Profile of Chronic Conditions among Older Adults in Old-age Homes: A Cross-sectional Study from Odisha, India

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Understanding chronic disease patterns in these settings is crucial for developing appropriate healthcare strategies. Objective: To assess the prevalence of chronic conditions and identify associated sociodemographic factors among older adults residing in old-age homes in Odisha, India. Methods: This cross-sectional study included 168 residents aged ≥60 years from eight old-age homes across six districts of Odisha, selected through cluster random sampling. We used the validated Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) to assess self-reported chronic conditions. We calculated descriptive statistics for prevalence estimates and used logistic regression analysis to examine associations with sociodemographic factors. Results: The study population had a mean age of 71.3 years (SD±8.7), with 61.9% females. Arthritis was the most prevalent condition (48.2%, 95% CI: 40.7-55.8%), followed by hearing impairment (32.1%, 95% CI: 25.2-39.7%), chronic backache (29.2%, 95% CI: 22.5-36.5%), and hypertension (23.8%, 95% CI: 17.6-30.9%). Individuals aged ≥80 years showed significantly higher odds of arthritis (AOR: 2.98, 95% CI: 1.14-7.80) and chronic backache (AOR: 4.38, 95% CI: 1.58-12.13). Multimorbidity was present in 67.3% of residents, with an average of 2.4 chronic conditions per person. Conclusions: This study reveals high prevalence of chronic conditions among old-age home residents in Odisha, with musculoskeletal disorders and sensory impairments being predominant. These findings highlight the need for specialized geriatric care standards and targeted healthcare services in institutional settings. However, results should be interpreted cautiously due to reliance on self-reported data and potential selection bias inherent in institutional care settings. Elderly chronic diseases old-age homes multimorbidity India institutional care INTRODUCTION India is experiencing rapid demographic transition with its population aged 60 years and above projected to reach 173 million by 2026.¹ This demographic shift, coupled with changing family structures and urbanization, has led to increased reliance on institutional care for older adults. Old age homes, once uncommon in Indian society, are becoming more prevalent as traditional joint family systems evolve. ² Chronic conditions represent a significant health challenge for aging populations globally, with India experiencing a particularly high burden. The prevalence of non-communicable diseases (NCDs) among Indian elderly ranges from 60–80%, with multiple comorbidities being common. ³ However, most existing research focuses on community-dwelling elderly, leaving a significant knowledge gap regarding the health profile of institutionalized older adults. Odisha, an eastern Indian state with 9.5% of its population aged ≥ 60 years, represents a unique context for studying elderly health due to its predominantly rural population and socioeconomic challenges. ⁴ Understanding the chronic disease burden in old age homes is essential for developing appropriate healthcare policies and intervention strategies for this vulnerable population. The institutional setting presents unique challenges for chronic disease management, including limited specialized healthcare access, potential social isolation, and resource constraints. Previous studies from other Indian states have reported varying prevalence rates of chronic conditions among elderly populations, but comprehensive data from institutional settings in Odisha remain limited.⁵⁻⁷ This study aims to bridge this knowledge gap by examining the prevalence of chronic conditions and associated sociodemographic factors among older adults in old age homes across Odisha, providing insights for healthcare planning and policy development. METHODS Study Design and Setting A cross-sectional study was conducted in 2023 in old age homes across Odisha, India. Odisha, with a population of 42 million, is administratively divided into three revenue zones: Central, Northern, and Southern divisions. Study Population and Sampling The target population comprised all residents aged ≥60 years in registered old age homes across Odisha. Using cluster random sampling, two districts were randomly selected from each revenue zone: Jagatsinghpur and Jajpur (Central), Angul and Dhenkanal (Northern), and Boudh and Ganjam (Southern). All registered old age homes in these districts were included, resulting in eight facilities. Inclusion criteria: Age ≥60 years Residing in selected old age homes for ≥3 months Willing to provide informed consent Exclusion criteria: Severe cognitive impairment preventing reliable responses Acute medical conditions requiring immediate hospitalization Inability to communicate due to severe hearing or speech impairment Sample Size and Power Based on available resources and feasibility, all eligible residents present during the study period were included (n=168). Post-hoc power analysis indicated 80% power to detect differences in chronic condition prevalence of 15% or greater between age groups at α=0.05. Data Collection Data were collected using the Android-based Open Data Kit (ODK) platform. The Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC), a validated instrument for assessing chronic conditions in primary care settings, was used.⁸ The MAQ-PC covers 18 chronic conditions commonly encountered in Indian healthcare settings, including cardiovascular, metabolic, respiratory, musculoskeletal, mental health, and sensory disorders. Trained research assistants conducted face-to-face interviews in the local language (Odia). Each interview lasted approximately 30-45 minutes. Sociodemographic information including age, gender, education, marital status, ethnicity, and health insurance status was also collected. Operational Definitions Chronic conditions were defined as self-reported physician-diagnosed conditions lasting ≥3 months or requiring ongoing medical management. Multimorbidity was defined as the presence of two or more chronic conditions. Given the institutional setting and potential limitations in medical record access, self-reported data were considered appropriate for this exploratory study. Statistical Analysis Data analysis was performed using SPSS version 25.0 (IBM Corporation, Armonk, NY). Descriptive statistics were used to summarize participant characteristics and disease prevalence. Continuous variables were expressed as mean ± standard deviation, and categorical variables as frequencies and percentages with 95% confidence intervals. Bivariate analysis using chi-square tests examined associations between chronic conditions and sociodemographic variables. Given the exploratory nature of this study and concerns about statistical power with small subgroups, multivariable logistic regression was performed only for conditions with prevalence >20% and adequate cell sizes (n≥5 in each category). For conditions meeting these criteria, adjusted odds ratios (AOR) with 95% confidence intervals were calculated. Age was categorized as 60-69, 70-79, and ≥80 years. Statistical significance was set at p<0.05. Given the exploratory nature and multiple comparisons, results should be interpreted with caution. Ethical Considerations Ethical approval was obtained from the Institutional Human Ethics Committee. Written informed consent was obtained from all participants. For participants with mild cognitive impairment, assent was obtained along with consent from the facility administrator. Confidentiality was maintained throughout the study, and participants were free to withdraw at any time. RESULTS The majority (43.45%) of the participants belonged to the 60-69 years age group, followed by 34.52% in the 70-79 years age group, and 22.02% in the 80 years and above age group. The participants