{"paper_id":"b63fafe6-e577-499e-b81d-1ef30910411a","body_text":"Unpacking the Factors Associated with Loneliness: An Inferential Analysis from the INTERACT Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unpacking the Factors Associated with Loneliness: An Inferential Analysis from the INTERACT Study Austen El-Osta, Mahmoud Al-Ammouri, Aos Alaa, Sami Altalib, Agustin Tristán-López, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9370594/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Loneliness is a pressing public health concern with wide-ranging impacts on mental, physical and social wellbeing. Building on the INTERACT Study which represents one of the largest volunteer-based studies of loneliness and social disconnection conducted in the UK, this paper explores demographic, social and health-related predictors of loneliness and social capital, using multiple validated measures. Methods We analysed cross-sectional data from 135,725 community-dwelling adults across England. Loneliness was assessed using both the UCLA 3-item Loneliness Scale and the ONS Direct Measure of Loneliness (DMOL). Social capital was measured using a composite scale of neighbourhood trust, cohesion and reciprocity. Multivariable ordinal logistic regression was used to examine factors associated with loneliness; binary logistic regression was used to analyse correlates of high versus low social capital. Results Younger age (particularly 16–25), being single, unemployed or living with disability were consistently associated with higher loneliness across both scales. In contrast, greater social contact (having nine or more friends or relatives) was strongly protective (UCLA: aOR 0.09; DMOL: aOR 0.16). University education was associated with higher loneliness on the UCLA scale but lower loneliness on the DMOL. High social capital was more prevalent among older, married and retired individuals and strongly predicted lower loneliness. Respondents with long-term conditions or disability had reduced odds of high social capital (aORs 0.82 and 0.77 respectively). Conclusions This study highlights consistent sociodemographic and social factors associated with loneliness, as well as the protective role of social capital. Findings highlight population subgroups that may warrant prioritisation in future intervention research and policy prescriptions that address social connection among young adults, single people, the unemployed and individuals in poor health. Given the non-probability sampling design, findings are not intended to estimate population parameters, but support further evaluation of strategies that strengthen neighbourhood cohesion and social infrastructure to mitigate loneliness and strengthen community wellbeing. Loneliness Social isolation Social capital Public health Mental health Social cohesion Community interventions Figures Figure 1 Figure 2 Figure 3 Background Loneliness is increasingly recognised as a critical public health challenge, associated with profound implications for mental, physical and social wellbeing[ 1 ]. Accumulating evidence links loneliness to depression, anxiety, cardiovascular disease, cognitive decline and elevated mortality risk highlighting its significance as a biopsychosocial determinant of health[ 2 ]. While the COVID-19 pandemic intensified public awareness and policy interest, the epidemiology of loneliness remains underexplored, particularly in terms of its social determinants, distribution across diverse populations and modifiable protective factors such as social capital[ 3 ]. To address this evidence gap, the Measuring Loneliness in the UK (INTERACT) Study which uses UCLA-3 item Loneliness Scale [ 4 ] and Office for National Statistics (ONS) Direct Measure of Loneliness (DMOL) [ 5 ]scales to measure subjective loneliness was launched as one of the largest volunteer-based investigations of loneliness and social disconnection conducted in the United Kingdom. The first paper in this series ( El-Osta et al., 2026a ) reported descriptive findings from over 135,000 adults, highlighting that loneliness is both widespread and unequally distributed. Young adults, ethnic minorities, urban residents and those living with disability or unemployment were identified as high-risk groups. Older adults, especially those aged 65 and over, also emerged as a population of interest. Although they reported lower average loneliness scores than younger individuals, the absolute number of older people affected by chronic loneliness was substantial. Within this group, widowed individuals, those living alone and people with long-term conditions or disabilities exhibited significantly higher levels of social disconnection. Importantly, the descriptive findings highlighted stark variations in loneliness levels even among individuals with seemingly similar sociodemographic characteristics, suggesting the influence of underlying structural and contextual factors such as social capital. This second paper builds on those foundational findings by conducting one of the largest inferential analyses of loneliness risk factors to date, drawing on a sample of over 135,000 participants and a broad set of socio-demographic, social and health-related variables across two validated loneliness measures. It applies advanced statistical modelling to estimate adjusted associations between loneliness and key sociodemographic, socioeconomic and health-related characteristics across the life course without implying predictive performance or causal relationships. In doing so, it sheds new light on the nuanced drivers of loneliness among diverse age groups, situating ageing within the broader epidemiology of loneliness. A key focus of this study is the role of social capital[ 3 ], measured through indicators such as neighbourhood