Relationship between living arrangements and cognitive impairment among older adults in India: Findings from LASI-2017-18

Relationship between living arrangements and cognitive impairment among older adults in India: Findings from LASI-2017-18 | 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 Relationship between living arrangements and cognitive impairment among older adults in India: Findings from LASI-2017-18 Kinkar Mandal, T. Muhammad Muhammad, Arun Bala Chandran, Karthick V This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6856596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective As a care and support system of older adults, living arrangements are associated with the older adult’s cognitive impairment. Such family arrangements play an important role in the wellbeing of the older adults with clear distinction across living with children and spouse with that of extended kin. The present study explores the association between living arrangements and cognitive impairment among older adults in India. Design Cross sectional study Setting For this study, we used the Longitudinal Ageing Study (LASI-2017-18) data, a nationally representative survey of 31,464 older people aged 60 years and above in India. Bivariate and multivariable regression analysis were performed to fulfil the study objective. Results We find that older women (20.03%) were more vulnerable to cognitive impairment as likelihood reported compared to older men (7.13%) in India. After controlling the variables separately for men and women, we find that cognitive impairment, among men and women, increases when age increases. Living with spouse older men (OR: 12.38) are more likely to be affected with the cognitive impairment than women (OR: 2.44). Further, less educated, difficulties with at least one ADL/IADL older adults have higher chances of getting affected with cognitive impairment. Cognitive impairment increases with decrease wealth that is lower quintile older adults are more likely to be cognitively impaired, especially women are the most affected. Conclusion Living arrangements play a vital role in the well-being of the older adults in India. Older adults who living with others, living with children and lives alone have higher prevalence of cognitive function, especially women are more vulnerable. Study suggests that policy interventions by the government would benefit for the living alone older women. Introduction India has the second-largest and fastest-growing older population in the world, and they account for 8.6 per cent of the total population in the country (Office of the Registrar General and Census Commissioner & Affairs, 2011 ). United Nations has projected that it will increase to 19 per cent by 2050 (United Nations, Department of Economic and Social Affairs, 2015). Such a change in Indian age structure is simultaneously associated with changes in family structure, and concerns regarding economic challenges, medical expenditure, and long-term support for the older adults (Bloom et al., 2014 ). Studies have shown that cognitive impairments are increasing with an associated effect on the quality of life among the older adults (Singh & Govil, 2016 ). Therefore, worldwide economic and social costs are also increasing to both individuals and societies (Mazzuco et al., 2017 ; Roystonn et al., 2020 ) The living arrangement is associated with cognitive impairments and as a result, the older adults those who are living alone have poor cognitive functions. Similarly, social isolation, loneliness, and low engagement in social activities are also associated with poor cognitive function (Evans et al., 2019 ). The cognitive functions of older adults measure multiple aspects of intellectual functions such as memory, concentration, decision making, ability to work, and social interactions (Roystonn et al., 2020 ). Studies suggest that higher risk of morbidity and mortality due to the lower cognitive functions and segregation of the older adults (Obisesan & Gillum, 2009 ). At the same time, cognitive functions are linked with the social fabric and living arrangements in the society. For instance, a higher level of psychological and psychiatric disorders corresponded to living alone (Kaisla et al., 2006). Concomitantly, the living arrangements play a role in several aspects of morbidity of the older adults. Having a partner and living with others are associated with higher levels of social interactions (Gelder et al., 2006 ). Living in multigenerational households tends to improve good health among the older adults in India (Samanta et al., 2015 ). Living arrangement and disability were moderated by gender status have shown that older adults living in two-generation with linear-linked arrangements (households with/without spouse, with married adult children and their children) have lower risk of disability. Whereas living in Single-generation with complex-linked (with/without spouse, and any adult children and grandchildren) arrangements have a higher level of disability. Further, throughout the living arrangement the older women, those who are living in linear-linked multigenerational arrangement have a higher risk of disability than older women who are living in a single generation or other types of arrangements. On the other hand, older men who live in multigenerational or two-generation households have a higher level of disability (Schatz et al., 2018 ). Similar types of study from China have found that living arrangement is associated with functional disability. That is married older adult living alone or children have better functional ability whereas, unmarried, widows, divorced and never-married older adult have higher prevalence of disability (H. Wang et al., 2013 ). While living arrangements influence cognitive impairment, it also confluences with other sociodemographic dimensions leading to more vulnerablitiy among certain sections of the population. For instance, in USA, higher rate of cognitive impairment have been found among widowed older women, especially with regards to Alzheimer or dementia (Gibson & Richardson, 2017 ). Further, exploration of the influence of gender on cognitive impairment in the context of the Chinese oldest old found that women in low socioeconomic status and rural areas have high risk of cognitive impairment due to limited availability of health resources (J. Wang et al., 2020 ). Similarly, changing family structure lead to rising mental health among the Turkish older population, subsequently increasing their cognitive impairment (Sahin et al., 2007 ). In rural Bangladesh, older women face multi-morbidity led cognitive and functional Impairments due to changing lifestyles, living conditions, behaviours, and working status (Akter et al., 2011 ). A study based in Japan showed that both men and women who are living without a spouse have a higher risk of basic activity of daily living disability even as living with non-spousal cohabitants led to a higher risk of basic activity of daily living disability (Saito et al., 2017 ). Another study based in South Korea found that living alone physically disable older adults have poor quality of life satisfaction compared to older adults living with spouses or children (Kim et al., 2015 ). Much of the research has identified that living with children and others directly benefit to the older adults physical and psychological health. Because older adults receive physical, emotional and social support from their children. On the other hand, living alone or living with non-immediate kin often lead to increasing the risk of mortality and cognitive impairment (Macknight et al., 2002 ; Roystonn et al., 2020 ). In the context of India, very limited studies have looked at the association between living arrangements and cognitive impairment. While there has been some evidence that living in multigenerational household with spouse and/or children and grandchildren increased the likelihood of wellbeing and higher physical health benefit for the older adults (Samanta et al., 2015 ) in India. To our knowledge, limited evidence exist in assessing the impact of living arrangements on cognitive impairment among the older adults in India using nationally representative longitudinal data. Methods Study design and setting The Longitudinal Ageing Study in India (LASI) is the first longitudinal nationally representative biannual panel survey of the older adult’s age 45 years and above conducted in 2017-18 by International Institute for Population Sciences (IIPS), Harvard T.H.Chan School of Public Health (HSPH) and University of Southern California (USC) in India. LASI data represents entire country (except Sikkim). The LASI Wave 1 was financially supported by the National Institute on Ageing (NIA/NIH), USA, United Nations Population Fund (UNFPA), India, and Ministry of Health and Family Welfare (MoHFW), Government of India. The technical support provided by International Technical Advisor Committee and ethical guidelines were approved by the Indian Council of Medical Research (ICMR). The detailed explanations of the sampling technique are available in the LASI Wave-1 report. The LASI data contains older adult’s related information of 72,250 respondents from the 42,949 households based on multistage stratified area probability cluster sampling design. A total of 31,464 older adult aged 60 and above, who lived more than 6 months in sample households in the last year, are included for the analysis in this study. Dependent variable To analyse the impact of cognitive impairment among older adults, this study considered cognitive impairment (whether physical or mental impairment or both) as an outcome variable. The outcome variable is dichotomous (0, 1), i.e. if older person has cognitive impairment = 1, otherwise = 0. Explanatory variables The explanatory variables used in this analysis are categorical variables. They are as follows. Age is a category variable which are grouped into three such as 60–69, 70–79, 80 + years. While sex is a dummy variable which has two category male and female, whereas marital status are coded into two category such as currently in union (i.e. currently married) and not in union (i.e. either widowed or divorced or separated or never married). Education is