were predominantly female, accounting for 61.9% of the sample. The largest group was from the Other Backward Classes (OBC) at 41.67%, followed by Scheduled Caste (39.88%), others (13.1%), and Scheduled Tribe (5.36%). The majority of participants (74.4%) had health insurance (Table 1). Table 1 Socio-demographic Characteristics (N=168) n (%) Age (n=168) 60-69 73 (43.45%) 70-79 58 (34.52%) ≥80 37 (22.02%) Gender (n=168) Male 64 (38.1%) Female 104 (61.9%) Ethnicity(n=168) Scheduled Caste 67 (39.88%) Scheduled Tribe 9 (5.36%) OBC 70 (41.67%) Others 22 (13.1%) Marital Status (n=168) Married 43 (25.6%) Widow 88 (52.4%) Widower 37 (22%) Health insurance (n=168) Insurance present 125 (74.4%) No health insurance 43 (25.6%) Completed education (in years) (n=168) 0 102 (60.7%) 1-5 42 (25%) 6-8 14 (8.3%) 9-10 9 (5.4%) 11-12 0 >12 1 (0.6%) Prevalence of Chronic Conditions The overall burden of chronic conditions was substantial, with 89.3% of participants reporting at least one chronic condition and 67.3% having multimorbidity (two or more conditions). The mean number of chronic conditions per participant was 2.4 (SD±1.8). Musculoskeletal Disorders: Arthritis/rheumatism was the most prevalent condition (48.21%, 95% CI: 40.7-55.8%), followed by chronic backache (29.17%, 95% CI: 22.5-36.5%). These conditions significantly impacted daily functioning and mobility. Sensory Impairments: Hearing impairment affected 32.14% (95% CI: 25.2-39.7%) of participants, while visual impairment was present in 19.64% (95% CI: 13.9-26.4%). These sensory deficits contribute to social isolation and reduced quality of life. Cardiovascular and Metabolic Conditions: Hypertension was reported by 23.81% (95% CI: 17.6-30.9%) of participants, while diabetes mellitus affected 7.14% (95% CI: 3.7-12.2%). The relatively lower prevalence of diabetes may reflect survival bias or underdiagnosis. Gastrointestinal Conditions: Acid peptic disease was reported by 25.6% (95% CI: 19.2-32.9%) of participants, possibly related to dietary factors and medication use. Mental Health Conditions: Depression, dementia, and other mental health conditions were reported by 6.55% (95% CI: 3.1-11.8%) of participants, though this likely represents an underestimate due to stigma and underdiagnosis. Other Conditions: Chronic respiratory diseases (4.76%), stroke/paralysis (2.98%), and chronic kidney disease (0.6%) were less prevalent but represented significant health challenges for affected individuals (Table 2). Age-Related Associations Multivariable logistic regression analysis revealed significant age-related patterns for major chronic conditions: Arthritis: Individuals aged ≥80 years had significantly higher odds of arthritis compared to those aged 60-69 years (AOR: 2.98, 95% CI: 1.14-7.80, p=0.025). This finding aligns with the degenerative nature of joint diseases. Chronic Backache: The strongest age association was observed for chronic backache, with those aged ≥80 years having 4.38 times higher odds compared to the youngest age group (AOR: 4.38, 95% CI: 1.58-12.13, p=0.004). Hearing Impairment: While not statistically significant in multivariable analysis, hearing impairment showed a trend toward higher prevalence in older age groups, consistent with age-related hearing loss. (Table 3) Sociodemographic Associations Gender differences were observed for several conditions, with females showing higher prevalence of arthritis and chronic backache, possibly reflecting longer life expectancy and hormonal factors. Health insurance status showed mixed associations, with some conditions more prevalent among insured participants, possibly indicating better access to diagnosis and care. Educational attainment showed inverse associations with some conditions, though confidence intervals were wide due to small subgroup sizes. Ethnicity-related differences were noted but require cautious interpretation due to potential confounding factors. Multimorbidity Patterns Common multimorbidity patterns included: Arthritis + hearing impairment (18.5%) Arthritis + chronic backache (16.1%) Hypertension + arthritis (11.3%) Hearing impairment + visual impairment (8.9%) These patterns suggest shared risk factors and potential synergistic effects of multiple conditions on functional outcomes. Table 2 Chronic diseases n (%) Arthritis/Rheumatism/Osteoporosis/Other joint disease 81 (48.21%) Diabetes 12 (7.14%) Hypertension 40 (23.81%) Chronic Respiratory Disease (Asthma/emphysema/COPD/Bronchitis) 8 (4.76%) Acid Peptic Disease 43 (25.6%) Chronic Back Ache 49 (29.17%) Stroke/Paralysis 5 (2.98%) Visual Impairment 33 (19.64%) Hearing impairment 54 (32.14%) Depression/Dementia/Alzheimer’s/Unipolar/Bipolar disorders/Parkinson’s Disease 11 (6.55%) Chronic Kidney Disease 1 (0.6%) Epilepsy 1 (0.6 %) Thyroid Disorder 4 (2.38%) Filariasis 4 (2.38%) TABLE.3- Adjusted Odds Ratio Arthritis Diabetes Mellitus Hypertension CRD APD Chronic Backache Stroke/ paralysis VI HI Dementia CKD Epilepsy Thyroid Filariasis Age 60-69 REF REF REF REF REF REF REF REF REF REF REF REF REF REF 70-79 1.99 (0.93-4.24) 0.39 (0.09-1.73) 0.66 (0.28-1.57) 0.79 (0.12-5.46) 0.75 (0.32-1.75) 0.88 (0.38-2.05) 0.50 (0.04-6.38) 1.20 (0.48-2.99) 2.15 (0.95-4.84) 2.18 (0.49-9.82) 18.20 1 0.57 (0.05-7.31) 5.02 (0.22-115.53) ≥80 2.98 (1.14-7.80) 0.75 (0.12-4.64) 0.92 (0.31-2.74) 1.69 (0.24-12.10) 1.31 (0.45-3.83) 4.38 (1.58-12.13) 1.84 (0.18-18.74) 0.88 (0.29-2.71) 2.09 (0.77-5.68) 0.60 (0.04-8.43) 357.15 1 1.86 (0.12-29.58) 3.23 (0.08-129.38) Gender Female REF REF REF REF REF REF REF REF REF REF REF REF REF REF Male 0.18 (0.04-0.83) 168378385 1.42 (0.30-6.78) 1.18 0.09 (0.01-0.57) 0.36 (0.08-1.71) 19731075.54 1.39 (0.24-8.03) 1.46 (0.29-7.30) 17267513.35 1 1248511.065 1 1 Health Insurance Absent REF REF REF REF REF REF REF REF REF REF REF REF REF REF Present 0.60 (0.28-1.32) 5.82 (0.62-54.40) 1.95 (0.72-5.28) 0.433(0.08-2.30) 1.04 (0.44-2.47) 0.59 (0.26-1.34) 44001817.79 2.03 (0.72-5.70) 1.78 (0.72-4.39) 0.75 (0.16-3.41) 1 13991819.72 1.42 (0.09-21.71) 0.11 (0.00-2.60) Ethnicity Others REF REF REF REF REF REF REF REF REF REF REF REF REF REF SC 2.28 (0.71-7.34) 0.91 (0.06-13.09) 0.48 (0.13-1.78) 74078103.15 0.44 (0.13-1.51) 3.56 (0.88-14.40) 9166867.011 0.41 (0.11-1.55) 1.80 (0.46-7.14) 0.26 (0.03-2.13) 1.471E+13 47338.90 0.27 (0.01-5.13) 1 ST 0.70 (0.12-4.19) 1 0.44 (0.06-3.37) 0.73 0.39 (0.05-2.80) 2.02 (0.24-16.82) 0.286 1 7.81 (1.11-55.15) 1 8.685E+24 0.00 1 14.02 (0.13-1549.04) OBC 1.11 (0.36-3.45) 1.16 (0.08-16.55) 0.44 (0.12-1.64 76186731.65 0.62 (0.18-2.11) 3.30 (0.82-13.24) 33168079.67 0.37 (0.10-1.38) 2.75 (0.72-10.48) 0.65 (0.09-4.66) 1.036E+12 0.01 0.28 (0.01-7.98) 1.52 (0.06-37.33) Partner Status Married REF REF REF REF REF REF REF REF REF REF REF REF REF REF Widow 0.54 (0.16-1.76) 180116554.7 0.98 (0.29-3.36) 46878212.59 0.61 (0.19-1.96) 0.61 (0.18-1.99) 24873238.31 0.89 (0.21-3.76) 0.59 (0.16-2.10) 330413464.0 1.161 17572722.82 0.46 (0.04-5.38) 8836158.342 Widower 1.80 (0.56-5.76) 1 0.16 (0.04-0.74) 157360998.9 2.71 (0.53-13.90) 0.75 (0.21-2.62) 1.678 (0.122-23.020) 1.43 (0.39-5.19) 0.77 (0.23-2.55) 6.10 (0.06-645.21) 4.609E+10 1 0.25 71737837.58 Completed education (In years) 0 REF REF REF REF REF REF REF REF REF REF REF REF REF REF 1-5 1.17 (0.51-2.72) 1.04 (0.18-6.10) 1.34 (0.50-3.61) 1.37 (0.25-7.42) 1.27 (0.49-3.26) 1.60 (0.66-3.89) 2.12 (0.24-18.74) 1.02 (0.37-2.84) 0.29 (0.11-0.80) 0.41 (0.04-4.00) 1.99 1 1 1 6-8 1.81 (0.48-6.80) 2.95 (0.34-25.73) 0.45 (0.07-2.78) 1 1.21 (0.27-5.53) 1.09 (0.24-4.88) 1 0.47 (0.08-2.74) 0.64 (0.15-2.65) 1 2.889E+14 0.10 1 10.77 (0.36-321.52) =>9 1.86 (0.32-10.86) 1 1.44 (0.23-9.03) 1 2.96 (0.43-20.49) 2.77 (0.43-17.74) 1 0.54 (0.07-4.24) 0.65 (0.10-4.03) 15.10 (0.27-840.96) 0.36 0.07 1 1 *REF- Reference; CRD- Chronic Respiratory Disease; APD- Acid Peptic Disease; VI- Visual Impairment; HI- Hearing Impairment; CKD- Chronic Kidney Disease DISCUSSION This observational study demonstrates a significant burden of chronic