trust, perceived support and community cohesion as a protective factor that may buffer against loneliness risk. This is particularly relevant to older adults, who often face reduced mobility, bereavement and shrinking social networks[ 6 ]. The hypothesis that place-based, relational and community-level assets can offset loneliness among older populations is explored using adjusted regression models and spatial analysis. Building on the descriptive architecture established in Paper 1 ( El-Osta et al., 2026a ), this analysis shifts from pattern characterisation to estimation of conditional associations within the sample, enabling quantification of gradients and the identification of converging correlates across measures. This second paper makes three primary contributions. First, it provides effect size estimates for a broad set of loneliness predictors, including common variables such as household composition, caregiving roles and frequency of social contact. Second, it offers empirical validation of the theorised buffering effect of social capital, an insight with direct relevance to ageing populations. Third, it explores variation in loneliness and social capital across population subgroups, considering contextual factors where measured. Crucially, this paper highlights age-related differences in loneliness across the adult population and supports the need for age-sensitive and life-stage-informed approaches to loneliness prevention, affirming the need for multigenerational public health strategies that acknowledge both the distinct and overlapping loneliness risks faced by younger and older people[ 6 , 7 ]. The findings presented here offer essential direction for designing effective, targeted and equity-informed interventions, particularly those aimed at improving social connectedness and resilience in an ageing society. Study aims The primary aim of this study was to estimate adjusted associations between loneliness and key demographic, socioeconomic, social and health-related factors within a large non-probability sample. Secondary aims were to (i) describe the distribution of loneliness and social capital across sociodemographic and health-related subgroups, (ii) examine associations between these factors and subjective loneliness using two validated measures, (iii) assess the relationship between social capital and loneliness, and (iv) identify consistent patterns across models to inform hypothesis generation. Methods Social connection is conceptualised as an overarching construct encompassing subjective loneliness, structural social contact (e.g., number of friends or relatives) and contextual dimensions captured through social capital. These components are analytically distinguished, with loneliness treated as a subjective outcome, social contact as a structural exposure, and social capital as a contextual construct. Study Design and Data Source Full methodological details, including study design, recruitment and data collection procedures, are provided in Paper 1 of this series (El-Osta et al., 2026a). Participants and Sampling A total of 135,725 adults aged 16 years and older were recruited via NHS primary care networks, voluntary sector organizations and the National Institute for Health and Care Research (NIHR) Be Part of Research Network. The recruitment strategy aimed at demographic and geographic diversity, with targeted outreach to underrepresented populations. Eligibility criteria and exclusion details have been described elsewhere (El-Osta et al., 2026a). Measures Loneliness was assessed indirectly using the validated UCLA Loneliness Scale, with response options categorized as never/hardly ever (scored as 1), some of the time (scored as 2) and often (scored as 3). Each question was scored from 1 to 3 and the total score ranged from 3 to 9. The total scores were further categorized into three levels of loneliness: no loneliness (score = 3), moderate loneliness (score = 4–6) and severe loneliness (score = 7–9). Given the absence of universally agreed cut-offs for the UCLA-3, scores were categorised pragmatically to reflect increasing severity while preserving ordinal structure for analysis. This pragmatic categorisation approach is consistent with prior population-based studies that have grouped UCLA-3 scores to reflect increasing severity while preserving ordinal structure for regression modelling[ 8 ]. A single-item DMOL recommended by the ONS was also included in our study. Crucially, the primary analyses retained the ordinal nature of the outcome, minimising reliance on arbitrary thresholds. Differences between UCLA-3 and DMOL estimates reflect established methodological differences between indirect multi-item and direct single-item measures, rather than inconsistency in findings. To measure social capital, we used a seven-item Likert scale with four response categories, based on an instrument developed by Sampson et al [ 9 ]. Response categories were dichotomised such that “agree” or “strongly agree” were scored as 1, and “disagree” or “strongly disagree” as 0, reflecting the presence versus absence of key neighbourhood attributes (e.g., trust, cohesion, shared values), consistent with prior applications of collective efficacy frameworks. Scores were summed to produce an overall index ranging from 0 to 7. Two negatively worded items (“People in this neighbourhood generally don’t get along with each other” and “People in this neighbourhood do not share the same values”) were reverse-coded. The summed index was subsequently dichotomised at the median (score = 4) to classify low (0–4) versus high (> 4) social capital, facilitating interpretation and binary logistic regression modelling of high versus low social capital. Dichotomisation was used to facilitate interpretability