Determinants of Functional Disability in India: The Interplay of Health Conditions, Environmental and Personal Factors

Determinants of Functional Disability in India: The Interplay of Health Conditions, Environmental and Personal Factors | 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 Determinants of Functional Disability in India: The Interplay of Health Conditions, Environmental and Personal Factors Trupti, Arnab Jana, Sarthak Gaurav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7692267/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Understanding the relationship between health conditions, particularly non-communicable diseases (NCDs), environmental and personal factors, and functional disability is crucial to achieve the SDG aim - "leaving no one behind." However, these risk-outcome pathways remain understudied. This study assesses the relationship between NCDs, environmental factors (social capital), and personal factors with functional disability (ADL: Activities of Daily Living, IADL: Instrumental Activities of Daily Living) in the middle and elderly population (45+ years). It adopts the International Classification of Functioning, Disability, and Health (ICIDH-2) framework, used Longitudinal Ageing Study in India (LASI,2017-18), and performed Multinomial Logistic Regression and Multiple Indicators Multiple Causes Model for middle-and-old population (45+ years). The findings suggest that hypertension (27.3%) was the most prevalent condition, and stroke increased the likelihood of disability. However, hypertension is a precursor to stroke, highlighting the need to target hypertension. NCDs are a significant predictor of disability, with social capital having a protective effect of reducing the likelihood of disability, and personal factors mediate to contribute to the disability burden. Disability is prevalent among vulnerable groups-older adults, women, and individuals with low wealth, limited education, and rural residence. However, these relationships are bidirectional, as disability may also limit social participation, further exacerbating low social capital and high NCDs. It highlights the need to effectively implement WHO Best Buys to address NCD, specifically hypertension, and adopt bottom-up strategies to strengthen social capital through community empowerment, trust-building, and participatory engagement. Lastly, expanding health, education, and social security programs would ensure adequate support for vulnerable populations. Health Policy Health Economics and Outcomes Research Ageing Disability NCDs Hypertension LASI-India Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Globally, 16% of the population lives with a disability, and non-communicable diseases (NCDs) contribute to more than 70% of disability (Global Burden of Disease Study 2019, 2020; WHO, 2022). NCDs contribution to disability-adjusted life years increased from 43·2% in 1990 to 63·8% in 2019 (Global Burden of Diseases, 2020). The population with a disability is growing due to the aging population, rise in NCDs, changing trends in the environment, and other personal factors (WHO & The World Bank, 2011). Disability multiplies risks across social determinants and contributes to poorer health outcomes, less economic participation, high rates of poverty, dependency, and restricted participation for the population with disability (WHO & The World Bank, 2011). In India, around 5-8% of the population (around 55-90 million) are experiencing disability (Human Development Unit South Asia Region, 2009), and NCD contributed to a 40 % increase in the population with disability during 1990-2000 (Human Development Unit South Asia Region, 2009). In India, the population with a disability has a higher vulnerability to age-related conditions, secondary diseases, social marginalization, violence, unintentional injury, and death (WHO & The World Bank, 2011). Disability results from complex interactions among individuals' health conditions and environmental and personal factors (WHO & The World Bank, 2011). In India, around 55% (2016) of disability-adjusted life years (DALYs) are attributable to NCD (Jeemon et al., 2019). Environmental factors are the physical, social, and attitudinal settings where the person lives and acts, i.e., technology, natural environment, relationships, attitudes, social systems, and policies (WHO & The World Bank, 2011). They are external to the individual and either enable or challenge the individual's performance or capacity (WHO, 2001). Personal factors comprise an individual's background, including age, sex, personality, and education level, enabling them to act and modify environmental factors and disability and vice versa (WHO & The World Bank, 2011). India is undergoing a demographic and social transition through which the institutions, i.e., social welfare organizations and community support networks, will adapt and develop different approaches to social care, relationships, and support (Kirk, 1996). In India, families are seen as prime social capital in the ageing population, and most are well-connected to immediate and extended social networks (Berkman et al., 2012). However, the social capital has declined, and the traditional extended family structure has been disintegrated due to urbanization, with children residing away due to education, work, and marriage weakening the social capital (IIPS et al., 2020a). The quality, quantity, and degree of social networking reduce disability; however, there is limited evidence of this in India (Lestari et al., 2019). Similarly, the transitions will also result in an increasingly aging population, changes in wealth status, health access and affordability, and social-cultural mindsets (WHO & The World Bank, 2011). Moreover, the cause of disabilities is shifting to an often unknown mixed set of causes, indicating the knowledge gaps in current disability research (Human Development Unit South Asia Region, 2009). In India, studies have examined the risk factors associated with functional disability among the ageing rural population, across genders, States and also examined the multi-morbidity combination with disability (Himanshu & Arokiasamy, 2021; Malik, 2022; Kumar et al., 2023; Halder et al., 2024). However, the risk-outcome relationship between health conditions, personal and environmental factors, and functional disability remains understudied. This study moves beyond identifying risk factors to map the problem, measure the strength of risk-outcome pathways, and understand mechanisms that increase disability for a targeted equitable policy responses. In the policy context, India has several initiatives to promote the rights of persons with disabilities. It has achieved significant progress in reducing disability due to polio and leprosy; however, it faces considerable challenges in addressing NCD-related disability (Human Development Unit South Asia Region, 2009). The overarching Ministry of Social Justice and Empowerment (MSJE), the nodal agency for disability, has limited resources, lower convening power, and weak coordination (Human Development Unit South Asia Region, 2009). Furthermore, the ambitious commitment, low institutional capacity, poor inter-sectoral coordination, lack of sanctions for non-compliance, inadequate sub-national implementation, limited involvement of non-governmental actors, including people with disabilities, NGOs, PWDs, and PRIs, and poor social attitude and awareness add to the insufficiency (Human Development Unit South Asia Region, 2009). Concurrently, social protection and health insurance policy interventions have low impacts on the poor population with disability as they offer low coverage, limited financial protection, and weak channels for increasing demand (Human Development Unit South Asia Region, 2009). Hence, India will likely fall short of achieving SDGs, the 2030 agenda aim of "leave no one behind" as it requires the inclusion and participation of persons with disabilities. This study is critical to understanding the risk factors that drive functional disability in the middle and elderly population (45+ years). It hypothesizes that NCDs, environmental factors (social capital), and personal factors influence functional disability. Specifically, from a policy standpoint, it can answer the question: Do NCDs, social capital, and personal factors contribute to functional disability. If yes, which components of the NCDs, social capital, and personal factors should policies target? It adopts the International Classification of Functioning, Disability, and Health framework. It conducts Confirmatory Factor Analysis (CFA) to validate the underlying structure of functional disability and the Multiple Indicators Multiple Causes Model (MIMIC) to examine the direct influence of NCDs, social capital, and personal factors. The CFA-MIMIC approach would identify specific components that policy interventions should target to mitigate functional disability. Data and Methods Conceptual Framework Disability is an impairment in body function or structure, a limitation in activity, and a restriction in participation (World Health Assembly 66, 2013). The present study adopted the International Classification of Functioning, Disability, and Health (ICIDH-2) framework for understanding disability (WHO, 2001). The bidirectional arrows indicate mutual interaction across all latent factors and explanatory variables (WHO, 2001). Disability is a gap between personal capability and environmental demand (Verbrugge & Jette, 1994). It follows a sequential three-step pathway , wherein poor health conditions contribute to structural impairments, which, in turn, restrict an individual’s ability to perform activities and participate in society, ultimately leading to functional disability (Verbrugge & Jette, 1994). However, this pathway is not linear; it is shaped by bidirectional interactions among various latent constructs, as represented by the arrows in Figure 1. The influence of these factors is dynamic, meaning they may exacerbate, mitigate, or even compensate for disability (WHO, 2001). NCDs (Health condition) : They represent the individual's health based on self-reporting rather than medical assessment (WHO, 