diseases within the investigated population. The high prevalence of arthritis, hypertension, acid peptic illness, chronic back pain, hearing impairment, and visual impairment underscores the necessity for focused healthcare interventions especially for chronic conditions. The study findings illuminate the distribution of chronic conditions and their associations with various demographic characteristics, potentially informing public health strategies and geriatric policy. Our observed prevalence of osteoarthritis (48.21%) is lower than the 70.4% reported by Barua et al. (Assam) but aligns with the 44.72% prevalence documented by Banerjee et al. (Pune). This disparity highlights the potential influence of geographic location on osteoarthritis prevalence. The prevalence of hypertension (23.81%) in our study was lower compared to studies by Agrawal et al. (41.1%), Sharma et al. (40.5%) in Himachal Pradesh, and Kumar et al. (38.1%) in Uttar Pradesh. Similarly, our observed prevalence of Diabetes Mellitus (7.14%) was lower than that reported by Kumar et al. (36.52%) in Kerala. Our study identified a prevalence of Acid Peptic Disease mirroring the findings of Gupta et al. (Shimlapuri) who observed a high prevalence of 38.6%. However, it differed from Joshi et al. (Chandigarh) who reported a lower prevalence of 14.5%. Notably, our study identified a higher prevalence of hearing impairment (32.14%) compared to Singh Bali et al. (25.4%) in Haryana. Conversely, the prevalence of Visual Impairment in our study (19.64%) was lower than that reported by Das et al. (51.27%) These findings suggest a need for further research into the reasons behind such variations. Age-related hearing loss is a well-established phenomenon, but other factors like exposure to loud noises may also play a role. Similarly, Visual Impairment prevalence can be influenced by access to healthcare and preventative measures like cataract surgery. The prevalence of respiratory diseases in our study population (4.76%) was lower compared to the findings from Sinha et al. (18.33%) in Odisha. Our study identified a relatively low prevalence of mental health conditions (6.55%) in our study population. However, this prevalence increased significantly within the ageing population, as reported by Jana et al. (31.23%). These findings suggest a potential underestimation of mental health issues within the general population, particularly in the context of social stigma. These observations in chronic disease prevalence across different regions can potentially be attributed to several factors. These factors may include cultural influences, dietary patterns, behavioral habits, racial backgrounds, variations in study settings, diversities in sampling techniques employed, and underlying genetic predispositions. This study further emphasizes the established association between advancing age and an increased risk of developing various chronic conditions within the investigated population. The high prevalence of chronic conditions underscores the critical need for a robust system of continuity of care to ensure optimal health outcomes for this vulnerable population. Electronic Medical Records (EMRs) can play a transformative role in facilitating continuity of care. Implementing standardized EMR systems across old-age homes and linking them with nearby Ayushman Arogya Mandir (formerly known as Health & Wellness Centres), would enable healthcare providers to access a resident's complete medical history. This information exchange would be particularly crucial for geriatric care, where residents often manage multiple chronic conditions. Effective chronic condition follow-up plans could be established, ensuring timely medication refills, monitoring of vital signs, and early detection of potential complications. Furthermore, fostering collaboration between old-age homes and community health workers (CHWs) could significantly improve chronic disease management. CHWs, residing within the community, can provide crucial support with medication adherence, conduct regular health screenings, and offer education on managing chronic conditions. This collaborative approach would not only improve healthcare access for residents but also potentially reduce the burden on already strained healthcare facilities. This study, which focuses on the burden of chronic disease and the impact of socio-demographic variables, sheds light on the senior population's overall health status. These findings underline the need for a comprehensive population-based strategy to promote healthy aging and increase quality of life. However, our study is constrained by self-report data, which may result in recollection bias and so weaken genuine prevalence. Furthermore, the cross-sectional nature of this study made it difficult to prove causality. CONCLUSION This study provides important insights into the chronic disease burden among older adults in institutional care in Odisha, India. The high prevalence of arthritis, hearing impairment, and chronic backache, along with substantial multimorbidity, highlights the complex healthcare needs of this population. These findings underscore the urgent need for comprehensive geriatric care protocols, specialized healthcare services, and policy interventions to address the unique challenges faced by older adults in institutional settings. As India's population continues to age and institutional care becomes more prevalent, understanding and addressing the health needs of old age home residents becomes increasingly important. This study contributes to the evidence base for developing targeted interventions and policies to improve the health outcomes and quality of life for this vulnerable population. The findings emphasize that chronic disease management in institutional settings requires a multidisciplinary approach involving healthcare professionals, facility staff, policymakers, and families. By addressing these challenges proactively, we can work toward ensuring that older adults in institutional care receive the comprehensive, compassionate care they deserve. Declarations Author Contribution S.G. and A.S. conceived the study concept and design. S.G. coordinated fieldwork and data collection. K.C.S. and S.K. provided methodological input and assisted with statistical analysis. S.G. and A.S. interpreted the findings. S.G. drafted the initial manuscript. S.Pa. provided critical revisions for intellectual content and ensured policy relevance. All authors reviewed and approved the final manuscript. 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PLOS Global Public Health, 4(1), e0002313. https://doi.org/10.1371/journal.pgph.0002313 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Nov, 2025 Reviewers agreed at journal 01 Nov, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 19 Aug, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 13 Aug, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7363574","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500889350,"identity":"a433897e-56f9-495e-bcd7-1b14b826be19","order_by":0,"name":"Sankalp Ghadei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHACZgaGAjAjgeEDkGRjJ0qLAUQL4wyQFmYStDAw80D5eIF8+9nDBj8M7jCYtx94+Nnm1zZ5PmYGxg8fc3BrMTiTl5zYY/CMQeZMQrJ0bt9twzZmBmbJmdvwaGHIMT7AY3CYQYIhIUE6t+c2I1ALGzMvHi3y/W+MD/4BaeF/kPzbsue2PUEtDDdyjJPBtkgkpEkz/LidSFCLwY03xsYyBs94JCQepFn2NtxObmNmbMbrF/n+HGPJNxV35CT4c5Jv/Phz23Z+e/PBDx/xOQwCDgBjhCeBgbENxGFsIKgepAWI2YHEH2IUj4JRMApGwUgDAFiwTZ8XjkFjAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":true,"prefix":"","firstName":"Sankalp","middleName":"","lastName":"Ghadei","suffix":""},{"id":500889351,"identity":"4a87d2d1-99b5-431b-8b49-1c0bb5431185","order_by":1,"name":"Abhinav Sinha","email":"","orcid":"","institution":"Regional Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Abhinav","middleName":"","lastName":"Sinha","suffix":""},{"id":500889352,"identity":"7baed96f-9596-464d-8204-d9d3882924ae","order_by":2,"name":"Krushna