another important variable included in the analysis which has three categories such as no-education/primary, secondary, and higher. The variable Working status is categorised into four such as never worked, not working, working, and retired. Whereas self-rated health variable is coded into two categories as good (including excellent, very good, and good) and poor (including fair and poor). The variable Activity of Daily Living and Instrumental Activity of Daily Living consists of a series of activities such as dressing, walking, bathing, eating, getting in or out of bed, toilet, preparing a meal, shopping, telephone, medication, workaround, managing money and getting around. For the analysis, these activities are grouped into two as high ADL/IADL (represents no difficulty in ADL/IADL) and low ADL/IADL (represents difficulty with at least one ADL/IADL). While wealth quantile variable is coded into five categories such as poorest, poorer, middle, richest, and richest. The variable religion of the older person consists of three groups such as Hindu, Muslim, and others. Similarly, the social group category consists of three dummy variables as Schedule Caste/Schedule Tribe (SC/ST), Other Backward Caste (OBC), and others. The dummy variable respondent’s place of residence consists of rural and urban categories. Statistical analysis This study used both descriptive statistics as well as econometric methods. Descriptive analysis is used to assess the frequency and percentage distribution of the elderly by various background characteristics of the elderly population. Differences in cognitive impairment across socioeconomic groups is analysed using bivariate estimation. The relationship between living arrangements and cognitive impairment is measured using logistic regression with separate models for males and females. A multivariable logistic regression model with adjusted odds ratio is used to control the predictor variables associated with outcome variables in our study. The results are reported at 95 per cent confidence intervals (CIs) and p -values considered for the statistically significant results are p < 0.05, p < 0.01, p < 0.001. Results The socioeconomic and demographic characteristics of the study participants are presented in Table 1 . A proportion of 5.68 per cent of older adults lived alone, among them 2.52 per cent are men and 8.53 per cent are women. The older persons living with their spouse and children were around 41 per cent and their corresponding figure for men and women were 54 per cent and 28 per cent, respectively. A proportion of 11.2 per cent of respondents were aged 80 + years. The proportion of older men who were currently in union is nearly double (81 per cent) in comparison to older women (44 per cent). Around 56 per cent of women and only 19 per cent of men were not in marital union. In case of education level, more women (86 per cent) compared to men (61 per cent) had no education. A proportion of 74 per cent of older adults had no education/primary or had primary level of education and only 8 per cent had reported higher education. Table 1 Socio-economic profile of the study participants Variable group Variables Total (N = 31,464) Male (N = 14,931) Female (N = 16,533) % N % N % N Living arrangements Living alone 5.68 1,787 2.52 380 8.53 1,397 Living with spouse 20.33 6,397 26.03 3929 15.19 2,485 Living with spouse and children 40.62 12,779 54.39 8211 28.18 4,612 Living with children 27.64 8,696 13.21 1994 40.67 6,656 Living with others 5.74 1,805 3.86 583 7.43 1,216 Age 60–69 years 58.51 18,410 57.82 9,678 59.13 9,678 70–79 years 30.20 9,501 31.14 4,803 29.35 4,803 80 + years 11.29 3,553 11.04 1,886 11.52 1,886 Marital status Currently in union 61.63 19,391 81.09 12,242 44.06 7,211 Not in union 38.37 12,073 18.91 2,856 55.94 9,155 Education No/primary 74.02 23,289 60.95 9,202 85.82 14,046 Secondary 18.24 5,741 26.22 3,958 11.04 1,808 Higher 7.74 2,434 12.83 1,937 3.13 513 Work status Never worked 26.43 8,315 3.83 578 46.84 7665 Not working 36.45 11,470 40.88 6,173 32.45 5,311 Working 29.87 9,397 42.05 6,348 18.87 3,088 Retired 7.25 2,282 13.24 1,999 1.84 302 SRH* Good 75.79 23,341 77.75 11,490 74.03 11,860 Poor 24.21 7,457 22.25 3,288 25.97 4,160 ADL* High 76.23 23,887 79.13 11,881 73.64 12,019 Low 23.77 7,448 20.87 3,133 26.36 4,303 IADL* High 51.64 16,162 61.16 9,175 43.14 7,029 Low 48.36 15,133 38.84 5,827 56.86 9,264 MPCE quintile Poorest 21.70 6,829 20.83 3,145 22.49 3,681 Poorer 21.71 6,831 21.32 3,219 22.06 3,611 Middle 20.95 6,590 21.60 3,262 20.35 3,331 Richer 19.19 6,038 19.22 2,902 19.16 3,136 Richest 16.45 5,176 17.02 2,570 15.93 2,607 Religion Hindu 82.20 25,871 82.04 12,386 82.39 13,484 Muslim 11.30 3,548 11.72 1,769 10.88 1,781 Others 6.50 2,045 6.25 943 6.73 1,101 Caste SC/ST 27.10 8,505 26.50 4,001 27.50 4,501 OBC 45.20 14,231 45.86 6,925 44.66 7,308 Others 27.70 8,728 27.63 4,172 27.84 4,556 Place of residence Urban 29.45 22,196 27.95 4,219 30.82 5,044 Rural 70.55 9,268 72.05 10,879 69.18 11,322 *Sample size may differ due to missing cases; MPCE: Monthly per capita consumption expenditure; ADL: Activities of daily living; IADL: Instrumental activities of daily living The prevalence estimates of cognitive impairment among older adults are presented in Table 2 . Cognitive impairment was higher among older adults who were living with children, living with others, and living alone, whereas it was lowest among those who lived with spouse or lived with spouse and children. The prevalence of cognitive impairment was higher among women (20.03 per cent) than men (7.14 per cent). Further, the prevalence was higher in those women who lived with others (28.2 per cent) than those who lived with others only (10.6 per cent). Similarly, older women who lived with children (23.8 per cent), lived alone (22.8 per cent), lived with spouse (17.3), lived with spouse and children (14.2) were having higher cognitive impairments. Table 2 Bivariate estimates for cognitive impairment by background characteristics Variable group Variables Total Male Female p-value % % % Living arrangements Living alone 19.07 5.7 22.83 0.001 Living with spouse 11.92 8.35 17.28 Living with spouse and children 8.67 5.62 14.18 Living with children 20.59 10.74 23.78 Living with others 21.93 10.57 28.24 Sex Male 7.14 0.001 Female 20.03 Age 60–69 years 10.03 5.15 14.61 0.001 70–79 years 16.77 8.33 25.43 80 + years 27.76 15.67 40.87 Marital status Currently in union 9.68 6.42 15.23 0.001 Not in union 20.77 10.34 24.29 Education No/primary education 18.79 11.41 23.9 0.001 Secondary 1.05 1.25 0.65 Higher 0.42 0.41 0.43 Work status Never worked 16.4 8.64 16.99 0.001 Not working 17.6 9.78 27.25 Working 9.98 6.46 17.53 Retired 2.24 1.59 6.76 SRH Good 12.2 6.38 18.11 0.001 Poor 18.62 9.96 26.08 ADL High 11.24 5.99 16.73 0.001 Low 22.32 11.99 30.26 IADL High 8.43 4.7 13.56 0.001 Low 19.88 11.4 25.38 MPCE quintile Poorest 18.55 9.81 26.56 0.001 Poorer 15.75 8.53 22.74 Middle 12.46 5.97 18.97 Richer 10.88 6.06 15.66 Richest 9.81 5.05 14.66 Religion Hindu 13.37 6.89 19.66 0.107 Muslim 14.62 7.53 22.09 Others 15.72 9.61 21.4 Caste SC/ST 19.74 11.53 27.73 0.001 OBC 12.03 5.85 18.12 Others 10.8 5.24 16.17 Place of residence Urban 6.69 2.4 10.3 Rural 16.67 8.97 24.7 Total 13.66 %: Per cent; ADL: Activities of daily living; IADL: Instrumental activities of daily living; MPCE: Monthly per capita consumption expenditure The multivariable logistic regression estimates are provided in Table 3 . The general the respondents living with spouse (OR 3.11), living with spouse and children (OR 2.35), living with children (OR 1.22), and living with others (OR 1.27) were less likely to have cognitive impairment than respondents living alone. The risk of cognitive impairment was three times more likely among older men living with spouse only (OR 12.38) compared to their living alone counterparts. Higher odds of cognitive impairment were observed among older men who lived with spouse and children (OR 9.049). On the other hand, older adults living with children and living with others had higher odds of cognitive impairment compared to their living alone counterparts. Whereas, older women had higher odds of cognitive impairment (OR 2.368) compared to men. Table 3 Logistic regression estimates of cognitive impairment (AOR) by socioeconomic and health characteristics Variable group Variables Total Male Female AOR (95% CI) AOR (95% CI) AOR (95% CI) Living arrangements Living alone (Ref.) Living with spouse 3.114*** (1.425–6.803) 12.38*** (2.281–67.21) 2.444** (1.015–5.887) Living with spouse and children 2.349** (1.083–5.097) 9.049** (1.679–48.78) 1.856 (0.774–4.452) Living with children 1.224* (0.965–1.554) 1.978** (1.014–3.856) 1.155 (0.891–1.498) Living with others 1.271 (0.939–1.721) 1.824 (0.805–4.136) 1.246 (0.894–1.737) Sex Male (Ref.) Female 2.368*** (2.019–2.778) - - Age 60–69 (Ref.) 70–79 1.554*** (1.321–1.828) 1.373** (1.019–1.851) 1.611*** (1.334–1.946) 80+ 2.537*** (2.074–3.103) 2.200*** (1.579–3.067) 2.765*** (2.145–3.565) Marital status Currently in union (Ref.) Not in union 2.837*** (1.357–5.931) 6.535** (1.416–30.15) 2.384** (1.038–5.478) Education No/primary (Ref.) Secondary 0.0948*** (0.0651–0.138) 0.149*** (0.0970–0.229) 0.0364*** (0.0180–0.0736) Higher 0.0627*** (0.0261–0.151) 0.0773*** (0.0277–0.216) 0.0371*** (0.00858–0.161) Work status Never worked (Ref.) Not working 1.176* (0.991–1.395) 0.919 (0.499–1.694) 1.229** (1.022–1.478) Working 0.965 (0.799–1.165) 0.864 (0.458–1.632) 0.886 (0.719–1.091) Retired 0.615* (0.377–1.003) 0.440** (0.198–0.975) 0.740 (0.294–1.866) SRH Good (Ref.) Poor 1.129* (0.979–1.302) 1.167 (0.902–1.509) 1.108 (0.934–1.315) ADL High (Ref.) Low 1.450*** (1.226–1.714) 1.371** (1.001–1.877) 1.473*** (1.209–1.796) IADL High (Ref.) Low 1.441*** (1.259–1.650) 1.524*** (1.164–1.997) 1.422*** (1.220–1.658) MPCE Poorest (Ref.) Poor 0.908 (0.761–1.084) 0.931 (0.674–1.286) 0.903 (0.731–1.116) Middle 0.709*** (0.594–0.846) 0.721* (0.517–1.005) 0.702*** (0.568–0.867) Rich 0.645*** (0.531–0.784) 0.718* (0.492–1.048) 0.617*** (0.495–0.769) Richest 0.667*** (0.509–0.875) 0.763 (0.494–1.179) 0.635*** (0.454–0.887) Religion Hindu (Ref.) Muslim 1.277** (1.020–1.598) 1.161 (0.689–1.958) 1.318** (1.057–1.642) Others 1.135 (0.918–1.402) 1.154 (0.765–1.741) 1.131 (0.886–1.444) Caste SC/ST (Ref.) OBC 0.630*** (0.542–0.732) 0.551*** (0.420–0.722) 0.666*** (0.557–0.797) Others 0.726*** (0.611–0.863) 0.771 (0.547–1.088) 0.714*** (0.587–0.868) Place of residence Urban (Ref.) Rural 1.982*** (1.613–2.436) 2.047*** (1.384–3.027) 1.944*** (1.530–2.470) *if p < 0.05, **if p < 0.01, ***if p < 0.001; AOR: Adjusted Odds Ratio; CI: Confidence Interval; ADL: Activities of daily living; IADL: Instrumental activities of daily living; MPCE: Monthly per capita consumption expenditure Discussion Studies suggest that cognitive impairment is rising in India compared to other developing countries. Some studies report that cognitive impairment affects the quality of life of the elderly in India (Singh & Govil, 2016 ). Globally, studies have found that living alone is associated with the decline of cognitive functions due to older adult’s loneliness, social isolation, and less engagement in social activities (Evans et al., 2019 ). Whereas, social interaction and active participation in work reduce the risk of cognitive impairment of the older adults (Chanda, Srei, 2019 ). The factors that might influence cognitive functions among the older adults are socio-demographic characteristics such as age, occupation, living arrangement, and living in urban areas (Tripathi & Tiwari, 2013 ). In this context, using the nationally representative LASI survey, this study analysed the relationship between living arrangement and cognitive impairment among older adults in India. Our study finds that living arrangements have a significant relationship with cognitive impairments. Living alone, living with children and living with others were found higher prevalence of cognitive impairment. In contrast, living with spouse and living with spouse and children were found lower risk of cognitive impairment. Even this association remain strong in multivariate regression for living with spouse. Our result supported by the others study have shown that the community-dwelling older adults in Singapore who living with spouse, children and others relatives have lower risk of cognitive impairment (Roystonn et al., 2020 ). In contrast, when a person living alone with cognitive impairment have the higher chances of get injury and health related symptoms (Gibson & Richardson, 2017 ). The study also demonstrates that cognitive impairment is deteriorated by socio-demographic and health variables in India. Our result is corroborated with previous study found that living with children and with spouse older adults was more beneficial for good cognitive ability (Mazzuco et al., 2017 ). Further, we examined the role of living arrangements across gender and the result shows a greater risk of cognitive impairment among older women. The higher prevalence and likelihood of cognitive impairment were found among older woman, living with other, living alone, and living with children alone. But, when controlling the socio-demographic variables in multivariate analysis, we found that living with spouse and living with spouse and children older men has the higher risk of cognitive impairment than older women. This possible explanation is that living with a spouse was less beneficial for the older women because women were most of the time busy in cooking which is a sex-appropriate role for females (Koyano et al., 1988 ). Our study also found that marital status is highly correlated with cognitive impairment. Respondents who are not in union (widowed, divorced, and separated) are affects more by cognitive impairment, especially women are the most vulnerable group. The present study, in line with previous study in old age marital status is associated with cognitive (Gelder et al., 2006 ). Being currently in the union leads to a good cognitive function in the older adults later life because not in the union have a less emotional bond, less feeling of support, and poor relationship (Evans et al., 2019 ; Holmen, 2000 ). However, wealth quintile, health, social groups, and place of residence were associated with cognitive impairment in our study which correlates with a study from China (Zhou et al., 2016 ). Our result finds that cognitive impairment is associated with increasing wealth with more prevalence in males. Interestingly, several health factors such as poor health, ADL, and IADL dependency associated with cognitive impairment, might be the greater contribution of cognitive impairment and more prevalence to females (J. Wang et al., 2020 ). Additionally, a higher prevalence of cognitive impairment was varying culture, ethnicity, and socio-economic context (Chen & Zissimopoulos, 2018 ). Our result observed the high cognitive impairment differences between religious groups, caste groups, and rural and urban areas. Nevertheless, this study has limitations; we did not consider the communication from non-coresidence children to older people because the sample size is very small. In addition, our analysis is based on the national level cross-sectional perspective, but cognitive impairment may be differ by living arrangement across states. Conclusion The study finds that living arrangements played an important role in reducing the risk of cognitive impairment for older adults. We also find that older adults with lower socioeconomic status such as lower wealth quintile, less educated, difficulty with at least one ADL/IADL, poor self-rated health, female and currently not in union and growing of aged are more vulnerable to cognitive impairment. Therefore, cognitive impairment is an increasing public health threat for India. We suggest that the researchers and policymaker should take strategies through socio-economic pathways to reduce the cognitive impairment among the older person living in India, particularly among those who do not have familial support. Declarations Funding The authors have not declared grant for this research from any funding agency in the public, commercial or not-for-profit section. Patient and Public involvement No patient involved Ethics approval statement The Indian Council of Medical Research (ICMR) extended the necessary guidelines and ethics approval for undertaking the LASI survey. References Akter, M., Streatfield, P. K., Kabir, Z. N., Qiu, C., Cornelius, C., Wahlin, Å., Journal, S., Khanam, M. A., & Streatfield, P. K. (2011). Prevalence and Patterns of Multimorbidity among Elderly People in Rural Bangladesh: A Cross-sectional Study. Journal of Health, Population and Nutrition , 29 (4), 406–414. Bloom, D. E., Chatterji, S., Kowal, P., Lloyd-sherlock, P., Mckee, M., Rechel, B., Rosenberg, L., & Smith, J. P. (2014). Ageing 5 Macroeconomic implications of population ageing and selected policy responses. The Lancet , 6736 (14), 1–9. https://doi.org/10.1016/S0140-6736(14)61464-1 Chanda, Srei, R. M. (2019). Impact of transition in work status and social participation on cognitive performance among elderly in India. BMC Geriatric , 19 , 2–10. https://doi.org/https://doi.org/10.1186/s12877-019-1261-5 Chen, C., & Zissimopoulos, J. M. (2018). Racial and ethnic differences in trends in dementia prevalence and risk factors in the United States. Alzheimer’s & Dementia: Translational Research & Clinical Interventions , 4 , 510–520. https://doi.org/10.1016/j.trci.2018.08.009 Evans, I. E. M., Llewellyn, D. J., Matthews, F. E., Woods, R. T., Brayne, C., & Clare, L. (2019). Living alone and cognitive function in later life. Archives of Gerontology and Geriatrics , 81 (August 2018), 222–233. https://doi.org/10.1016/j.archger.2018.12.014 Gelder, B. M. Van, Tijhuis, M., Kalmijn, S., Giampaoli, S., Nissinen, A., & Kromhout, D. (2006). Marital Status and Living Situation During a 5-Year Period Are Associated With a Subsequent 10-Year Cognitive Decline in Older Men : The FINE Study . 61 (4), 213–219. Gibson, A. K., & Richardson, V. E. (2017). Living Alone With Cognitive Impairment : Findings From the National Health and Aging Trends Study . 32 (1), 56–62. https://doi.org/10.1177/1533317516673154 Holmen, K. K. E. B. W. (2000). Social and emotional loneliness among non-demented and demented elderly people. Archives of Gerontology and Geriatrics , 31 , 177–192. https://doi.org/doi: 10.1016/s0167-4943(00)00070-4. Kaisla Joutsenniemi, Tuija Martelin, Pekka Martikainen, Sami Pirkola, S. K. (2006). Living arrangements and mental health in Finland. Journal Epidemiol Community Health , 60 , 468–475. https://doi.org/10.1136/jech.2005.040741 Kim, H., Hong, S., & Kim, M. (2015). Living Arrangement, Social Connectedness, and Life Satisfaction among Korean Older Adults with Physical Disabilities: The Results from the National Survey on Persons with Disabilities. J Dev Phys Disabil , 27 , 307–321. https://doi.org/10.1007/s10882-014-9418-9 Koyano, W., Shibata, H., Nakazato, K., Haga, H., Suyama, Y., & Matsuzaki, T. (1988). Prevalence of disability in instrumental activities of daily living among elderly Japanese. Journals of Gerontology , 43 (2), 41–45. https://doi.org/10.1093/geronj/43.2.S41 Macknight, A. S. C., Fisk, K. R. J., & Guzman, S. G. D. A. (2002). The Effect of the Living Situation on the Severity of Dementia at Diagnosis. Dementia and Geriatric Cognitive Disorders , 1 , 40–45. https://doi.org/doi: 10.1159/000048632 Mazzuco, S., Meggiolaro, S., Ongaro, F., & Toffolutti, V. (2017). Living arrangement and cognitive decline among older people in Europe. Ageing and Society , 37 , 1111–1133. https://doi.org/doi:10.1017/S0144686X16000374 Obisesan, T. O., & Gillum, R. F. (2009). Cognitive function, social integration and mortality in a U.S. national cohort study of older adults. BMC Geriatrics , 9 , 1–9. https://doi.org/10.1186/1471-2318-9-33 Office of the Registrar General and Census Commissioner, I., & Affairs, M. of H. (2011). Census of India . Roystonn, K., Abdin, E., Shahwan, S., Zhang, Y., Sambasivam, R., Vaingankar, J. A., Mahendran, R., Chua, H. C., Chong, S. A., & Subramaniam, M. (2020). Living arrangements and cognitive abilities of community-dwelling older adults in Singapore. Psychogeriatrics , 20 (5), 625–635. https://doi.org/10.1111/psyg.12532 Sahin, E., Eker, E., International, S., & Health, M. (2007). Being Elderly in a Young Country: Geriatric Psychiatry in Turkey. International Journal of Mental Health , 36 (3), 66–72. Saito, T., Murata, C., Aida, J., & Kondo, K. (2017). Cohort study on living arrangements of older men and women and risk for basic activities of daily living disability: findings from the AGES project. BMC Geriatrics (2017) , 1–14. https://doi.org/10.1186/s12877-017-0580-7 Samanta, T., Chen, F., & Vanneman, R. (2015). Living Arrangements and Health of Older Adults in India. Journals of Gerontology - Series B Psychological Sciences and Social Sciences , 70 (6), 937–947. https://doi.org/10.1093/geronb/gbu164 Schatz, E., Ralston, M., Madhavan, S., Collinson, M. A., & Gómez-olivé, F. X. (2018). Living Arrangements , Disability and Gender of Older Adults Among Rural South Africa . 