2001) Social capital (Environmental factors) : The individual's external physical, social, and attitudinal environment that positively or negatively influences the body function (WHO, 2001). The social and cultural norms and values of society that govern interactions among people and the institutions in which they are embedded (Grootaert, 2001) Personal factors : These include the background of an individual's life, as well as personal characteristics like age, sex, wealth status, and education level (WHO, 2001). While personal factors are not classified in ICIDH, they impact the outcome of other latent constructs (WHO, 2001). By integrating these elements, the conceptual framework provides a holistic perspective on functional disability as a product of socio-environmental interactions, highlighting the need for multi-sectoral interventions that address individual health conditions and structural determinants through healthcare, social, and policy-level approaches. Study Design and sample A cross-sectional design from the Longitudinal Ageing Study in India (LASI, 2017-19) was utilized for this study. LASI is a comprehensive, nationally representative survey investigating the health, economic, and social determinants and consequences of aging in India (IIPS et al., 2020b). Out of a 72,250-sample size, the population under 45 years was removed, and 65,562 middle and elderly adults (45+ years) were selected. Variable Description The constructs were functional disability, non-communicable diseases, social capital, and other covariates using the ICIDH-2 framework (WHO, 2001) Functional Disability : Functional disability is a multi-dimensional construct comprising two factors: limitations in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). ADL limitations included dressing, walking across a room, and bathing, eating, getting in or out of bed, and using the toilet. IADL limitations included preparing a hot meal, shopping for groceries, making telephone calls, taking medicine, doing housework, managing money, and navigating unfamiliar locations. For ADL and IADL, responses were categorized as 0 = able to perform all activities, 1 = unable to perform one activity, 2 = unable to perform two, and 3 = unable to perform three or more activities. Non-communicable diseases : NCD status was a 13-factor model based on self-reported diagnoses by a healthcare professional for 13 conditions: hypertension, diabetes, cancer, chronic lung disease, chronic heart disease, stroke, arthritis, neurological disorders, high cholesterol, thyroid disorders, gastrointestinal problems, skin diseases, and urogenital conditions. Responses were categorized as 0= No and 1= Yes. Social Capital : Social capital was conceptualized as a four-factor model derived from self-reported activities: visiting relatives/friends, attending cultural performances/shows/cinema, participating in religious functions/events such as bhajan/ satsang/ prayer, and attending political/community/organization group meetings. Responses were coded as 0 = low, 1 = medium, and 2 = high. The values for all the latent variables were obtained through data imputation (by regression) on the measurement model. Covariates : The control variables were age (continuous), sex (0 = male, 1 = female), education level (0 = less than secondary, 1 = secondary and higher), wealth quintiles (1 = poorest, 2 = poorer, 3 = middle, 4 = richer, 5 = richest), and type of residence (0 = urban, 1 = rural) as control variables (Refer to Annexure 1 , Table S1 for measurements) Statistical analysis The analysis included the following statistical analyses, and each method offers unique strengths, making their combined use essential. First, a multinomial logistic regression was used to measure the association of NCDs, social capital, and personal factors with functional disability status, i.e., basic activities of daily living (ADLs) and instrumental activities of daily living (IADLs). However, multinomial logistic regression has limitations such as sensitivity to outliers, collinearity issues, and overfitting, which can compromise the stability and accuracy of parameter estimates (Meloun & Militký, 2011). Given multinomial logistic regression's limitations, this study employed factor analysis to validate the measurement structure of latent constructs. It adopted Confirmatory Factor Analysis (CFA) over Exploratory Factor Analysis (EFA) as CFA was more appropriate for testing a predefined factorial structure based on the ICIDH-2 framework. Unlike EFA, which identifies factor structures without prior assumptions, CFA allows for evaluating whether the observed data aligns with the expected constructs of functional disability (Wells, 2021). Additionally, the CFA's ability to test measurement invariance ensured the stability of this structure across demographic groups (Wells, 2021). The model fit was assessed using the Root Mean Square Error of Approximation (RMSEA< 0.08), Comparative Fit Index (CFI ≥ 0.90), and Tucker-Lewis Index (TLI ≥ 0.90) (37–39) (Wells, 2021). However, CFA models may face identification issues , particularly in complex frameworks, where parameter estimates can remain unstable despite appearing correctly specified (Wells, 2021). Additionally, CFA’s reliance on fit indices increases the risk of overfitting , as adjustments to improve model fit may compromise theoretical validity (Wells, 2021). It employs the Multiple Indicators Multiple Causes Model (MIMIC) to address these limitations. By incorporating covariates directly, MIMIC enhances identification and reduces the need for excessive model adjustments, improving stability and theoretical coherence (Lee et al., 2013). Figure 2 is a conceptual framework for the MIMIC model to validate the hypothesized relationship between NCDs, social capital, personal factors, and functional disability. The three unobserved latent variables (Oval) are functional disability, NCD, and social capital. The bold black arrows show the relationship between functional disability, NCD, and social capital. The observed measured indicators (rectangles) that define the latent variables (ovals) with grey arrows. The observed exogenous variables (covariates) are age, sex, wealth status, education level, and type of residence. The analysis consisted of the simultaneous estimation of the three following regression models: (i) Regression models consisting of three correlated latent factors Functional Disability ~ ADL + IADL Social capital ~ Attend political meetings + Attend religious functions + Attend cultural events + Visit relatives NCDs ~ Hypertension + Diabetes + Cancer + Chronic lung cancer + Chronic heart disease + Stroke + Arthritis + High Cholesterol + Thyroid Disorder + Gastrointestinal Problem + Skin disease + Urogenital condition + Neurological Disorders (ii) Regression of explanatory variables on the latent factors: Functional Disability + Social Capital + NCDs ~ Age + Sex + Wealth + Education level +Residence These models ensured a comprehensive analysis that integrated predictive relationships, validated latent constructs, and accounted for covariate effects, thereby enhancing the robustness and reliability of the findings(Meloun & Militký, 2011; Lee et al., 2013; Wells, 2021). Results Background characteristics of the middle and elderly adults The mean age of this study population was 59.7 years, with 53.5% women. The wealth index was divided into quintiles, with the most significant proportion in the poorer group (21.2%), followed by the poorest (20.9%), middle (20.5%), richer (19.4%), and richest (18.0%). Around 68% of the population had secondary and higher education, and 32.4% lived in urban areas. Overall, the population had more IADL than ADL impairments. Rates of having one, two, three, or more ADLs were 7.4%, 4.2%, and 5.4%, whereas rates of having one, two, and three or more IADLs were 10.4%, 7.0%, and 20.1%, respectively. Around 43% had no NCDs, whereas 29.5% had one, 16.7% had two, 7.2% had three, and 3.6% had four or more NCDs. The population had the highest prevalence of hypertension (27.5%), followed by gastrointestinal problems (18.1%), arthritis (16.3%), and diabetes (12.3%). Within social capacity, 13.9% frequently visited relatives and friends, 2.4% regularly attended cultural performances, 8.7% participated in religious, and 1.7% often attended group meetings. ( Refer to Annexure 3, Table S2 ) Figure 3 shows the prevalence and proportions of multi-morbidity for the 13 NCDs. The most common NCD was hypertension (27.3%), followed by gastrointestinal problems (18.1%), arthritis (16.3%) and diabetes (12.3%). Chronic lung disease, skin disease, chronic heart disease, thyroid disorder, neurological disorders, high cholesterol, and stroke showed prevalence rates from 6.7% to 1.9%, whereas urogenital conditions and cancer were less frequently reported (≤ 1.0%). Multinomial Logistic Regression Figure 4 represents the heat map for the Multinomial Logistic Regression analysis results with the base outcome as no ADL/no IADL having three reference categories, i.e., 1 ADL/ 1 IADL; 2 ADL/ 2 IADL; 3+ ADL/ 3+ IADL. Model 1 examined the relationship between ADL and NCDs, social capital, and personal factors, while Model 2 examined these factors concerning IADL. Across both models, NCDs increase the likelihood of ADL and IADL, higher social capital reduces its likelihood, and personal factors show mixed results. Within the NCDs, stroke had the highest likelihood of being associated with ADL and IADL, followed by neurological disorders, arthritis, and urogenital conditions. High cholesterol increased the likelihood of 1ADL and 3+ IADL; however, it reduced the likelihood of 2 ADL and 3+ADL. Most social capital activities reduce ADL and IADL likelihood; however, attending religious functions slightly increases it. Within personal factors, increasing age and being a woman increased the likelihood of ADL and IADL, whereas population belonging to higher wealth status and living in urban population reduce the likelihood of ADL and IADL. Lastly, the population with secondary and higher education decreased the likelihood of ADL; however, it increased the likelihood of IADL. Confirmatory factor Analysis Figure 5 depicts factor loadings