026lt;0.001). Nearly half (48%) of the older adults with multimorbidity had inadequate health literacy, compared to 20% of those without multimorbidity. After adjustment, inadequate health literacy increased the likelihood of having multimorbidity by more than 4 times (AOR 4.2, 95% CI 2.1-8.5). Older age (AOR 1.05, 95% CI 1.02-1.09; p=0.002), female sex (AOR 1.86, 95% CI 1.12-3.08; p=0.016), widowhood (AOR 2.05, 95% CI 1.15-3.65; p=0.015), no formal education (AOR 3.12, 95% CI 1.52-6.41; p=0.002), low socioeconomic status (AOR 2.35, 95% CI 1.22-4.52; p=0.011), being physically inactive (AOR 1.68, 95% CI 1.02-2.77), and lacking social support (AOR 1.57, 95% CI 1.01-2.45) were associated with greater odds of multimorbidity.\u003c/p\u003e\n\u003cp\u003eConclusion: There is a high burden of multimorbidity among urban slum dwellers aged ≥65 years in India, which is strongly linked to inadequate health literacy, physical inactivity, and poor social support. Improving health literacy and addressing modifiable social determinants of health are essential to reducing multimorbidity prevalence in this marginalized population.\u003c/p\u003e","manuscriptTitle":"Multimorbidity, Health Literacy, and Quality of Life Among Older Adults in an Urban Slum in India: A Community-Based Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 19:31:18","doi":"10.21203/rs.3.rs-3871975/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-14T16:55:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-29T07:08:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"862f6601-0e6b-448e-a2bc-290dbafe5a95","date":"2024-01-28T11:18:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-28T09:59:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-28T09:52:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-23T14:28:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-23T14:26:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-01-17T05:35:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"41d3f9cd-3f2b-41d2-8e2c-71f3201bb6b2","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T11:16:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 19:31:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3871975","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3871975","identity":"rs-3871975","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}