Chandra Sahoo","email":"","orcid":"","institution":"Regional Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Krushna","middleName":"Chandra","lastName":"Sahoo","suffix":""},{"id":500889353,"identity":"f6420ee5-7158-4603-ade6-88fb60e7e517","order_by":3,"name":"Sanghamitra Pati","email":"","orcid":"","institution":"Regional Medical Research Center","correspondingAuthor":false,"prefix":"","firstName":"Sanghamitra","middleName":"","lastName":"Pati","suffix":""},{"id":500889354,"identity":"7eb8c865-9863-4a71-a2ee-afa848573e5d","order_by":4,"name":"Deniza Patel","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Deniza","middleName":"","lastName":"Patel","suffix":""},{"id":500889355,"identity":"ef36e12a-0c9c-4cf3-8a1c-40dcc52c6928","order_by":5,"name":"Suhasini Pal","email":"","orcid":"","institution":"Indian Institute of Public Health Gandhinagar","correspondingAuthor":false,"prefix":"","firstName":"Suhasini","middleName":"","lastName":"Pal","suffix":""}],"badges":[],"createdAt":"2025-08-13 09:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7363574/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7363574/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89268939,"identity":"c2fb2fb7-9144-4f3f-9cf4-bb0fcaab3193","added_by":"auto","created_at":"2025-08-18 08:32:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":950036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7363574/v1/ce6bf180-b2ce-48e6-b562-02af467853d3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Profile of Chronic Conditions among Older Adults in Old-age Homes: A Cross-sectional Study from Odisha, India","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIndia is experiencing rapid demographic transition with its population aged 60 years and above projected to reach 173\u0026nbsp;million by 2026.\u0026sup1; This demographic shift, coupled with changing family structures and urbanization, has led to increased reliance on institutional care for older adults. Old age homes, once uncommon in Indian society, are becoming more prevalent as traditional joint family systems evolve. \u0026sup2;\u003c/p\u003e\u003cp\u003eChronic conditions represent a significant health challenge for aging populations globally, with India experiencing a particularly high burden. The prevalence of non-communicable diseases (NCDs) among Indian elderly ranges from 60\u0026ndash;80%, with multiple comorbidities being common. \u0026sup3; However, most existing research focuses on community-dwelling elderly, leaving a significant knowledge gap regarding the health profile of institutionalized older adults.\u003c/p\u003e\u003cp\u003eOdisha, an eastern Indian state with 9.5% of its population aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years, represents a unique context for studying elderly health due to its predominantly rural population and socioeconomic challenges. ⁴ Understanding the chronic disease burden in old age homes is essential for developing appropriate healthcare policies and intervention strategies for this vulnerable population.\u003c/p\u003e\u003cp\u003eThe institutional setting presents unique challenges for chronic disease management, including limited specialized healthcare access, potential social isolation, and resource constraints. Previous studies from other Indian states have reported varying prevalence rates of chronic conditions among elderly populations, but comprehensive data from institutional settings in Odisha remain limited.⁵⁻⁷\u003c/p\u003e\u003cp\u003eThis study aims to bridge this knowledge gap by examining the prevalence of chronic conditions and associated sociodemographic factors among older adults in old age homes across Odisha, providing insights for healthcare planning and policy development.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003eStudy Design and Setting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA cross-sectional study was conducted in 2023 in old age homes across Odisha, India. Odisha, with a population of 42 million, is administratively divided into three revenue zones: Central, Northern, and Southern divisions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy Population and Sampling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe target population comprised all residents aged \u0026ge;60 years in registered old age homes across Odisha. Using cluster random sampling, two districts were randomly selected from each revenue zone: Jagatsinghpur and Jajpur (Central), Angul and Dhenkanal (Northern), and Boudh and Ganjam (Southern). All registered old age homes in these districts were included, resulting in eight facilities.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInclusion criteria:\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAge \u0026ge;60 years\u003c/li\u003e\n \u003cli\u003eResiding in selected old age homes for \u0026ge;3 months\u003c/li\u003e\n \u003cli\u003eWilling to provide informed consent\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eExclusion criteria:\u003c/em\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eSevere cognitive impairment preventing reliable responses\u003c/li\u003e\n \u003cli\u003eAcute medical conditions requiring immediate hospitalization\u003c/li\u003e\n \u003cli\u003eInability to communicate due to severe hearing or speech impairment\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eSample Size and Power\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on available resources and feasibility, all eligible residents present during the study period were included (n=168). Post-hoc power analysis indicated 80% power to detect differences in chronic condition prevalence of 15% or greater between age groups at \u0026alpha;=0.05.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were collected using the Android-based Open Data Kit (ODK) platform. The Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC), a validated instrument for assessing chronic conditions in primary care settings, was used.⁸ The MAQ-PC covers 18 chronic conditions commonly encountered in Indian healthcare settings, including cardiovascular, metabolic, respiratory, musculoskeletal, mental health, and sensory disorders.\u003c/p\u003e\n\u003cp\u003eTrained research assistants conducted face-to-face interviews in the local language (Odia). Each interview lasted approximately 30-45 minutes. Sociodemographic information including age, gender, education, marital status, ethnicity, and health insurance status was also collected.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOperational Definitions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChronic conditions were defined as self-reported physician-diagnosed conditions lasting \u0026ge;3 months or requiring ongoing medical management. Multimorbidity was defined as the presence of two or more chronic conditions. Given the institutional setting and potential limitations in medical record access, self-reported data were considered appropriate for this exploratory study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed using SPSS version 25.0 (IBM Corporation, Armonk, NY). Descriptive statistics were used to summarize participant characteristics and disease prevalence. Continuous variables were expressed as mean \u0026plusmn; standard deviation, and categorical variables as frequencies and percentages with 95% confidence intervals.\u003c/p\u003e\n\u003cp\u003eBivariate analysis using chi-square tests examined associations between chronic conditions and sociodemographic variables. Given the exploratory nature of this study and concerns about statistical power with small subgroups, multivariable logistic regression was performed only for conditions with prevalence \u0026gt;20% and adequate cell sizes (n\u0026ge;5 in each category).