for NCDs, social capital and functional disability. Hypertension is the strongest NCD indicator, and cancer is the weakest. Attending cultural events has the strongest correlation with social capital, followed by attending political meetings, religious functions, and relatives. Lastly, functional disability is associated with IADL, followed by ADL. This trend illustrates the hierarchical nature of functional abilities, with IADLs encompassing complex cognitive and motor functioning usually impacted before ADLs. Multiple Indicators Multiple Causes (MIMIC) model Figure 5 illustrates the path diagram that serves as the foundation for the analysis of the intricate relationship between functional disability, NCDs, social capital, and personal factors, i.e., covariates (age, sex, wealth, education, and residence). The final model demonstrated adequate goodness-of-fit: CFI = 0.838, TLI = 0.805, and RMSEA = 0.029. The impact of NCDs was higher on functional disability than social capital. The relationships are two-way, suggesting functional disability even modifies the NCDs and social capital. However, this association should be interpreted with caution, as individuals with functional disability due to chronic conditions may face barriers in participating in social activities and meetings, potentially influencing the observed relationship. The relationship between NCDs and social capital was minimal. All the covariates were significantly correlated with each other. It also had a significant positive effect on NCDs. Older age, being a woman, and having low education were associated with lower social capital, while higher wealth and living in rural areas were linked to greater social capital. Similarly, older age, being a woman, and having low education were associated with higher functional disability, whereas higher wealth and living in urban areas were linked to lower functional disability. Thus, the model indicates a bi-directional complex interplay where NCDs exacerbate functional disability, social capital has a protective influence, and personal factors mediate the relationship. (Refer to Annexure 2 Table S3 for Regression weights; and Annexure 2 Table S4 for Covariance) Discussion The present study provides evidence on the risk-outcome relationship between linkages of NCDs, social capital, and personal factors with functional disability in the middle-and-elderly population (45 + years) in India using the ICIDH-2 framework, LASI (2017-18) data, and Multinomial Logistic Regression and MIMIC model. It has the following two key evidence. First, hypertension (27.3%) was the most prevalent; however, stroke increased the likelihood of functional disability. Other studies also report that hypertension is the most common multi-morbidity pattern and directly contributes to 57% of strokes (Gupta, 2004 ; Zhang et al., 2022 ), making hypertension a primary target for prevention. Second, NCDs are a significant predictor of functional disability, with social capital having a protective effect of reducing the likelihood of functional disability, and personal factors mediate to contribute to the functional disability burden directly and indirectly. Functional disability is more prevalent in women, ageing, and poor populations with less than secondary education living in rural areas. Other studies had similar results: NCDs increased functional disability risk, and community health programs for targeted social interactions, which highlight the importance of community engagement, prevented the onset of functional disability in the elderly population (Hikichi et al., 2015 ; Skou et al., 2022 ). In low-income countries, functional disability disproportionally affects vulnerable populations- people from the poorest wealth quintile, low education, older population, women, and those in ethnic minority groups (WHO & The World Bank, 2011). NCDs are associated with an increased likelihood of functional disability, with hypertension being the most prevalent and stroke having the highest contribution. All the personal factors had positive yet minimal effects associated with NCDs, suggesting that NCDs are prevalent across all socio-economic groups. Various risk factors contribute to increasing NCD-related functional disability- growing aging population, urbanization, changing lifestyle, metabolic syndrome, intrauterine malnutrition followed by calorie-rich food in later years, and environmental toxins (Jeemon et al., 2019 ). The National Program for Prevention and Control of Cancer, Diabetes, CVD, and Stroke (NPCDCS, 2010) aims for the prevention and management of NCDs by generating awareness of behaviour and lifestyle changes, and screening, early diagnosis, referral and treatment (MOHFW, 2013 ). Ayushman Bharat Abhiyan provides a health insurance plan (INR 5 Lakh/ annually) for secondary and tertiary care per family. However, the Indian system faces challenges, including inadequate health financing, human resources, surveillance systems, poor access services, and high out-of-pocket expenditure (Jeemon et al., 2019 ). Hence, India needs to shift its NCD approach from a 'cure-based reactive model' to a 'care-based proactive healthcare model' (Jeemon et al., 2019 ), emphasising preventive measures and early intervention. With the demographic, social, and epidemiological transition, the NCDs will continue rising, and social capital will likely reduce, highlighting the need to prepare for the rising functional disability rate, which has significant implications for health, the economy, and society. While India's Rights of Persons with Disabilities Act (RPWD, 2016) aimed for equitable opportunities for the population with disabilities, it did not have a desirable impact and remains inadequate to augment their quality of life (National Institute of Urban Affairs (NIUA) & Department for International Development (DfID-UK), 2020). Recently, WHO established the World Rehabilitation Alliance (WRA, 2022) to optimize functioning in everyday life, ensure equitable access through universal health coverage, and integrate rehabilitation services across secondary and primary healthcare to reduce disability (WHO, 2023 ). It generates demand and mobilizes political will through primary care, workforce, external relations, emergencies, health systems, and policy research (WHO, 2023 ). India can adopt key components identified as good practices at both the systems and service levels to create a disability-inclusive health system (Kuper et al., 2024 ). At the systems level, this includes (1) ensuring enforceability and accountability through country-specific laws and policies, (2) representing disability within institutions to improve leadership, (3) providing health financing and insurance coverage to strengthen financial protection, and (4) conducting routine monitoring and evaluation to generate evidence for decision-making and improved service delivery (Kuper et al., 2024 ). At the service level, essential components to improve demand and supply include: (1) increasing awareness and ensuring autonomy in healthcare decision-making; (2) improving healthcare affordability; (3) ensuring the availability of an adequately skilled healthcare workforce; (4) developing accessible healthcare infrastructure; and (5) incorporating technology-assisted solutions to enhance service delivery (Kuper et al., 2024 ). Adopting these practices can help India build a more inclusive healthcare system that addresses gaps in disability-related care. Social capital reduces the likelihood of disability. It aligns with Nussbaum's (2006) capability approach that suggests that disability is not merely a medical condition (Nussbaum, 2006 ; Harnacke, 2013 )​. It arises from systemic deficiencies in providing necessary capabilities brought on by socio-environmental restrictions that prevent people from interacting and contributing to society (Nussbaum, 2006 ; Harnacke, 2013 )​. Social capital improves health and health knowledge, facilitates healthcare access, promotes healthy aging and behaviours, and reduces functional disability (Nussbaum, 2006 ; Harnacke, 2013 ; Simplican et al., 2015 ). It suggests that marginalized populations- older adults, women, and those with higher education are less likely to have social capital, resulting in a higher risk of disability. Evidence from rural India also suggests that populations from poorer households and socially disadvantaged communities experience lower social capital due to limited social networks, reduced trust in institutions, and weaker collective action mechanisms (Krishna, 2007 ; Singh & Gaurav, 2024 ). Recognizing these benefits, India’s National Policy on Senior Citizens ( 2011 ) aims at active aging by encouraging community support systems and social engagement through senior citizens' associations and strengthening family-based care mechanisms (National Policy on Senior Citizens, 2011 ). However, top-down institutional interventions alone may be insufficient for social capital growth (Nussbaum, 2006 ; Krishna, 2007 ; Harnacke, 2013 ). Hence, policies can prioritize bottom-up strategies that empower local communities for participatory engagement (Nussbaum, 2006 ; Krishna, 2007 ; Harnacke, 2013 ). It will ensure that social capital is embedded within social structures and sustained through community-driven efforts rather than external dependency ​(Nussbaum, 2006 ; Krishna, 2007 ; Harnacke, 2013 ). The present study bridges a critical literature gap by examining the risk-outcome relationship between NCDs, social capital, and personal factors with functional disability among middle-and-old aged adults (45 + years) using the first wave of LASI-India and robust models Multinomial Logistic Regression, CFA, and MIMIC each offering unique strengths. Multinomial Logistic Regression facilitated examining predictive relationships, enhancing predictive validity (Meloun & Militký, 2011 ). CFA ensured construct validity by confirming the predefined ICIDH-2 factorial structure and assessing measurement invariance across groups (Wells, 2021 ). The MIMIC model addressed CFA’s limitations by incorporating covariates directly, improving identification, reducing parameter instability, and minimizing overfitting (Lee et al., 2013 ). This integrated approach strengthened theoretical coherence and provided a comprehensive analysis. While these models enhance this study's robustness, a larger sample size is essential to produce reliable Multinomial Logistic Regression and MIMIC estimates (Meloun & Militký, 2011 ; Wells, 2021 ). Notably, it leverages LASI, a nationally representative data source with a large sample size, real-time monitoring, and an automated data quality control protocol, ensuring impeccable state-level data accuracy (Paul & Chandra Sarma, 2024 ). It also establishes a baseline for future research that can utilize longitudinal data from LASI Wave II to track changes in the population over time. This study also has limitations. The statistical methods employed have inherent constraints; Multinomial Logistic Regression is likely to outlier influence, collinearity, and overfitting (Meloun & Militký, 2011 ), while CFA’s reliance on fit indices risks overfitting and may face identification issues in complex frameworks (Wells, 2021 ). Although MIMIC improves identification and handles covariates, it may oversimplify complex interactions and face challenges in capturing bidirectional relationships (Lee et al., 2013 ). Moreover, while MIMIC identifies directional relationships, these findings should be interpreted as correlational rather than causal due to the cross-sectional nature of the LASI data. Reverse causation remains a possibility, as individuals with functional disability due to chronic conditions may face barriers to social participation, potentially influencing the observed outcomes. The LASI questionnaire does not specify whether participants reported difficulty with IADL tasks because they had never engaged in those activities, which may reflect social and gendered roles. Consequently, the analysis assumes that reported difficulty reflects impairment rather than non-engagement, potentially overlooking gendered differences in IADL patterns. Additionally, self-reported variables may have introduced recall and reporting biases, resulting in over- or under-estimation. Future research could improve accuracy by distinguishing between "never did" and "cannot do" responses or exploring role-based exclusions, as suggested by Sheehan & Tucker-Drob, ( 2019 ), to better account for gendered patterns in IADL assessment. It also does not account for factors such as household work, physical activity, unhealthy diet, or stigma, which could influence functional disability outcomes. Further research may benefit from focusing on elderly populations (60 + years), examining gendered differences within marginalized communities, and employing ethnographic approaches to understand how health systems can delay disability onset, improve recovery outcomes, and better manage functional disabilities in India. Conclusion India is undergoing demographic transition, rapid urbanization, industrialization, and modernization with socio-economic change, including family structure and living arrangements, and affecting ideologies of social support. Hence, India provides an attractive setting for determining risk factors associated with functional disability. While studies have examined the risk factors of functional disability, the risk-outcome interplay of health conditions, environment, and personal factors in the middle-and-elderly population (45 + years) is understudied. This study addresses this gap in India by examining the linkages of NCDs, social capital, and personal factors to functional disability. While a majority of the population has hypertension, functional disability has a higher likelihood of being associated with stroke. As hypertension is a precursor for stroke, it remains the critical target for policy action. NCDs are associated with a higher likelihood of functional disability, whereas greater social capital is linked to a lower risk. Personal factors mediate these associations, shaping the functional disability burden both directly and indirectly. Furthermore, the impact of NCDs and limited social capital on functional disability is exacerbated by aging, female gender, low socioeconomic and educational status, and rural residence. However, these relationships are bidirectional, as functional disability itself may further restrict social participation, reinforcing the effects of low social capital and worsening health outcomes. Building on the capability approach, the present study highlights the need to address structural barriers that limit an individual's ability to function and participate fully in society. It emphasizes the need to (1) Effectively implement Best Buys to prevent and manage NCDs, particularly hypertension, (2) Strengthen social capital by adopting bottom-up strategies that empower communities, build trust networks, and foster participatory engagement through community centers, peer-support groups, and intergenerational programs embedded within social structures, and (3) Expand social-safety needs, health and educational services, health insurance, improve awareness and accessibility for the vulnerable population. References Berkman, L. F., Sekher, T. V., Capistrant, B., & Zheng, Y. (2012). Social Networks, Family, and Care Giving Among Older Adults in India. In Aging in Asia: Findings From New and Emerging Data Initiatives (pp. 261–279). 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WHO Press. https://www.who.int/publications/i/item/9789240087378 WHO, & The World Bank. (2011). World report on disability. WHO Press. https://www.who.int/publications/i/item/9789241564182 World Health Assembly 66. (2013). Sixty-sixth World Health Assembly, Geneva, 20–27 May 2013: Resolutions and decisions (No. WHA66/2013/REC/1). WHO Press. https://iris.who.int/handle/10665/150207 Zhang, X., Padhi, A., Wei, T., Xiong, S., Yu, J., Ye, P., Tian, W., Sun, H., Peiris, D., Praveen, D., & Tian, M. (2022). Community prevalence and dyad disease pattern of multimorbidity in China and India: A systematic review. BMJ Global Health, 7(9), e008880. https://doi.org/10.1136/bmjgh-2022-008880 Additional Declarations The authors declare no competing interests. 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04:55:02","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145990,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/1024a52ff64912989545ac73.html"},{"id":92046121,"identity":"1f13dd54-8c84-4a52-97d9-65a9be759ffd","added_by":"auto","created_at":"2025-09-24 04:47:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27991,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of the interplay of factors examined by the analysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/d6c49a950f34f1460792083b.png"},{"id":92046703,"identity":"c42127d1-1f98-4327-979f-e15a8373ae9a","added_by":"auto","created_at":"2025-09-24 04:55:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122838,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual frame depicting the impact of NCDs, social capital, and personal factors on functional disability\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/ed1651ac35c1b5d2e8f8908b.png"},{"id":92046877,"identity":"b8139b61-1f8f-4224-9308-779d55d69254","added_by":"auto","created_at":"2025-09-24 05:03:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54978,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence (per 100 population) and proportions of multi-morbidity\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/fc777704ece6b241dd4e02d7.png"},{"id":92046129,"identity":"798a7524-9012-49b5-b79c-f1832a47a9c6","added_by":"auto","created_at":"2025-09-24 04:47:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":275641,"visible":true,"origin":"","legend":"\u003cp\u003eHeat map of regressions coefficients obtained from multinomial logistic regressions performed between ADL, IADL level and NCDs, social capital, and personal factors\u003c/p\u003e\n\u003cp\u003eNote: The square color represents the direction of correlation (red = negative association, blue = positive association). Darker color indicates higher association in absolute value (*p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/bae347e81e41180a1f4a55d7.png"},{"id":92046134,"identity":"66ee4c61-d768-4093-b9ac-f6fb3db83d57","added_by":"auto","created_at":"2025-09-24 04:47:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149188,"visible":true,"origin":"","legend":"\u003cp\u003eMultiple Indicators Multiple Causes (MIMIC) model showing the impact of NCDs, social capital, and personal factors on functional disability\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/f01b7fb753f022f7128ae697.png"},{"id":92048182,"identity":"10946fbd-dba5-40f4-aeb2-752cd60ea732","added_by":"auto","created_at":"2025-09-24 05:11:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296866,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/1ef3d91d-6a16-4149-8751-58fa8f85a4cd.pdf"},{"id":92046119,"identity":"a573aa9c-4ce4-499a-adda-1184c2fe7ab4","added_by":"auto","created_at":"2025-09-24 04:47:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33586,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7692267/v1/ff9083a127609e13b0fdd6d1.