73 (6), 1112–1122. https://doi.org/10.1093/geronb/gbx081 Singh, P., & Govil, D. (2016). Cognitive Impairment and Quality of Life among Elderly in India. Applied Research in Quality of Life . https://doi.org/10.1007/s11482-016-9499-y Tripathi, R. K., & Tiwari, S. C. (2013). Cognitive Functioning of Community Dwelling Urban Older Adults with Reference to Socio-Demographic Variables. Indian Journal of Clinical Psychology , 40 (2), 92–102. United Nations, Department of Economic and Social Affairs, P. D. (2015). World Population Ageing . Wang, H., Chen, K., Pan, Y., Jing, F., & Liu, H. (2013). Associations and Impact Factors between Living Arrangements and Functional Disability among Older Chinese Adults . 8 (1). https://doi.org/10.1371/journal.pone.0053879 Wang, J., Xiao, L. D., Wang, K., Luo, Y., & Li, X. (2020). Gender Differences in Cognitive Impairment among Rural Elderly in China. International Journal of Environmental Research and Public Health Article , 1–16. Zhou, Z., Mao, F., Ma, J., Turner, J. S., & Fang, Y. (2016). A Longitudinal Analysis of the Association Between Living Arrangements and Health Among Older Adults in China. Research on Aging , 1–16. https://doi.org/10.1177/0164027516680854 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers agreed at journal 30 Aug, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 13 Jun, 2025 First submitted to journal 09 Jun, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6856596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491307764,"identity":"64077708-441b-4ddb-ab1d-e7e30cf44069","order_by":0,"name":"Kinkar Mandal","email":"data:image/png;base64,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","orcid":"","institution":"SRM University","correspondingAuthor":true,"prefix":"","firstName":"Kinkar","middleName":"","lastName":"Mandal","suffix":""},{"id":491307765,"identity":"6811d241-d3fe-4614-9cb2-eef816100510","order_by":1,"name":"T. Muhammad Muhammad","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"T.","middleName":"Muhammad","lastName":"Muhammad","suffix":""},{"id":491307766,"identity":"622e9726-e580-42ae-9c28-4b89745c64fa","order_by":2,"name":"Arun Bala Chandran","email":"","orcid":"","institution":"University of Maryland, College Park","correspondingAuthor":false,"prefix":"","firstName":"Arun","middleName":"Bala","lastName":"Chandran","suffix":""},{"id":491307767,"identity":"f0a7e552-e3a3-49f5-b2b2-051d425c771b","order_by":3,"name":"Karthick V","email":"","orcid":"","institution":"Institute for Social and Economic Change","correspondingAuthor":false,"prefix":"","firstName":"Karthick","middleName":"","lastName":"V","suffix":""}],"badges":[],"createdAt":"2025-06-09 17:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6856596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6856596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87754130,"identity":"0a6b13a0-ecce-4718-af52-8588385f8cc4","added_by":"auto","created_at":"2025-07-28 15:33:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":930970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6856596/v1/88a4b70f-1916-4d23-a79d-70371a9a14a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between living arrangements and cognitive impairment among older adults in India: Findings from LASI-2017-18","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIndia has the second-largest and fastest-growing older population in the world, and they account for 8.6 per cent of the total population in the country (Office of the Registrar General and Census Commissioner \u0026amp; Affairs, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). United Nations has projected that it will increase to 19 per cent by 2050 (United Nations, Department of Economic and Social Affairs, 2015). Such a change in Indian age structure is simultaneously associated with changes in family structure, and concerns regarding economic challenges, medical expenditure, and long-term support for the older adults (Bloom et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Studies have shown that cognitive impairments are increasing with an associated effect on the quality of life among the older adults (Singh \u0026amp; Govil, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, worldwide economic and social costs are also increasing to both individuals and societies (Mazzuco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Roystonn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) The living arrangement is associated with cognitive impairments and as a result, the older adults those who are living alone have poor cognitive functions. Similarly, social isolation, loneliness, and low engagement in social activities are also associated with poor cognitive function (Evans et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe cognitive functions of older adults measure multiple aspects of intellectual functions such as memory, concentration, decision making, ability to work, and social interactions (Roystonn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Studies suggest that higher risk of morbidity and mortality due to the lower cognitive functions and segregation of the older adults (Obisesan \u0026amp; Gillum, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). At the same time, cognitive functions are linked with the social fabric and living arrangements in the society. For instance, a higher level of psychological and psychiatric disorders corresponded to living alone (Kaisla et al., 2006). Concomitantly, the living arrangements play a role in several aspects of morbidity of the older adults. Having a partner and living with others are associated with higher levels of social interactions (Gelder et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Living in multigenerational households tends to improve good health among the older adults in India (Samanta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Living arrangement and disability were moderated by gender status have shown that older adults living in two-generation with linear-linked arrangements (households with/without spouse, with married adult children and their children) have lower risk of disability. Whereas living in Single-generation with complex-linked (with/without spouse, and any adult children and grandchildren) arrangements have a higher level of disability. Further, throughout the living arrangement the older women, those who are living in linear-linked multigenerational arrangement have a higher risk of disability than older women who are living in a single generation or other types of arrangements. On the other hand, older men who live in multigenerational or two-generation households have a higher level of disability (Schatz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similar types of study from China have found that living arrangement is associated with functional disability. That is married older adult living alone or children have better functional ability whereas, unmarried, widows, divorced and never-married older adult have higher prevalence of disability (H. Wang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile living arrangements influence cognitive impairment, it also confluences with other sociodemographic dimensions leading to more vulnerablitiy among certain sections of the population. For instance, in USA, higher rate of cognitive impairment have been found among widowed older women, especially with regards to Alzheimer or dementia (Gibson \u0026amp; Richardson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further, exploration of the influence of gender on cognitive impairment in the context of the Chinese oldest old found that women in low socioeconomic status and rural areas have high risk of cognitive impairment due to limited availability of health resources (J. Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, changing family structure lead to rising mental health among the Turkish older population, subsequently increasing their cognitive impairment (Sahin et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In rural Bangladesh, older women face multi-morbidity led cognitive and functional Impairments due to changing lifestyles, living conditions, behaviours, and working status (Akter et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). A study based in Japan showed that both men and women who are living without a spouse have a higher risk of basic activity of daily living disability even as living with non-spousal cohabitants led to a higher risk of basic activity of daily living disability (Saito et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Another study based in South Korea found that living alone physically disable older adults have poor quality of life satisfaction compared to older adults living with spouses or children (Kim et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMuch of the research has identified that living with children and others directly benefit to the older adults physical and psychological health. Because older adults receive physical, emotional and social support from their children. On the other hand, living alone or living with non-immediate kin often lead to increasing the risk of mortality and cognitive impairment (Macknight et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Roystonn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the context of India, very limited studies have looked at the association between living arrangements and cognitive impairment. While there has been some evidence that living in multigenerational household with spouse and/or children and grandchildren increased the likelihood of wellbeing and higher physical health benefit for the older adults (Samanta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) in India. To our knowledge, limited evidence exist in assessing the impact of living arrangements on cognitive impairment among the older adults in India using nationally representative longitudinal data.