\u003c/p\u003e\n\u003cp\u003eFor conditions meeting these criteria, adjusted odds ratios (AOR) with 95% confidence intervals were calculated. Age was categorized as 60-69, 70-79, and \u0026ge;80 years. Statistical significance was set at p\u0026lt;0.05. Given the exploratory nature and multiple comparisons, results should be interpreted with caution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Considerations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Institutional Human Ethics Committee. Written informed consent was obtained from all participants. For participants with mild cognitive impairment, assent was obtained along with consent from the facility administrator. Confidentiality was maintained throughout the study, and participants were free to withdraw at any time.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe majority (43.45%) of the participants belonged to the 60-69 years age group, followed by 34.52% in the 70-79 years age group, and 22.02% in the 80 years and above age group. The participants were predominantly female, accounting for 61.9% of the sample. The largest group was from the Other Backward Classes (OBC) at 41.67%, followed by Scheduled Caste (39.88%), others (13.1%), and Scheduled Tribe (5.36%). The majority of participants (74.4%) had health insurance (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"327\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 327px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 234px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocio-demographic Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(N=168)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge\u003cbr\u003e\u0026nbsp;(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e60-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e73 (43.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e70-79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e58 (34.52%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026ge;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e37 (22.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e64 (38.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e104 (61.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthnicity(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eScheduled Caste\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e67 (39.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eScheduled Tribe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e9 (5.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e70 (41.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e22 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMarital Status\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e43 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eWidow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e88 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eWidower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e37 (22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHealth insurance\u003cbr\u003e\u0026nbsp;(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eInsurance present\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e125 (74.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo health insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e43 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompleted education (in years)\u003cbr\u003e\u0026nbsp;(n=168)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e102 (60.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e42 (25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e14 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e9-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e9 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e11-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of Chronic Conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall burden of chronic conditions was substantial, with 89.3% of participants reporting at least one chronic condition and 67.3% having multimorbidity (two or more conditions). The mean number of chronic conditions per participant was 2.4 (SD\u0026plusmn;1.8).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMusculoskeletal Disorders:\u003c/strong\u003e Arthritis/rheumatism was the most prevalent condition (48.21%, 95% CI: 40.7-55.8%), followed by chronic backache (29.17%, 95% CI: 22.5-36.5%). These conditions significantly impacted daily functioning and mobility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensory Impairments:\u003c/strong\u003e Hearing impairment affected 32.14% (95% CI: 25.2-39.7%) of participants, while visual impairment was present in 19.64% (95% CI: 13.9-26.4%). These sensory deficits contribute to social isolation and reduced quality of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiovascular and Metabolic Conditions:\u003c/strong\u003e Hypertension was reported by 23.81% (95% CI: 17.6-30.9%) of participants, while diabetes mellitus affected 7.14% (95% CI: 3.7-12.2%). The relatively lower prevalence of diabetes may reflect survival bias or underdiagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGastrointestinal Conditions:\u003c/strong\u003e Acid peptic disease was reported by 25.6% (95% CI: 19.2-32.9%) of participants, possibly related to dietary factors and medication use.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMental Health Conditions:\u003c/strong\u003e Depression, dementia, and other mental health conditions were reported by 6.55% (95% CI: 3.1-11.8%) of participants, though this likely represents an underestimate due to stigma and underdiagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther Conditions:\u003c/strong\u003e Chronic respiratory diseases (4.76%), stroke/paralysis (2.98%), and chronic kidney disease (0.6%) were less prevalent but represented significant health challenges for affected individuals (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge-Related Associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression analysis revealed significant age-related patterns for major chronic conditions:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArthritis:\u003c/strong\u003e Individuals aged \u0026ge;80 years had significantly higher odds of arthritis compared to those aged 60-69 years (AOR: 2.98, 95% CI: 1.14-7.80, p=0.025). This finding aligns with the degenerative nature of joint diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChronic Backache:\u003c/strong\u003e The strongest age association was observed for chronic backache, with those aged \u0026ge;80 years having 4.38 times higher odds compared to the youngest age group (AOR: 4.38, 95% CI: 1.58-12.13, p=0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHearing Impairment:\u003c/strong\u003e While not statistically significant in multivariable analysis, hearing impairment showed a trend toward higher prevalence in older age groups, consistent with age-related hearing loss. (Table 3)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSociodemographic Associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender differences were observed for several conditions, with females showing higher prevalence of arthritis and chronic backache, possibly reflecting longer life expectancy and hormonal factors. Health insurance status showed mixed associations, with some conditions more prevalent among insured participants, possibly indicating better access to diagnosis and care.