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDeterminants of Functional Disability in India: The Interplay of Health Conditions, Environmental and Personal Factors\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eGlobally, 16% of the population lives with a disability, and non-communicable diseases (NCDs) contribute to more than 70% of disability \u0026nbsp;(Global Burden of Disease Study 2019, 2020; WHO, 2022). NCDs contribution to disability-adjusted life years increased from 43·2% in 1990 to 63·8% in 2019 (Global Burden of Diseases, 2020). The population with a disability is growing due to the aging population, rise in NCDs, changing trends in the environment, and other personal factors (WHO \u0026amp; The World Bank, 2011). Disability multiplies risks across social determinants and contributes to poorer health outcomes, less economic participation, high rates of poverty, dependency, and restricted participation for the population with disability (WHO \u0026amp; The World Bank, 2011). In India, around 5-8% of the population (around 55-90 million) are experiencing disability (Human Development Unit South Asia Region, 2009), and NCD contributed to a 40 % increase in the population with disability during 1990-2000 (Human Development Unit South Asia Region, 2009). In India, the population with a disability has a higher vulnerability to age-related conditions, secondary diseases, social marginalization, violence, unintentional injury, and death (WHO \u0026amp; The World Bank, 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisability results from complex interactions among individuals' health conditions and environmental and personal factors (WHO \u0026amp; The World Bank, 2011). In India, around 55% (2016) of disability-adjusted life years (DALYs) are attributable to NCD (Jeemon et al., 2019). Environmental factors are the physical, social, and attitudinal settings where the person lives and acts, i.e., technology, natural environment, relationships, attitudes, social systems, and policies (WHO \u0026amp; The World Bank, 2011). They are external to the individual and either enable or challenge the individual's performance or capacity (WHO, 2001). Personal factors comprise an individual's background, including age, sex, personality, and education level, enabling them to act and modify environmental factors and disability and vice versa (WHO \u0026amp; The World Bank, 2011). India is undergoing a demographic and social transition through which the institutions, i.e., social welfare organizations and community support networks, will adapt and develop different approaches to social care, relationships, and support (Kirk, 1996). In India, families are seen as prime social capital in the ageing population, and most are well-connected to immediate and extended social networks (Berkman et al., 2012). However, the social capital has declined, and the traditional extended family structure has been disintegrated due to urbanization, with children residing away due to education, work, and marriage weakening the social capital (IIPS et al., 2020a). The quality, quantity, and degree of social networking reduce disability; however, there is limited evidence of this in India (Lestari et al., 2019). Similarly, the transitions will also result in an increasingly aging population, changes in wealth status, health access and affordability, and social-cultural mindsets (WHO \u0026amp; The World Bank, 2011). Moreover, the cause of disabilities is shifting to an often unknown mixed set of causes, indicating the knowledge gaps in current disability research (Human Development Unit South Asia Region, 2009). In India, studies have examined the risk factors associated with functional disability among the ageing rural population, across genders, States and also examined the multi-morbidity combination with disability\u0026nbsp;(Himanshu \u0026amp; Arokiasamy, 2021; Malik, 2022; Kumar et al., 2023; Halder et al., 2024).\u0026nbsp;However, the risk-outcome relationship between health conditions, personal and environmental factors, and functional disability remains understudied. This study moves beyond identifying risk factors to map the problem, measure the strength of risk-outcome pathways, and understand mechanisms that increase disability for a targeted equitable policy responses.\u003c/p\u003e\n\u003cp\u003eIn the policy context, India has several initiatives to promote the rights of persons with disabilities. It has achieved significant progress in reducing disability due to polio and leprosy; however, it faces considerable challenges in addressing NCD-related disability (Human Development Unit South Asia Region, 2009). The overarching Ministry of Social Justice and Empowerment (MSJE), the nodal agency for disability, has limited resources, lower convening power, and weak coordination (Human Development Unit South Asia Region, 2009). Furthermore, the ambitious commitment, low institutional capacity, \u0026nbsp;poor inter-sectoral coordination, lack of sanctions for non-compliance, inadequate sub-national implementation, limited involvement of non-governmental actors, including people with disabilities, NGOs, PWDs, and PRIs, and poor social attitude and awareness add to the insufficiency (Human Development Unit South Asia Region, 2009). Concurrently, social protection and health insurance policy interventions have low impacts on the poor population with disability as they offer low coverage, limited financial protection, and weak channels for increasing demand (Human Development Unit South Asia Region, 2009). Hence, India will likely fall short of achieving SDGs, the 2030 agenda aim of \"leave no one behind\" as it\u0026nbsp;requires the inclusion and participation of persons with disabilities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study is critical to understanding the risk factors that drive functional disability in the middle and elderly population (45+ years). It hypothesizes that NCDs, environmental factors (social capital), and personal factors influence functional disability. Specifically, from a policy standpoint, it can answer the question: Do NCDs, social capital, and personal factors contribute to functional disability. If yes, which components of the NCDs, social capital, and personal factors should policies target? It adopts the International Classification of Functioning, Disability, and Health framework. It conducts Confirmatory Factor Analysis (CFA) to validate the underlying structure of functional disability and the Multiple Indicators Multiple Causes Model (MIMIC) to examine the direct influence of NCDs, social capital, and personal factors. The CFA-MIMIC approach would identify specific components that policy interventions should target to mitigate functional disability.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003ch3\u003e\u003cem\u003eConceptual Framework\u003c/em\u003e\u003c/h3\u003e\n\u003cp\u003eDisability is an impairment in body function or structure, a limitation in activity, and a restriction in participation (World Health Assembly 66, 2013). The present study adopted the International Classification of Functioning, Disability, and Health (ICIDH-2) framework for understanding disability (WHO, 2001). The bidirectional arrows indicate mutual interaction across all latent factors and explanatory variables (WHO, 2001). Disability is a gap between personal capability and environmental demand (Verbrugge \u0026amp; Jette, 1994). It follows a \u003cstrong\u003esequential three-step pathway\u003c/strong\u003e, wherein \u003cstrong\u003epoor health conditions contribute to structural impairments, which, in turn, restrict an individual\u0026rsquo;s ability to perform activities and participate in society, ultimately leading to functional disability\u003c/strong\u003e (Verbrugge \u0026amp; Jette, 1994). However, this pathway is not linear; it is \u003cstrong\u003eshaped by bidirectional interactions\u003c/strong\u003e among various latent constructs, as represented by the arrows in Figure 1. The influence of these factors is dynamic, meaning they may \u003cstrong\u003eexacerbate, mitigate, or even compensate for disability\u0026nbsp;\u003c/strong\u003e(WHO, 2001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNCDs (Health condition)\u003c/strong\u003e: They represent the individual\u0026apos;s health \u0026nbsp;based on self-reporting rather than medical assessment (WHO, 2001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial capital (Environmental factors)\u003c/strong\u003e: The individual\u0026apos;s external physical, social, and attitudinal environment that positively or negatively influences the body function (WHO, 2001). The social and cultural norms and values of society that govern interactions among people and the institutions in which they are embedded (Grootaert, 2001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePersonal factors\u003c/strong\u003e: These include the background of an individual\u0026apos;s life, as well as personal characteristics like age, sex, wealth status, and education level (WHO, 2001). \u0026nbsp;While personal factors are not classified in ICIDH, they impact the outcome of other latent constructs (WHO, 2001). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy integrating these elements, the conceptual framework provides a holistic perspective on functional disability as a product of socio-environmental interactions, highlighting the need for multi-sectoral interventions that address individual health conditions and structural determinants through healthcare, social, and policy-level approaches.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eStudy Design and sample\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eA cross-sectional design from the Longitudinal Ageing Study in India (LASI, 2017-19) was utilized for this study. LASI is a comprehensive, nationally representative survey investigating the health, economic, and social determinants and consequences of aging in India (IIPS et al., 2020b). Out of a 72,250-sample size, the population under 45 years was removed, and 65,562 middle and elderly adults (45+ years) were selected.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eVariable Description\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe constructs were functional disability, non-communicable diseases, social capital, and other covariates using the ICIDH-2 framework (WHO, 2001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Disability\u003c/strong\u003e: Functional disability is a multi-dimensional construct comprising two factors: limitations in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL). ADL limitations included dressing, walking across a room, and bathing, eating, getting in or out of bed, and using the toilet. IADL limitations included preparing a hot meal, shopping for groceries, making telephone calls, taking medicine, doing housework, managing money, and navigating unfamiliar locations. For ADL and IADL, responses were categorized as 0 = able to perform all activities, 1 = unable to perform one activity, 2 = unable to perform two, and 3 = unable to perform three or more activities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-communicable diseases\u003c/strong\u003e: NCD status was a 13-factor model based on self-reported diagnoses by a healthcare professional for 13 conditions: hypertension, diabetes, cancer, chronic lung disease, chronic heart disease, stroke, arthritis, neurological disorders, high cholesterol, thyroid disorders, gastrointestinal problems, skin diseases, and urogenital conditions. Responses were categorized as 0= No and 1= Yes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Capital\u003c/strong\u003e: Social capital was conceptualized as a four-factor model derived from self-reported activities: visiting relatives/friends, attending cultural performances/shows/cinema, participating in religious functions/events such as bhajan/ satsang/ prayer, and attending political/community/organization group meetings. Responses were coded as 0 = low, 1 = medium, and 2 = high.