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and setting\u003c/h2\u003e\u003cp\u003eThe Longitudinal Ageing Study in India (LASI) is the first longitudinal nationally representative biannual panel survey of the older adult\u0026rsquo;s age 45 years and above conducted in 2017-18 by International Institute for Population Sciences (IIPS), Harvard T.H.Chan School of Public Health (HSPH) and University of Southern California (USC) in India. LASI data represents entire country (except Sikkim). The LASI Wave 1 was financially supported by the National Institute on Ageing (NIA/NIH), USA, United Nations Population Fund (UNFPA), India, and Ministry of Health and Family Welfare (MoHFW), Government of India. The technical support provided by International Technical Advisor Committee and ethical guidelines were approved by the Indian Council of Medical Research (ICMR). The detailed explanations of the sampling technique are available in the LASI Wave-1 report. The LASI data contains older adult\u0026rsquo;s related information of 72,250 respondents from the 42,949 households based on multistage stratified area probability cluster sampling design. A total of 31,464 older adult aged 60 and above, who lived more than 6 months in sample households in the last year, are included for the analysis in this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDependent variable\u003c/h3\u003e\n\u003cp\u003eTo analyse the impact of cognitive impairment among older adults, this study considered cognitive impairment (whether physical or mental impairment or both) as an outcome variable. The outcome variable is dichotomous (0, 1), i.e. if older person has cognitive impairment\u0026thinsp;=\u0026thinsp;1, otherwise\u0026thinsp;=\u0026thinsp;0.\u003c/p\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eThe explanatory variables used in this analysis are categorical variables. They are as follows. \u003cem\u003eAge\u003c/em\u003e is a category variable which are grouped into three such as 60\u0026ndash;69, 70\u0026ndash;79, 80\u0026thinsp;+\u0026thinsp;years. While \u003cem\u003esex\u003c/em\u003e is a dummy variable which has two category male and female, whereas \u003cem\u003emarital status\u003c/em\u003e are coded into two category such as currently in union (i.e. currently married) and not in union (i.e. either widowed or divorced or separated or never married). \u003cem\u003eEducation\u003c/em\u003e is another important variable included in the analysis which has three categories such as no-education/primary, secondary, and higher. The variable \u003cem\u003eWorking status\u003c/em\u003e is categorised into four such as never worked, not working, working, and retired. Whereas \u003cem\u003eself-rated health\u003c/em\u003e variable is coded into two categories as good (including excellent, very good, and good) and poor (including fair and poor). The variable Activity of Daily Living and Instrumental Activity of Daily Living consists of a series of activities such as dressing, walking, bathing, eating, getting in or out of bed, toilet, preparing a meal, shopping, telephone, medication, workaround, managing money and getting around. For the analysis, these activities are grouped into two as \u003cem\u003ehigh ADL/IADL\u003c/em\u003e (represents no difficulty in ADL/IADL) and \u003cem\u003elow ADL/IADL\u003c/em\u003e (represents difficulty with at least one ADL/IADL). While \u003cem\u003ewealth quantile\u003c/em\u003e variable is coded into five categories such as poorest, poorer, middle, richest, and richest. The variable \u003cem\u003ereligion of the older person\u003c/em\u003e consists of three groups such as Hindu, Muslim, and others. Similarly, the \u003cem\u003esocial group\u003c/em\u003e category consists of three dummy variables as Schedule Caste/Schedule Tribe (SC/ST), Other Backward Caste (OBC), and others. The dummy variable \u003cem\u003erespondent\u0026rsquo;s place of residence\u003c/em\u003e consists of rural and urban categories.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThis study used both descriptive statistics as well as econometric methods. Descriptive analysis is used to assess the frequency and percentage distribution of the elderly by various background characteristics of the elderly population. Differences in cognitive impairment across socioeconomic groups is analysed using bivariate estimation. The relationship between living arrangements and cognitive impairment is measured using logistic regression with separate models for males and females. A multivariable logistic regression model with adjusted odds ratio is used to control the predictor variables associated with outcome variables in our study. The results are reported at 95 per cent confidence intervals (CIs) and \u003cem\u003ep\u003c/em\u003e-values considered for the statistically significant results are p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe socioeconomic and demographic characteristics of the study participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A proportion of 5.68 per cent of older adults lived alone, among them 2.52 per cent are men and 8.53 per cent are women. The older persons living with their spouse and children were around 41 per cent and their corresponding figure for men and women were 54 per cent and 28 per cent, respectively. A proportion of 11.2 per cent of respondents were aged 80\u0026thinsp;+\u0026thinsp;years. The proportion of older men who were currently in union is nearly double (81 per cent) in comparison to older women (44 per cent). Around 56 per cent of women and only 19 per cent of men were not in marital union. In case of education level, more women (86 per cent) compared to men (61 per cent) had no education. A proportion of 74 per cent of older adults had no education/primary or had primary level of education and only 8 per cent had reported higher education.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-economic profile of the study participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;31,464)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;14,931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;16,533)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eLiving arrangements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,397\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse and children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12,779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e40.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6,656\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18,410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9,678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9,678\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,803\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrently in union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19,391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e81.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12,242\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7,211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot in union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12,073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9,155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo/primary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23,289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9,202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e85.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14,046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,808\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e513\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eWork status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7665\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11,470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5,311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3,088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSRH*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23,341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e77.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11,490\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e74.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11,860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eADL*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23,887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e79.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11,881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e73.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12,019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eIADL*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16,162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9,175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7,029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15,133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5,827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9,264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMPCE quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3,681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3,611\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3,331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,902\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3,136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,607\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25,871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e82.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12,386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e82.