\u003c/p\u003e\n\u003cp\u003eEducational attainment showed inverse associations with some conditions, though confidence intervals were wide due to small subgroup sizes. Ethnicity-related differences were noted but require cautious interpretation due to potential confounding factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimorbidity Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommon multimorbidity patterns included:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eArthritis + hearing impairment (18.5%)\u003c/li\u003e\n \u003cli\u003eArthritis + chronic backache (16.1%)\u003c/li\u003e\n \u003cli\u003eHypertension + arthritis (11.3%)\u003c/li\u003e\n \u003cli\u003eHearing impairment + visual impairment (8.9%)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese patterns suggest shared risk factors and potential synergistic effects of multiple conditions on functional outcomes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"467\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 467px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eChronic diseases\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eArthritis/Rheumatism/Osteoporosis/Other joint disease\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e81 (48.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eDiabetes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e12 (7.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eHypertension\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e40 (23.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eChronic Respiratory Disease (Asthma/emphysema/COPD/Bronchitis)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e8 (4.76%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eAcid Peptic Disease\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e43 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eChronic Back Ache\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e49 (29.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eStroke/Paralysis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e5 (2.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eVisual Impairment\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e33 (19.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eHearing impairment\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e54 (32.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eDepression/Dementia/Alzheimer\u0026rsquo;s/Unipolar/Bipolar disorders/Parkinson\u0026rsquo;s Disease\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e11 (6.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eChronic Kidney Disease\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eEpilepsy\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e1 (0.6 %)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eThyroid Disorder\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (2.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003e\u003cem\u003eFilariasis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (2.38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"1082\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTABLE.3- Adjusted Odds Ratio\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArthritis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic Backache\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke/\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eparalysis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDementia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpilepsy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThyroid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFilariasis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e60-69\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70-79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.99 (0.93-4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.39 (0.09-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.66 (0.28-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.79 (0.12-5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.75 (0.32-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.88 (0.38-2.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.50 (0.04-6.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.20 (0.48-2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.15 (0.95-4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.18 (0.49-9.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e18.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.57 (0.05-7.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e5.02 (0.22-115.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.98 (1.14-7.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75 (0.12-4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.92 (0.31-2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.69 (0.24-12.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.31 (0.45-3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.38 (1.58-12.13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n 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style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWidow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.54 (0.16-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e180116554.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.98 (0.29-3.36)\u003c/p\u003e\n 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style=\"width: 66px;\"\u003e\n \u003cp\u003e0.75 (0.21-2.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.678 (0.122-23.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.43 (0.39-5.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.77 (0.23-2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6.10 (0.06-645.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e4.609E+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e71737837.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompleted education\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(In years)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eREF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1-5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.17 (0.51-2.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.04 (0.18-6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.34 (0.50-3.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.37 (0.25-7.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.27 (0.49-3.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.60 (0.66-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e2.12 (0.24-18.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.02 (0.37-2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.29 (0.11-0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.41 (0.04-4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6-8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.81 (0.48-6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.95 (0.34-25.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.45 (0.07-2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.21 (0.27-5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.09 (0.24-4.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.47 (0.08-2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.64 (0.15-2.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e2.889E+14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e10.77 (0.36-321.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e=\u0026gt;9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.86 (0.32-10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1.44 (0.23-9.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.96 (0.43-20.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.77 (0.43-17.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.54 (0.07-4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.65 (0.10-4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e15.10 (0.27-840.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*REF- Reference; CRD- Chronic Respiratory Disease; APD- Acid Peptic Disease; VI- Visual Impairment; HI- Hearing Impairment; CKD- Chronic Kidney Disease\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis observational study demonstrates a significant burden of chronic diseases within the investigated population. The high prevalence of arthritis, hypertension, acid peptic illness, chronic back pain, hearing impairment, and visual impairment underscores the necessity for focused healthcare interventions especially for chronic conditions. The study findings illuminate the distribution of chronic conditions and their associations with various demographic characteristics, potentially informing public health strategies and geriatric policy.