\u003c/p\u003e\n\u003cp\u003eThe values for all the latent variables were obtained through data imputation (by regression) on the measurement model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e: The control variables were age (continuous), sex (0 = male, 1 = female), education level (0 = less than secondary, 1 = secondary and higher), wealth quintiles (1 = poorest, 2 = poorer, 3 = middle, 4 = richer, 5 = richest), and type of residence (0 = urban, 1 = rural) as control variables \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e(Refer to\u0026nbsp;\u003c/em\u003e\u003cem\u003eAnnexure 1\u003c/em\u003e\u003cem\u003e, Table S1 for\u003c/em\u003e\u003cem\u003e\u0026nbsp;measurements)\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe analysis included the following statistical analyses, and each method offers unique strengths, making their combined use essential. First, a\u0026nbsp;multinomial logistic regression\u0026nbsp;was used to measure the association of\u0026nbsp;NCDs, social capital, and personal factors with functional disability status,\u0026nbsp;i.e., basic activities of daily living (ADLs) and instrumental activities of daily living (IADLs).\u0026nbsp;However, multinomial logistic regression has limitations such as sensitivity to outliers, collinearity issues, and overfitting, which can compromise the stability and accuracy of parameter estimates\u0026nbsp;(Meloun \u0026amp; Militk\u0026yacute;, 2011). Given multinomial logistic regression\u0026apos;s limitations, this study employed factor analysis to validate the measurement structure of latent constructs.\u0026nbsp;It\u0026nbsp;adopted Confirmatory Factor Analysis (CFA) over Exploratory Factor Analysis (EFA) as CFA was more appropriate for testing a predefined factorial structure based on the ICIDH-2 framework. Unlike EFA, which identifies factor structures without prior assumptions, CFA allows for evaluating whether the observed data aligns with the expected constructs of functional disability\u0026nbsp;(Wells, 2021). Additionally, the CFA\u0026apos;s ability to test measurement invariance ensured the stability of this structure across demographic groups\u0026nbsp;(Wells, 2021). The model fit was assessed using the Root Mean Square Error of Approximation (RMSEA\u0026lt; 0.08), Comparative Fit Index (CFI \u0026ge; 0.90), and Tucker-Lewis Index (TLI \u0026ge; 0.90) (37\u0026ndash;39) (Wells, 2021). However, CFA models may face \u003cstrong\u003eidentification issues\u003c/strong\u003e, particularly in complex frameworks, where parameter estimates can remain unstable despite appearing correctly specified\u0026nbsp;(Wells, 2021). Additionally, CFA\u0026rsquo;s reliance on \u003cstrong\u003efit indices\u003c/strong\u003e increases the risk of \u003cstrong\u003eoverfitting\u003c/strong\u003e, as adjustments to improve model fit may compromise theoretical validity\u0026nbsp;(Wells, 2021). It employs the\u0026nbsp;Multiple Indicators Multiple Causes Model\u0026nbsp;(MIMIC) to address these limitations. By incorporating covariates directly, MIMIC enhances \u003cstrong\u003eidentification\u003c/strong\u003e and reduces the need for excessive model adjustments, improving stability and theoretical coherence\u0026nbsp;(Lee et al., 2013). \u0026nbsp;Figure 2\u0026nbsp;is a conceptual framework for the MIMIC model to validate the hypothesized relationship between NCDs, social capital, personal factors, and\u0026nbsp;functional\u0026nbsp;disability. The three unobserved latent variables (Oval) are\u0026nbsp;functional\u0026nbsp;disability, NCD, and social capital. The bold black arrows show the relationship between\u0026nbsp;functional\u0026nbsp;disability, NCD, and social capital. The observed measured indicators (rectangles) that define the latent variables (ovals) with grey arrows. The observed exogenous variables (covariates) are age, sex, wealth status, education level, and type of residence.\u0026nbsp;The analysis consisted of the simultaneous estimation of the three following regression models:\u003c/p\u003e\n\u003cp\u003e(i) Regression models consisting of three correlated latent factors\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eFunctional Disability ~ ADL + IADL\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSocial capital ~ Attend political meetings + Attend religious functions + Attend cultural events + Visit relatives\u003c/li\u003e\n \u003cli\u003eNCDs ~ Hypertension + Diabetes + Cancer + Chronic lung cancer + Chronic heart disease + Stroke + Arthritis + High Cholesterol + Thyroid Disorder + Gastrointestinal Problem + Skin disease + Urogenital condition + Neurological Disorders\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e(ii) Regression of explanatory variables on the latent factors:\u003c/p\u003e\n\u003cp\u003eFunctional Disability + Social Capital + NCDs ~ Age + Sex + Wealth + Education level +Residence\u003c/p\u003e\n\u003cp\u003eThese models ensured a comprehensive analysis that integrated predictive relationships, validated latent constructs, and accounted for covariate effects, thereby enhancing the robustness and reliability of the findings(Meloun \u0026amp; Militk\u0026yacute;, 2011; Lee et al., 2013; Wells, 2021).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cem\u003eBackground characteristics of the middle and elderly adults\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe mean age of this study population was 59.7 years, with\u0026nbsp;53.5% women. The wealth index was divided into quintiles, with the most significant proportion in the poorer group (21.2%), followed by the poorest (20.9%), middle (20.5%), richer (19.4%), and richest (18.0%). Around 68% of the population had secondary and higher education, and 32.4% lived in urban areas. Overall, the population had more IADL than ADL impairments. Rates of having one, two, three, or more ADLs were 7.4%, 4.2%, and 5.4%, whereas rates of having one, two, and three or more IADLs were 10.4%, 7.0%, and 20.1%, respectively. Around 43% had no NCDs, whereas 29.5% had one, 16.7% had two, 7.2% had three, and 3.6% had four or more NCDs. The population had the highest prevalence of hypertension (27.5%), followed by gastrointestinal problems (18.1%), arthritis (16.3%), and diabetes (12.3%). Within social capacity, 13.9% frequently visited relatives and friends, 2.4% regularly attended cultural performances, 8.7% participated in religious, and 1.7% often attended group meetings. (\u003cem\u003eRefer to\u003c/em\u003e \u003cem\u003eAnnexure 3, Table S2\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eFigure 3 shows the prevalence and proportions of multi-morbidity for the 13 NCDs. The most common NCD was hypertension (27.3%), followed by gastrointestinal problems (18.1%), arthritis (16.3%) and diabetes (12.3%). Chronic lung disease, skin disease, chronic heart disease, thyroid disorder, neurological disorders, high cholesterol, and stroke showed prevalence rates from 6.7% to 1.9%, whereas urogenital conditions and cancer were less frequently reported (≤ 1.0%).\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eMultinomial Logistic Regression\u0026nbsp;\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFigure 4 represents the heat map for the Multinomial Logistic Regression analysis results with the base outcome as no ADL/no IADL having three reference categories, i.e., 1 ADL/ 1 IADL; 2 ADL/ 2 IADL; 3+ ADL/ 3+ IADL. Model 1 examined the relationship between ADL and NCDs, social capital, and personal factors, while Model 2 examined these factors concerning IADL. Across both models, NCDs increase the likelihood of ADL and IADL, higher social capital reduces its likelihood, and personal factors show mixed results. Within the NCDs, stroke had the highest likelihood of being associated with ADL and IADL, followed by neurological disorders, arthritis, and urogenital conditions. High cholesterol increased the likelihood of 1ADL and 3+ IADL; however, it reduced the likelihood of 2 ADL and 3+ADL. Most social capital activities reduce ADL and IADL likelihood; however, attending religious functions slightly increases it. Within personal factors, increasing age and being a woman increased the likelihood of ADL and IADL, whereas population belonging to higher wealth status and living in urban population reduce the likelihood of ADL and IADL. Lastly, the population with secondary and higher education decreased the likelihood of ADL; however, it increased the likelihood of IADL.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eConfirmatory factor Analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFigure 5 depicts factor loadings for NCDs, social capital and functional disability. Hypertension is the strongest NCD indicator, and cancer is the weakest. Attending cultural events has the strongest correlation with social capital, followed by attending political meetings, religious functions, and relatives. Lastly, functional disability is associated with IADL, followed by ADL. This trend illustrates the hierarchical nature of functional abilities, with IADLs encompassing complex cognitive and motor functioning usually impacted before ADLs.