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13,484\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,781\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1,101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCaste\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSC/ST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14,231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6,925\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e44.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7,308\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,728\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4,556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22,196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5,044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10,879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11,322\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003e*Sample size may differ due to missing cases; MPCE: Monthly per capita consumption expenditure; ADL: Activities of daily living; IADL: Instrumental activities of daily living\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe prevalence estimates of cognitive impairment among older adults are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Cognitive impairment was higher among older adults who were living with children, living with others, and living alone, whereas it was lowest among those who lived with spouse or lived with spouse and children. The prevalence of cognitive impairment was higher among women (20.03 per cent) than men (7.14 per cent). Further, the prevalence was higher in those women who lived with others (28.2 per cent) than those who lived with others only (10.6 per cent). Similarly, older women who lived with children (23.8 per cent), lived alone (22.8 per cent), lived with spouse (17.3), lived with spouse and children (14.2) were having higher cognitive impairments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBivariate estimates for cognitive impairment by background characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eLiving arrangements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving alone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse and children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;69 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrently in union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot in union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo/primary education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eWork status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever worked\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSRH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eADL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e30.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eIADL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMPCE quintile\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRicher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHindu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCaste\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSC/ST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003e%: Per cent; ADL: Activities of daily living; IADL: Instrumental activities of daily living; MPCE: Monthly per capita consumption expenditure\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe multivariable logistic regression estimates are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The general the respondents living with spouse (OR 3.11), living with spouse and children (OR 2.35), living with children (OR 1.22), and living with others (OR 1.27) were less likely to have cognitive impairment than respondents living alone. The risk of cognitive impairment was three times more likely among older men living with spouse only (OR 12.38) compared to their living alone counterparts. Higher odds of cognitive impairment were observed among older men who lived with spouse and children (OR 9.049). On the other hand, older adults living with children and living with others had higher odds of cognitive impairment compared to their living alone counterparts. Whereas, older women had higher odds of cognitive impairment (OR 2.368) compared to men.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression estimates of cognitive impairment (AOR) by socioeconomic and health characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eLiving arrangements\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving alone (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.114*** (1.425\u0026ndash;6.803)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.38*** (2.281\u0026ndash;67.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.444** (1.015\u0026ndash;5.887)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with spouse and children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.349** (1.083\u0026ndash;5.097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.049** (1.679\u0026ndash;48.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.856 (0.774\u0026ndash;4.452)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.224* (0.965\u0026ndash;1.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.978** (1.014\u0026ndash;3.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.155 (0.891\u0026ndash;1.498)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLiving with others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.271 (0.939\u0026ndash;1.721)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.824 (0.805\u0026ndash;4.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.246 (0.894\u0026ndash;1.737)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.368*** (2.019\u0026ndash;2.778)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u0026ndash;69 (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.554*** (1.321\u0026ndash;1.828)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.373** (1.019\u0026ndash;1.851)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.611*** (1.334\u0026ndash;1.946)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.537*** (2.074\u0026ndash;3.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.200*** (1.579\u0026ndash;3.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.765*** (2.145\u0026ndash;3.565)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrently in union (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot in union\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.837*** (1.357\u0026ndash;5.931)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.535** (1.416\u0026ndash;30.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.384** (1.038\u0026ndash;5.478)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo/primary (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0948*** (0.0651\u0026ndash;0.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.149*** (0.0970\u0026ndash;0.229)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0364*** (0.0180\u0026ndash;0.0736)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0627*** (0.0261\u0026ndash;0.151)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0773*** (0.0277\u0026ndash;0.216)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0371*** (0.00858\u0026ndash;0.161)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eWork status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever worked (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot working\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.176* (0.991\u0026ndash;1.395)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.919 (0.499\u0026ndash;1.694)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.229** (1.022\u0026ndash;1.478)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWorking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.965 (0.799\u0026ndash;1.165)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.864 (0.458\u0026ndash;1.632)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.886 (0.719\u0026ndash;1.091)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.615* (0.377\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.440** (0.198\u0026ndash;0.975)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.740 (0.294\u0026ndash;1.866)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eSRH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGood (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.129* (0.979\u0026ndash;1.302)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.167 (0.902\u0026ndash;1.509)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.108 (0.934\u0026ndash;1.315)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eADL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.450*** (1.226\u0026ndash;1.714)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.371** (1.001\u0026ndash;1.877)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.473*** (1.209\u0026ndash;1.796)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eIADL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.441*** (1.259\u0026ndash;1.650)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.524*** (1.164\u0026ndash;1.997)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.422*** (1.220\u0026ndash;1.658)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMPCE\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorest (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.908 (0.761\u0026ndash;1.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.931 (0.674\u0026ndash;1.286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.903 (0.731\u0026ndash;1.116)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.709*** (0.594\u0026ndash;0.846)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.721* (0.517\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.702*** (0.568\u0026ndash;0.867)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRich\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.645*** (0.531\u0026ndash;0.784)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.718* (0.492\u0026ndash;1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.617*** (0.495\u0026ndash;0.769)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRichest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.667*** (0.509\u0026ndash;0.