\u003c/p\u003e\u003cp\u003eOur observed prevalence of osteoarthritis (48.21%) is lower than the 70.4% reported by Barua \u003cem\u003eet al.\u003c/em\u003e (Assam) but aligns with the 44.72% prevalence documented by Banerjee \u003cem\u003eet al.\u003c/em\u003e (Pune). This disparity highlights the potential influence of geographic location on osteoarthritis prevalence. The prevalence of hypertension (23.81%) in our study was lower compared to studies by Agrawal \u003cem\u003eet al.\u003c/em\u003e (41.1%), Sharma \u003cem\u003eet al.\u003c/em\u003e (40.5%) in Himachal Pradesh, and Kumar \u003cem\u003eet al.\u003c/em\u003e (38.1%) in Uttar Pradesh. Similarly, our observed prevalence of Diabetes Mellitus (7.14%) was lower than that reported by Kumar \u003cem\u003eet al.\u003c/em\u003e (36.52%) in Kerala.\u003c/p\u003e\u003cp\u003eOur study identified a prevalence of Acid Peptic Disease mirroring the findings of Gupta \u003cem\u003eet al.\u003c/em\u003e (Shimlapuri) who observed a high prevalence of 38.6%. However, it differed from Joshi \u003cem\u003eet al.\u003c/em\u003e (Chandigarh) who reported a lower prevalence of 14.5%. Notably, our study identified a higher prevalence of hearing impairment (32.14%) compared to Singh Bali \u003cem\u003eet al.\u003c/em\u003e (25.4%) in Haryana. Conversely, the prevalence of Visual Impairment in our study (19.64%) was lower than that reported by Das \u003cem\u003eet al.\u003c/em\u003e (51.27%) These findings suggest a need for further research into the reasons behind such variations. Age-related hearing loss is a well-established phenomenon, but other factors like exposure to loud noises may also play a role. Similarly, Visual Impairment prevalence can be influenced by access to healthcare and preventative measures like cataract surgery. The prevalence of respiratory diseases in our study population (4.76%) was lower compared to the findings from Sinha \u003cem\u003eet al.\u003c/em\u003e (18.33%) in Odisha. Our study identified a relatively low prevalence of mental health conditions (6.55%) in our study population. However, this prevalence increased significantly within the ageing population, as reported by Jana \u003cem\u003eet al.\u003c/em\u003e (31.23%). These findings suggest a potential underestimation of mental health issues within the general population, particularly in the context of social stigma.\u003c/p\u003e\u003cp\u003eThese observations in chronic disease prevalence across different regions can potentially be attributed to several factors. These factors may include cultural influences, dietary patterns, behavioral habits, racial backgrounds, variations in study settings, diversities in sampling techniques employed, and underlying genetic predispositions. This study further emphasizes the established association between advancing age and an increased risk of developing various chronic conditions within the investigated population. The high prevalence of chronic conditions underscores the critical need for a robust system of continuity of care to ensure optimal health outcomes for this vulnerable population. Electronic Medical Records (EMRs) can play a transformative role in facilitating continuity of care. Implementing standardized EMR systems across old-age homes and linking them with nearby Ayushman Arogya Mandir (formerly known as Health \u0026amp; Wellness Centres), would enable healthcare providers to access a resident's complete medical history. This information exchange would be particularly crucial for geriatric care, where residents often manage multiple chronic conditions. Effective chronic condition follow-up plans could be established, ensuring timely medication refills, monitoring of vital signs, and early detection of potential complications.\u003c/p\u003e\u003cp\u003eFurthermore, fostering collaboration between old-age homes and community health workers (CHWs) could significantly improve chronic disease management. CHWs, residing within the community, can provide crucial support with medication adherence, conduct regular health screenings, and offer education on managing chronic conditions. This collaborative approach would not only improve healthcare access for residents but also potentially reduce the burden on already strained healthcare facilities.\u003c/p\u003e\u003cp\u003eThis study, which focuses on the burden of chronic disease and the impact of socio-demographic variables, sheds light on the senior population's overall health status. These findings underline the need for a comprehensive population-based strategy to promote healthy aging and increase quality of life. However, our study is constrained by self-report data, which may result in recollection bias and so weaken genuine prevalence. Furthermore, the cross-sectional nature of this study made it difficult to prove causality.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study provides important insights into the chronic disease burden among older adults in institutional care in Odisha, India. The high prevalence of arthritis, hearing impairment, and chronic backache, along with substantial multimorbidity, highlights the complex healthcare needs of this population. These findings underscore the urgent need for comprehensive geriatric care protocols, specialized healthcare services, and policy interventions to address the unique challenges faced by older adults in institutional settings.\u003c/p\u003e\u003cp\u003eAs India's population continues to age and institutional care becomes more prevalent, understanding and addressing the health needs of old age home residents becomes increasingly important. This study contributes to the evidence base for developing targeted interventions and policies to improve the health outcomes and quality of life for this vulnerable population.\u003c/p\u003e\u003cp\u003eThe findings emphasize that chronic disease management in institutional settings requires a multidisciplinary approach involving healthcare professionals, facility staff, policymakers, and families. By addressing these challenges proactively, we can work toward ensuring that older adults in institutional care receive the comprehensive, compassionate care they deserve.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.G. and A.S. conceived the study concept and design. S.G. coordinated fieldwork and data collection. K.C.S. and S.K. provided methodological input and assisted with statistical analysis. S.G. and A.S. interpreted the findings. S.G. drafted the initial manuscript. S.Pa. provided critical revisions for intellectual content and ensured policy relevance. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCONFLICTS OF INTEREST\u003c/strong\u003e: None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e: None\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgeing and health India [Internet]. www.who.int. Available from: https://www.who.int/india/health-topics/ageing\u003c/li\u003e\n \u003cli\u003eGiridhar G, Bhat TN, Gulati SC, Verma S. The Status of Elderly in Odisha. UNFPA; 2011\u003c/li\u003e\n \u003cli\u003eBarua K, Borah M, Deka C, Kakati R. Morbidity pattern and health-seeking behavior of elderly in urban slums: A cross-sectional study in Assam, India. Journal of Family Medicine and Primary Care [Internet]. 