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eMultiple Indicators Multiple Causes (MIMIC) model\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eFigure 5\u0026nbsp;illustrates the path diagram that serves as the foundation for the analysis of the intricate relationship between functional disability, NCDs, social capital, and personal factors, i.e., covariates (age, sex, wealth, education, and residence). The final model demonstrated adequate goodness-of-fit: CFI = 0.838, TLI = 0.805, and RMSEA = 0.029. The impact of NCDs was higher on functional disability than social capital. The relationships are two-way, suggesting functional disability even modifies the NCDs and social capital. \u003cstrong\u003eHowever, this association should be interpreted with caution, as individuals with functional disability due to chronic conditions may face barriers in participating in social activities and meetings, potentially influencing the observed relationship.\u003c/strong\u003e The relationship between NCDs and social capital was minimal. All the covariates were significantly correlated with each other. It also had a significant positive effect on NCDs. Older age, being a woman, and having low education were associated with lower social capital, while higher wealth and living in rural areas were linked to greater social capital. Similarly, older age, being a woman, and having low education were associated with higher functional disability, whereas higher wealth and living in urban areas were linked to lower functional disability. Thus, the model indicates a bi-directional complex interplay where NCDs exacerbate functional disability, social capital has a protective influence, and personal factors mediate the relationship.\u003cem\u003e\u0026nbsp;(Refer to Annexure 2 Table S3 for Regression weights; and Annexure 2 Table S4 for Covariance)\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provides evidence on the risk-outcome relationship between linkages of NCDs, social capital, and personal factors with functional disability in the middle-and-elderly population (45\u0026thinsp;+\u0026thinsp;years) in India using the ICIDH-2 framework, LASI (2017-18) data, and Multinomial Logistic Regression and MIMIC model. It has the following two key evidence. First, hypertension (27.3%) was the most prevalent; however, stroke increased the likelihood of functional disability. Other studies also report that hypertension is the most common multi-morbidity pattern and directly contributes to 57% of strokes (Gupta, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), making hypertension a primary target for prevention. Second, NCDs are a significant predictor of functional disability, with social capital having a protective effect of reducing the likelihood of functional disability, and personal factors mediate to contribute to the functional disability burden directly and indirectly. Functional disability is more prevalent in women, ageing, and poor populations with less than secondary education living in rural areas. Other studies had similar results: NCDs increased functional disability risk, and community health programs for targeted social interactions, which highlight the importance of community engagement, prevented the onset of functional disability in the elderly population (Hikichi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Skou et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In low-income countries, functional disability disproportionally affects vulnerable populations- people from the poorest wealth quintile, low education, older population, women, and those in ethnic minority groups (WHO \u0026amp; The World Bank, 2011).\u003c/p\u003e\u003cp\u003eNCDs are associated with an increased likelihood of functional disability, with hypertension being the most prevalent and stroke having the highest contribution. All the personal factors had positive yet minimal effects associated with NCDs, suggesting that NCDs are prevalent across all socio-economic groups. Various risk factors contribute to increasing NCD-related functional disability- growing aging population, urbanization, changing lifestyle, metabolic syndrome, intrauterine malnutrition followed by calorie-rich food in later years, and environmental toxins (Jeemon et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The National Program for Prevention and Control of Cancer, Diabetes, CVD, and Stroke (NPCDCS, 2010) aims for the prevention and management of NCDs by generating awareness of behaviour and lifestyle changes, and screening, early diagnosis, referral and treatment (MOHFW, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cem\u003eAyushman Bharat Abhiyan\u003c/em\u003e provides a health insurance plan (INR 5 Lakh/ annually) for secondary and tertiary care per family. However, the Indian system faces challenges, including inadequate health financing, human resources, surveillance systems, poor access services, and high out-of-pocket expenditure (Jeemon et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Hence, India needs to shift its NCD approach from a 'cure-based reactive model' to a 'care-based proactive healthcare model' (Jeemon et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), emphasising preventive measures and early intervention.\u003c/p\u003e\u003cp\u003eWith the demographic, social, and epidemiological transition, the NCDs will continue rising, and social capital will likely reduce, highlighting the need to prepare for the rising functional disability rate, which has significant implications for health, the economy, and society. While India's Rights of Persons with Disabilities Act (RPWD, 2016) aimed for equitable opportunities for the population with disabilities, it did not have a desirable impact and remains inadequate to augment their quality of life (National Institute of Urban Affairs (NIUA) \u0026amp; Department for International Development (DfID-UK), 2020). Recently, WHO established the World Rehabilitation Alliance (WRA, 2022) to optimize functioning in everyday life, ensure equitable access through universal health coverage, and integrate rehabilitation services across secondary and primary healthcare to reduce disability (WHO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It generates demand and mobilizes political will through primary care, workforce, external relations, emergencies, health systems, and policy research (WHO, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). India can adopt key components identified as good practices at both the systems and service levels to create a disability-inclusive health system (Kuper et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the systems level, this includes (1) ensuring enforceability and accountability through country-specific laws and policies, (2) representing disability within institutions to improve leadership, (3) providing health financing and insurance coverage to strengthen financial protection, and (4) conducting routine monitoring and evaluation to generate evidence for decision-making and improved service delivery (Kuper et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the service level, essential components to improve demand and supply include: (1) increasing awareness and ensuring autonomy in healthcare decision-making; (2) improving healthcare affordability; (3) ensuring the availability of an adequately skilled healthcare workforce; (4) developing accessible healthcare infrastructure; and (5) incorporating technology-assisted solutions to enhance service delivery (Kuper et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Adopting these practices can help India build a more inclusive healthcare system that addresses gaps in disability-related care.\u003c/p\u003e\u003cp\u003eSocial capital reduces the likelihood of disability. It aligns with Nussbaum's (2006) capability approach that suggests that disability is not merely a medical condition (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)​. It arises from systemic deficiencies in providing necessary capabilities brought on by socio-environmental restrictions that prevent people from interacting and contributing to society (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)​. Social capital improves health and health knowledge, facilitates healthcare access, promotes healthy aging and behaviours, and reduces functional disability (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Simplican et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It suggests that marginalized populations- older adults, women, and those with higher education are less likely to have social capital, resulting in a higher risk of disability. Evidence from rural India also suggests that populations from poorer households and socially disadvantaged communities experience lower social capital due to limited social networks, reduced trust in institutions, and weaker collective action mechanisms (Krishna, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Singh \u0026amp; Gaurav, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Recognizing these benefits, India\u0026rsquo;s National Policy on Senior Citizens (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) aims at active aging by encouraging community support systems and social engagement through senior citizens' associations and strengthening family-based care mechanisms (National Policy on Senior Citizens, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, top-down institutional interventions alone may be insufficient for social capital growth (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krishna, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Hence, policies can prioritize bottom-up strategies that empower local communities for participatory engagement (Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krishna, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It will ensure that social capital is embedded within social structures and sustained through community-driven efforts rather than external dependency ​(Nussbaum, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Krishna, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harnacke, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study bridges a critical literature gap by examining the risk-outcome relationship between NCDs, social capital, and personal factors with functional disability among middle-and-old aged adults (45\u0026thinsp;+\u0026thinsp;years) using the first wave of LASI-India and robust models Multinomial