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.763 (0.494\u0026ndash;1.179)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.635*** (0.454\u0026ndash;0.887)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHindu (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.277** (1.020\u0026ndash;1.598)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.161 (0.689\u0026ndash;1.958)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.318** (1.057\u0026ndash;1.642)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.135 (0.918\u0026ndash;1.402)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.154 (0.765\u0026ndash;1.741)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.131 (0.886\u0026ndash;1.444)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eCaste\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSC/ST (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOBC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.630*** (0.542\u0026ndash;0.732)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.551*** (0.420\u0026ndash;0.722)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.666*** (0.557\u0026ndash;0.797)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.726*** (0.611\u0026ndash;0.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.771 (0.547\u0026ndash;1.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.714*** (0.587\u0026ndash;0.868)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.982*** (1.613\u0026ndash;2.436)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.047*** (1.384\u0026ndash;3.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.944*** (1.530\u0026ndash;2.470)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*if p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **if p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***if p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AOR: Adjusted Odds Ratio; CI: Confidence Interval; ADL: Activities of daily living; IADL: Instrumental activities of daily living; MPCE: Monthly per capita consumption expenditure\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eStudies suggest that cognitive impairment is rising in India compared to other developing countries. Some studies report that cognitive impairment affects the quality of life of the elderly in India (Singh \u0026amp; Govil, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Globally, studies have found that living alone is associated with the decline of cognitive functions due to older adult\u0026rsquo;s loneliness, social isolation, and less engagement in social activities (Evans et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whereas, social interaction and active participation in work reduce the risk of cognitive impairment of the older adults (Chanda, Srei, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The factors that might influence cognitive functions among the older adults are socio-demographic characteristics such as age, occupation, living arrangement, and living in urban areas (Tripathi \u0026amp; Tiwari, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In this context, using the nationally representative LASI survey, this study analysed the relationship between living arrangement and cognitive impairment among older adults in India.\u003c/p\u003e\u003cp\u003eOur study finds that living arrangements have a significant relationship with cognitive impairments. Living alone, living with children and living with others were found higher prevalence of cognitive impairment. In contrast, living with spouse and living with spouse and children were found lower risk of cognitive impairment. Even this association remain strong in multivariate regression for living with spouse. Our result supported by the others study have shown that the community-dwelling older adults in Singapore who living with spouse, children and others relatives have lower risk of cognitive impairment (Roystonn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, when a person living alone with cognitive impairment have the higher chances of get injury and health related symptoms (Gibson \u0026amp; Richardson, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study also demonstrates that cognitive impairment is deteriorated by socio-demographic and health variables in India. Our result is corroborated with previous study found that living with children and with spouse older adults was more beneficial for good cognitive ability (Mazzuco et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further, we examined the role of living arrangements across gender and the result shows a greater risk of cognitive impairment among older women. The higher prevalence and likelihood of cognitive impairment were found among older woman, living with other, living alone, and living with children alone. But, when controlling the socio-demographic variables in multivariate analysis, we found that living with spouse and living with spouse and children older men has the higher risk of cognitive impairment than older women. This possible explanation is that living with a spouse was less beneficial for the older women because women were most of the time busy in cooking which is a sex-appropriate role for females (Koyano et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study also found that marital status is highly correlated with cognitive impairment. Respondents who are not in union (widowed, divorced, and separated) are affects more by cognitive impairment, especially women are the most vulnerable group. The present study, in line with previous study in old age marital status is associated with cognitive (Gelder et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Being currently in the union leads to a good cognitive function in the older adults later life because not in the union have a less emotional bond, less feeling of support, and poor relationship (Evans et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Holmen, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, wealth quintile, health, social groups, and place of residence were associated with cognitive impairment in our study which correlates with a study from China (Zhou et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our result finds that cognitive impairment is associated with increasing wealth with more prevalence in males. Interestingly, several health factors such as poor health, ADL, and IADL dependency associated with cognitive impairment, might be the greater contribution of cognitive impairment and more prevalence to females (J. Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, a higher prevalence of cognitive impairment was varying culture, ethnicity, and socio-economic context (Chen \u0026amp; Zissimopoulos, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our result observed the high cognitive impairment differences between religious groups, caste groups, and rural and urban areas. Nevertheless, this study has limitations; we did not consider the communication from non-coresidence children to older people because the sample size is very small. In addition, our analysis is based on the national level cross-sectional perspective, but cognitive impairment may be differ by living arrangement across states.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study finds that living arrangements played an important role in reducing the risk of cognitive impairment for older adults. We also find that older adults with lower socioeconomic status such as lower wealth quintile, less educated, difficulty with at least one ADL/IADL, poor self-rated health, female and currently not in union and growing of aged are more vulnerable to cognitive impairment. Therefore, cognitive impairment is an increasing public health threat for India. We suggest that the researchers and policymaker should take strategies through socio-economic pathways to reduce the cognitive impairment among the older person living in India, particularly among those who do not have familial support.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not declared grant for this research from any funding agency in the public, commercial or not-for-profit section. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Public involvement\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo patient involved\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Indian Council of Medical Research (ICMR) extended the necessary guidelines and ethics approval for undertaking the LASI survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkter, M., Streatfield, P. K., Kabir, Z. N., Qiu, C., Cornelius, C., Wahlin, \u0026Aring;., Journal, S., Khanam, M. A., \u0026amp; Streatfield, P. K. (2011). Prevalence and Patterns of Multimorbidity among Elderly People in Rural Bangladesh: A Cross-sectional Study. \u003cem\u003eJournal of Health, Population and Nutrition\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(4), 406\u0026ndash;414.\u003c/li\u003e\n\u003cli\u003eBloom, D. E., Chatterji, S., Kowal, P., Lloyd-sherlock, P., Mckee, M., Rechel, B., Rosenberg, L., \u0026amp; Smith, J. P. (2014). Ageing 5 Macroeconomic implications of population ageing and selected policy responses. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e6736\u003c/em\u003e(14), 1\u0026ndash;9. https://doi.org/10.1016/S0140-6736(14)61464-1\u003c/li\u003e\n\u003cli\u003eChanda, Srei, R. M. (2019). Impact of transition in work status and social participation on cognitive performance among elderly in India. \u003cem\u003eBMC Geriatric\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 2\u0026ndash;10. https://doi.org/https://doi.org/10.1186/s12877-019-1261-5\u003c/li\u003e\n\u003cli\u003eChen, C., \u0026amp; Zissimopoulos, J. M. (2018). Racial and ethnic differences in trends in dementia prevalence and risk factors in the United States. \u003cem\u003eAlzheimer\u0026rsquo;s \u0026amp; Dementia: Translational Research \u0026amp; Clinical Interventions\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 510\u0026ndash;520. https://doi.org/10.1016/j.trci.2018.08.009\u003c/li\u003e\n\u003cli\u003eEvans, I. E. M., Llewellyn, D. J., Matthews, F. E., Woods, R. T., Brayne, C., \u0026amp; Clare, L. (2019). Living alone and cognitive function in later life. \u003cem\u003eArchives of Gerontology and Geriatrics\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(August 2018), 222\u0026ndash;233. https://doi.org/10.1016/j.archger.2018.12.014\u003c/li\u003e\n\u003cli\u003eGelder, B. M. Van, Tijhuis, M., Kalmijn, S., Giampaoli, S., Nissinen, A., \u0026amp; Kromhout, D. (2006). \u003cem\u003eMarital Status and Living Situation During a 5-Year Period Are Associated With a Subsequent 10-Year Cognitive Decline in Older Men : The FINE Study\u003c/em\u003e. \u003cem\u003e61\u003c/em\u003e(4), 213\u0026ndash;219.