2017;6(2):345. Available from: https://dx.doi.org/10.4103%2F2249-4863.220030\u003c/li\u003e\n \u003cli\u003eAWarbhe, P., \u0026amp; Rupesh, W. (2015). Morbidity profile, health seeking behaviour and home environment survey for adaptive measures in geriatric population-Urban community study. International Journal of Medical Research \u0026amp; Health Sciences, 4(4), 778. https://doi.org/10.5958/2319-5886.2015.00153.8\u003c/li\u003e\n \u003cli\u003eSharma, D., Mazta, S., \u0026amp; Parashar, A. (2013). Morbidity pattern and health-seeking behavior of aged population residing in Shimla hills of north India: A cross-sectional study. Journal of Family Medicine and Primary Care, 2(2), 188. https://doi.org/10.4103/2249-4863.117421\u003c/li\u003e\n \u003cli\u003eBanerjee, A., Nikumb, V., \u0026amp; Thakur, R. (2013). Health Problems Among the Elderly: A Cross-Sectional Study. Annals of Medical and Health Sciences Research, 3(1), 19. https://doi.org/10.4103/2141-9248.109466\u003c/li\u003e\n \u003cli\u003eGupta, A., Girdhar, S., Chaudhary, A., Singh Chawla, J., \u0026amp; Kaushal, P. (2016). PATTERNS OF MULTIMORBIDITY AMONG ELDERLY IN AN URBAN AREA OF NORTH INDIA. \u003cem\u003eJournal of Evolution of Medical and Dental Sciences\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(19), 936\u0026ndash;941. https://doi.org/10.14260/jemds/2016/218\u003c/li\u003e\n \u003cli\u003eJoshi, K., Kumar, R., \u0026amp; Avasthi, A. (2003). Morbidity profile and its relationship with disability and psychological distress among elderly people in Northern India. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(6), 978\u0026ndash;987. https://doi.org/10.1093/ije/dyg204\u003c/li\u003e\n \u003cli\u003eKumar, S. Ganesh., Das, R. Anil., \u0026amp; Roy, G. (2017). Morbidity pattern and its relation to functional limitations among old age rural population in Kerala, India. \u003cem\u003eJournal of Family Medicine and Primary Care\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 301. https://doi.org/10.4103/2249-4863.220015\u003c/li\u003e\n \u003cli\u003eSingh Bali, S., Rafiq, R., Mir, M., Bhat, A., \u0026amp; Jan, Y. (2017). Health Problems Among Rural Elderly Population of Ambala, Haryana:A Cross Sectional Study. \u003cem\u003eIOSR Journal of Dental and Medical Sciences\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 68\u0026ndash;71. https://doi.org/10.9790/0853-1605076871\u003c/li\u003e\n \u003cli\u003eDas, S., Sinha, A., Srikanta Kanungo, \u0026amp; Pati, S. (2024). Decline in unmet needs for cataract surgery among the ageing population in India: findings from LASI, wave-1. Frontiers in Health Services, 4. https://doi.org/10.3389/frhs.2024.1365485\u003c/li\u003e\n \u003cli\u003eJana, A., Verma, P., Sinha, A., Srikanta Kanungo, \u0026amp; Pati, S. (2023). Prevalence, correlates, and treatment gap of mental illnesses among middle age and elderly population of India. International Journal of Noncommunicable Diseases, 8(4), 197\u0026ndash;205. https://doi.org/10.4103/jncd.jncd_80_23\u003c/li\u003e\n \u003cli\u003ePati, S., Agrawal, R., Sinha, A., Jogesh Murmu, \u0026amp; Srikanta Kanungo. (2023). Uncovering the hidden epidemic: Prevalence and predictors of undiagnosed hypertension among older adults in India. International Journal of Noncommunicable Diseases, 8(3), 157\u0026ndash;157. https://doi.org/10.4103/jncd.jncd_69_23\u003c/li\u003e\n \u003cli\u003eRitik Agrawal, Jogesh Murmu, Abhinav Sinha, Srikanta Kanungo, \u0026amp; Sanghamitra Pati. (2023). Association of dietary sodium intake and hypertension among older adults in India: Insights from (Study on global AGEing and adult health) SAGE wave-2 (2015\u0026ndash;16). Clinical Epidemiology and Global Health, 23(101358-). https://doi.org/10.1016/j.cegh.2023.101358\u003c/li\u003e\n \u003cli\u003eSinha, A., Pritam, J. A., Jain, H. K., Giri, S., Pati, S., \u0026amp; Kshatri, J. S. (2024). Seasonal variations in respiratory morbidity in primary care and its correlation with the quality of air in urban Odisha, India. PLOS Global Public Health, 4(1), e0002313. https://doi.org/10.1371/journal.pgph.0002313\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ageing-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agin","sideBox":"Learn more about [Ageing International](http://link.springer.com/journal/12126)","snPcode":"12126","submissionUrl":"https://submission.springernature.com/new-submission/12126/3","title":"Ageing International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Elderly, chronic diseases, old-age homes, multimorbidity, India, institutional care","lastPublishedDoi":"10.21203/rs.3.rs-7363574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7363574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eIndia's aging population increasingly relies on institutional care, yet limited research exists on the health profile of old-age home residents. Understanding chronic disease patterns in these settings is crucial for developing appropriate healthcare strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo assess the prevalence of chronic conditions and identify associated sociodemographic factors among older adults residing in old-age homes in Odisha, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study included 168 residents aged ≥60 years from eight old-age homes across six districts of Odisha, selected through cluster random sampling. We used the validated Multimorbidity Assessment Questionnaire for Primary Care (MAQ-PC) to assess self-reported chronic conditions. We calculated descriptive statistics for prevalence estimates and used logistic regression analysis to examine associations with sociodemographic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe study population had a mean age of 71.3 years (SD±8.7), with 61.9% females. Arthritis was the most prevalent condition (48.2%, 95% CI: 40.7-55.8%), followed by hearing impairment (32.1%, 95% CI: 25.2-39.7%), chronic backache (29.2%, 95% CI: 22.5-36.5%), and hypertension (23.8%, 95% CI: 17.6-30.9%). Individuals aged ≥80 years showed significantly higher odds of arthritis (AOR: 2.98, 95% CI: 1.14-7.80) and chronic backache (AOR: 4.38, 95% CI: 1.58-12.13). Multimorbidity was present in 67.3% of residents, with an average of 2.4 chronic conditions per person.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study reveals high prevalence of chronic conditions among old-age home residents in Odisha, with musculoskeletal disorders and sensory impairments being predominant. These findings highlight the need for specialized geriatric care standards and targeted healthcare services in institutional settings. However, results should be interpreted cautiously due to reliance on self-reported data and potential selection bias inherent in institutional care settings.\u003c/p\u003e","manuscriptTitle":"Profile of Chronic Conditions among Older Adults in Old-age Homes: A Cross-sectional Study from Odisha, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 08:24:06","doi":"10.21203/rs.3.rs-7363574/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-01T14:28:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316849180862580735546845729629369898564","date":"2025-11-01T14:24:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T14:44:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"232728947435212497518611382871086064611","date":"2025-10-20T01:12:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-14T17:07:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-19T06:49:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-19T06:46:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ageing International","date":"2025-08-13T09:49:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ageing-international","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agin","sideBox":"Learn more about [Ageing International](http://link.springer.com/journal/12126)","snPcode":"12126","submissionUrl":"https://submission.springernature.com/new-submission/12126/3","title":"Ageing International","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"153fae8a-ea10-417b-8fc3-24f1d6ded1b6","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-14T17:23:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 08:24:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7363574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7363574","identity":"rs-7363574","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}