Logistic Regression, CFA, and MIMIC each offering unique strengths. Multinomial Logistic Regression facilitated examining predictive relationships, enhancing predictive validity (Meloun \u0026amp; Militk\u0026yacute;, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). CFA ensured construct validity by confirming the predefined ICIDH-2 factorial structure and assessing measurement invariance across groups (Wells, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The MIMIC model addressed CFA\u0026rsquo;s limitations by incorporating covariates directly, improving identification, reducing parameter instability, and minimizing overfitting (Lee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This integrated approach strengthened theoretical coherence and provided a comprehensive analysis. While these models enhance this study's robustness, a larger sample size is essential to produce reliable Multinomial Logistic Regression and MIMIC estimates (Meloun \u0026amp; Militk\u0026yacute;, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wells, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, it leverages LASI, a nationally representative data source with a large sample size, real-time monitoring, and an automated data quality control protocol, ensuring impeccable state-level data accuracy (Paul \u0026amp; Chandra Sarma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It also establishes a baseline for future research that can utilize longitudinal data from LASI Wave II to track changes in the population over time.\u003c/p\u003e\u003cp\u003eThis study also has limitations. The statistical methods employed have inherent constraints; Multinomial Logistic Regression is likely to outlier influence, collinearity, and overfitting (Meloun \u0026amp; Militk\u0026yacute;, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while CFA\u0026rsquo;s reliance on fit indices risks overfitting and may face identification issues in complex frameworks (Wells, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although MIMIC improves identification and handles covariates, it may oversimplify complex interactions and face challenges in capturing bidirectional relationships (Lee et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, while MIMIC identifies directional relationships, these findings should be interpreted as correlational rather than causal due to the cross-sectional nature of the LASI data. Reverse causation remains a possibility, as individuals with functional disability due to chronic conditions may face barriers to social participation, potentially influencing the observed outcomes. The LASI questionnaire does not specify whether participants reported difficulty with IADL tasks because they had never engaged in those activities, which may reflect social and gendered roles. Consequently, the analysis assumes that reported difficulty reflects impairment rather than non-engagement, potentially overlooking gendered differences in IADL patterns. Additionally, self-reported variables may have introduced recall and reporting biases, resulting in over- or under-estimation. Future research could improve accuracy by distinguishing between \"never did\" and \"cannot do\" responses or exploring role-based exclusions, as suggested by Sheehan \u0026amp; Tucker-Drob, (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), to better account for gendered patterns in IADL assessment. It also does not account for factors such as household work, physical activity, unhealthy diet, or stigma, which could influence functional disability outcomes. Further research may benefit from focusing on elderly populations (60\u0026thinsp;+\u0026thinsp;years), examining gendered differences within marginalized communities, and employing ethnographic approaches to understand how health systems can delay disability onset, improve recovery outcomes, and better manage functional disabilities in India.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIndia is undergoing demographic transition, rapid urbanization, industrialization, and modernization with socio-economic change, including family structure and living arrangements, and affecting ideologies of social support. Hence, India provides an attractive setting for determining risk factors associated with functional disability. While studies have examined the risk factors of functional disability, the risk-outcome interplay of health conditions, environment, and personal factors in the middle-and-elderly population (45\u0026thinsp;+\u0026thinsp;years) is understudied. This study addresses this gap in India by examining the linkages of NCDs, social capital, and personal factors to functional disability. While a majority of the population has hypertension, functional disability has a higher likelihood of being associated with stroke. As hypertension is a precursor for stroke, it remains the critical target for policy action. NCDs are associated with a higher likelihood of functional disability, whereas greater social capital is linked to a lower risk. Personal factors mediate these associations, shaping the functional disability burden both directly and indirectly. Furthermore, the impact of NCDs and limited social capital on functional disability is exacerbated by aging, female gender, low socioeconomic and educational status, and rural residence. However, these relationships are bidirectional, as functional disability itself may further restrict social participation, reinforcing the effects of low social capital and worsening health outcomes.\u003c/p\u003e\u003cp\u003eBuilding on the capability approach, the present study highlights the need to address structural barriers that limit an individual's ability to function and participate fully in society. It emphasizes the need to (1) Effectively implement \u003cem\u003eBest Buys\u003c/em\u003e to prevent and manage NCDs, particularly hypertension, (2) Strengthen social capital by adopting bottom-up strategies that empower communities, build trust networks, and foster participatory engagement through community centers, peer-support groups, and intergenerational programs embedded within social structures, and (3) Expand social-safety needs, health and educational services, health insurance, improve awareness and accessibility for the vulnerable population.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerkman, L. F., Sekher, T. V., Capistrant, B., \u0026amp; Zheng, Y. (2012). Social Networks, Family, and Care Giving Among Older Adults in India. In Aging in Asia: Findings From New and Emerging Data Initiatives (pp. 261\u0026ndash;279). 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(2013). Sixty-sixth World Health Assembly, Geneva, 20\u0026ndash;27 May 2013: Resolutions and decisions (No. WHA66/2013/REC/1). WHO Press. https://iris.who.int/handle/10665/150207\u003c/li\u003e\n\u003cli\u003eZhang, X., Padhi, A., Wei, T., Xiong, S., Yu, J., Ye, P., Tian, W., Sun, H., Peiris, D., Praveen, D., \u0026amp; Tian, M. (2022). Community prevalence and dyad disease pattern of multimorbidity in China and India: A systematic review. BMJ Global Health, 7(9), e008880. https://doi.org/10.1136/bmjgh-2022-008880\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Indian Institute of Technology Bombay","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Ageing, Disability, NCDs, Hypertension, LASI-India","lastPublishedDoi":"10.21203/rs.3.rs-7692267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7692267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the relationship between health conditions, particularly non-communicable diseases (NCDs), environmental and personal factors, and functional disability is crucial to achieve the SDG aim - \"leaving no one behind.\" However, these risk-outcome pathways remain understudied. This study assesses the relationship between NCDs, environmental factors (social capital), and personal factors with functional disability (ADL: Activities of Daily Living, IADL: Instrumental Activities of Daily Living) in the middle and elderly population (45+ years). It adopts the International Classification of Functioning, Disability, and Health (ICIDH-2) framework, used Longitudinal Ageing Study in India (LASI,2017-18), and performed Multinomial Logistic Regression and Multiple Indicators Multiple Causes Model for middle-and-old population (45+ years). The findings suggest that hypertension (27.3%) was the most prevalent condition, and stroke increased the likelihood of disability. However, hypertension is a precursor to stroke, highlighting the need to target hypertension. NCDs are a significant predictor of disability, with social capital having a protective effect of reducing the likelihood of disability, and personal factors mediate to contribute to the disability burden. Disability is prevalent among vulnerable groups-older adults, women, and individuals with low wealth, limited education, and rural residence. However, these relationships are bidirectional, as disability may also limit social participation, further exacerbating low social capital and high NCDs. It highlights the need to effectively implement WHO Best Buys to address NCD, specifically hypertension, and adopt bottom-up strategies to strengthen social capital through community empowerment, trust-building, and participatory engagement. Lastly, expanding health, education, and social security programs would ensure adequate support for vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Determinants of Functional Disability in India: The Interplay of Health Conditions, Environmental and Personal Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 04:46:56","doi":"10.21203/rs.3.rs-7692267/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e92176e3-26e0-4956-81b8-4943b89a65b6","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55178522,"name":"Health Policy"},{"id":55178523,"name":"Health Economics and Outcomes Research"}],"tags":[],"updatedAt":"2025-09-24T04:46:57+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 04:46:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7692267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7692267","identity":"rs-7692267","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}