\u003c/li\u003e\n\u003cli\u003eGibson, A. K., \u0026amp; Richardson, V. E. (2017). \u003cem\u003eLiving Alone With Cognitive Impairment : Findings From the National Health and Aging Trends Study\u003c/em\u003e. \u003cem\u003e32\u003c/em\u003e(1), 56\u0026ndash;62. https://doi.org/10.1177/1533317516673154\u003c/li\u003e\n\u003cli\u003eHolmen, K. K. E. B. W. (2000). Social and emotional loneliness among non-demented and demented elderly people. \u003cem\u003eArchives of Gerontology and Geriatrics\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 177\u0026ndash;192. https://doi.org/doi: 10.1016/s0167-4943(00)00070-4.\u003c/li\u003e\n\u003cli\u003eKaisla Joutsenniemi, Tuija Martelin, Pekka Martikainen, Sami Pirkola, S. K. (2006). Living arrangements and mental health in Finland. \u003cem\u003eJournal Epidemiol Community Health\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e, 468\u0026ndash;475. https://doi.org/10.1136/jech.2005.040741\u003c/li\u003e\n\u003cli\u003eKim, H., Hong, S., \u0026amp; Kim, M. (2015). Living Arrangement, Social Connectedness, and Life Satisfaction among Korean Older Adults with Physical Disabilities: The Results from the National Survey on Persons with Disabilities. \u003cem\u003eJ Dev Phys Disabil\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e, 307\u0026ndash;321. https://doi.org/10.1007/s10882-014-9418-9\u003c/li\u003e\n\u003cli\u003eKoyano, W., Shibata, H., Nakazato, K., Haga, H., Suyama, Y., \u0026amp; Matsuzaki, T. (1988). Prevalence of disability in instrumental activities of daily living among elderly Japanese. \u003cem\u003eJournals of Gerontology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(2), 41\u0026ndash;45. https://doi.org/10.1093/geronj/43.2.S41\u003c/li\u003e\n\u003cli\u003eMacknight, A. S. C., Fisk, K. R. J., \u0026amp; Guzman, S. G. D. A. (2002). The Effect of the Living Situation on the Severity of Dementia at Diagnosis. \u003cem\u003eDementia and Geriatric Cognitive Disorders\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e, 40\u0026ndash;45. https://doi.org/doi: 10.1159/000048632\u003c/li\u003e\n\u003cli\u003eMazzuco, S., Meggiolaro, S., Ongaro, F., \u0026amp; Toffolutti, V. (2017). Living arrangement and cognitive decline among older people in Europe. \u003cem\u003eAgeing and Society\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e, 1111\u0026ndash;1133. https://doi.org/doi:10.1017/S0144686X16000374\u003c/li\u003e\n\u003cli\u003eObisesan, T. O., \u0026amp; Gillum, R. F. (2009). Cognitive function, social integration and mortality in a U.S. national cohort study of older adults. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e, 1\u0026ndash;9. https://doi.org/10.1186/1471-2318-9-33\u003c/li\u003e\n\u003cli\u003eOffice of the Registrar General and Census Commissioner, I., \u0026amp; Affairs, M. of H. (2011). \u003cem\u003eCensus of India\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRoystonn, K., Abdin, E., Shahwan, S., Zhang, Y., Sambasivam, R., Vaingankar, J. A., Mahendran, R., Chua, H. C., Chong, S. A., \u0026amp; Subramaniam, M. (2020). Living arrangements and cognitive abilities of community-dwelling older adults in Singapore. \u003cem\u003ePsychogeriatrics\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(5), 625\u0026ndash;635. https://doi.org/10.1111/psyg.12532\u003c/li\u003e\n\u003cli\u003eSahin, E., Eker, E., International, S., \u0026amp; Health, M. (2007). Being Elderly in a Young Country: Geriatric Psychiatry in Turkey. \u003cem\u003eInternational Journal of Mental Health\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(3), 66\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eSaito, T., Murata, C., Aida, J., \u0026amp; Kondo, K. (2017). Cohort study on living arrangements of older men and women and risk for basic activities of daily living disability: findings from the AGES project. \u003cem\u003eBMC Geriatrics (2017)\u003c/em\u003e, 1\u0026ndash;14. https://doi.org/10.1186/s12877-017-0580-7\u003c/li\u003e\n\u003cli\u003eSamanta, T., Chen, F., \u0026amp; Vanneman, R. (2015). Living Arrangements and Health of Older Adults in India. \u003cem\u003eJournals of Gerontology - Series B Psychological Sciences and Social Sciences\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(6), 937\u0026ndash;947. https://doi.org/10.1093/geronb/gbu164\u003c/li\u003e\n\u003cli\u003eSchatz, E., Ralston, M., Madhavan, S., Collinson, M. A., \u0026amp; G\u0026oacute;mez-oliv\u0026eacute;, F. X. (2018). \u003cem\u003eLiving Arrangements , Disability and Gender of Older Adults Among Rural South Africa\u003c/em\u003e. \u003cem\u003e73\u003c/em\u003e(6), 1112\u0026ndash;1122. https://doi.org/10.1093/geronb/gbx081\u003c/li\u003e\n\u003cli\u003eSingh, P., \u0026amp; Govil, D. (2016). Cognitive Impairment and Quality of Life among Elderly in India. \u003cem\u003eApplied Research in Quality of Life\u003c/em\u003e. https://doi.org/10.1007/s11482-016-9499-y\u003c/li\u003e\n\u003cli\u003eTripathi, R. K., \u0026amp; Tiwari, S. C. (2013). Cognitive Functioning of Community Dwelling Urban Older Adults with Reference to Socio-Demographic Variables. \u003cem\u003eIndian Journal of Clinical Psychology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(2), 92\u0026ndash;102.\u003c/li\u003e\n\u003cli\u003eUnited Nations, Department of Economic and Social Affairs, P. D. (2015). \u003cem\u003eWorld Population Ageing\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eWang, H., Chen, K., Pan, Y., Jing, F., \u0026amp; Liu, H. (2013). \u003cem\u003eAssociations and Impact Factors between Living Arrangements and Functional Disability among Older Chinese Adults\u003c/em\u003e. \u003cem\u003e8\u003c/em\u003e(1). https://doi.org/10.1371/journal.pone.0053879\u003c/li\u003e\n\u003cli\u003eWang, J., Xiao, L. D., Wang, K., Luo, Y., \u0026amp; Li, X. (2020). Gender Differences in Cognitive Impairment among Rural Elderly in China. \u003cem\u003eInternational Journal of Environmental Research and Public Health Article\u003c/em\u003e, 1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eZhou, Z., Mao, F., Ma, J., Turner, J. S., \u0026amp; Fang, Y. (2016). A Longitudinal Analysis of the Association Between Living Arrangements and Health Among Older Adults in China. \u003cem\u003eResearch on Aging\u003c/em\u003e, 1\u0026ndash;16. https://doi.org/10.1177/0164027516680854\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-6856596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6856596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eAs a care and support system of older adults, living arrangements are associated with the older adult\u0026rsquo;s cognitive impairment. Such family arrangements play an important role in the wellbeing of the older adults with clear distinction across living with children and spouse with that of extended kin. The present study explores the association between living arrangements and cognitive impairment among older adults in India.\u003c/p\u003e\u003ch2\u003eDesign\u003c/h2\u003e\u003cp\u003eCross sectional study\u003c/p\u003e\u003ch2\u003eSetting\u003c/h2\u003e\u003cp\u003eFor this study, we used the Longitudinal Ageing Study (LASI-2017-18) data, a nationally representative survey of 31,464 older people aged 60 years and above in India. Bivariate and multivariable regression analysis were performed to fulfil the study objective.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe find that older women (20.03%) were more vulnerable to cognitive impairment as likelihood reported compared to older men (7.13%) in India. After controlling the variables separately for men and women, we find that cognitive impairment, among men and women, increases when age increases. Living with spouse older men (OR: 12.38) are more likely to be affected with the cognitive impairment than women (OR: 2.44). Further, less educated, difficulties with at least one ADL/IADL older adults have higher chances of getting affected with cognitive impairment. Cognitive impairment increases with decrease wealth that is lower quintile older adults are more likely to be cognitively impaired, especially women are the most affected.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eLiving arrangements play a vital role in the well-being of the older adults in India. Older adults who living with others, living with children and lives alone have higher prevalence of cognitive function, especially women are more vulnerable. Study suggests that policy interventions by the government would benefit for the living alone older women.\u003c/p\u003e","manuscriptTitle":"Relationship between living arrangements and cognitive impairment among older adults in India: Findings from LASI-2017-18","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 15:25:52","doi":"10.21203/rs.3.rs-6856596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-10-06T07:34:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-25T20:44:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123832882509512038570642006903134151248","date":"2025-09-22T19:00:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193776420307115344999314680889688806608","date":"2025-08-30T16:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158597157672212805696597652326110487233","date":"2025-07-26T16:37:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-24T16:31:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-13T06:37:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-13T06:35:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Ageing International","date":"2025-06-09T17:22:08+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":"fcd78d60-d0f5-41c9-907d-7d068b962d55","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T15:25:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 15:25:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6856596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6856596","identity":"rs-6856596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}