in regression models; however, we acknowledge this may reduce variability and should be interpreted as a pragmatic modelling decision rather than a theoretically derived threshold. While this approach may reduce granularity, it supports comparability with existing literature and population-level analyses [ 10 ]. All scores for loneliness and social capital in this analysis were derived using a classical test theory approach by summing item responses. Rasch model-derived person measures in logits are reported separately in Paper 3 of this series [ 11 ]. In addition to loneliness and social capital, a range of socio-demographic, social and health-related variables were included as predictors selected a priori based on established theoretical frameworks (see Statistical Analysis). These comprised age group, gender, ethnicity, educational attainment, employment status and marital status, as well as indicators of social connectedness (number of friends and relatives, household size, presence of children and pet ownership) and health status (self-reported disability and long-term conditions). All variables were self-reported and categorised as described in the Statistical Analysis section and corresponding tables. Handling of missing data We assessed the extent of missing data across all variables in the dataset. Overall, 6.8% of the dataset values were missing. Prior to imputation, we used Little’s MCAR test to examine the missing data mechanism, which suggested that the data were not missing completely at random. Therefore, we proceeded with multiple imputations using the Multivariate Imputation by Chained Equations (MICE) algorithm, implemented via the ‘ mice ’ package in R for comparison and robustness checking with complete case analysis. The missing data pattern was inspected using the md.pattern() function and methods were tailored based on variable types: polytomous regression ( polyreg ) for nominal categorical variables (e.g., gender, ethnicity), proportional odds models ( polr ) for ordinal variables (e.g., age group, number of relatives and friends, household size) and logistic regression ( logreg ) for binary variables (e.g., having children, pet ownership). We generated five imputed datasets (m = 5) with 40 iterations per dataset (maxit = 40), setting a random seed (123) for reproducibility. Diagnostic plots confirmed algorithm convergence, showing stable mean and standard deviation trends across iterations. Post-imputation, analyses were conducted on each dataset individually and the results were pooled using Rubin’s rules to account for imputation uncertainty and derive final estimates Supplementary file 1 . The primary analyses presented in this paper are based on complete-case models, with multiply imputed analyses conducted as sensitivity analyses to assess robustness. Statistical Analysis Predictor variables were selected a priori based on established theoretical frameworks, including the Social Determinants of Health [ 12 ] and biopsychosocial models of social isolation [ 13 ]. These frameworks informed the inclusion of structural (e.g., education, employment), demographic (e.g., age, gender, ethnicity), relational (e.g., marital status, number of friends and relatives, household composition), behavioural/contextual (e.g., pet ownership, caregiving roles) and health-related factors (e.g., disability, long-term conditions), each representing distinct pathways through which social connection and loneliness may be shaped. Separate analyses were conducted for the UCLA-3, DMOL and social capital score following ONS guidance. Participant characteristics were summarized using frequencies and percentages. Differences between groups were assessed using Pearson’s chi-square test. Primary analyses were conducted on complete-case data, with multiply imputed models used for sensitivity analyses. Sample sizes vary across analyses due to variable availability and complete-case restrictions; these are reported for each model. Multivariable regression analyses were conducted to examine associations between sociodemographic, health and social variables and loneliness and social capital. Ordinal logistic regression was applied to model associations with loneliness (UCLA-3 and DMOL), while binary logistic regression was used to identify predictors of high versus low social capital. Models were adjusted for key covariates including age, gender, ethnicity, education, employment, marital status, social contacts and health status, with estimates reported as adjusted odds ratios (aORs) with 95% confidence intervals (CI). Unadjusted odds ratios represent crude associations between each predictor and the outcome, while adjusted odds ratios are estimated from multivariable models controlling for all included covariates. Given the non-probability sampling design, estimates represent within-sample associations and are not intended for population-level inference. All statistical analyses were performed using R software version 4.2.2. Ethical Considerations The INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study was approved by the NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483). The Strengthening the Reporting of Observational Studies in Epidemiology [ 14 ] checklist and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)[ 15 ] were used to improve the quality of the reporting. Results Results are presented descriptively, with interpretation reserved for the Discussion. Given the large sample size (N = 135,725), statistical significance should be interpreted alongside effect sizes rather than p-values alone. Descriptive distributions of social capital were based on 120,583 respondents with available social capital data, whereas the adjusted regression model included 117,781 complete observations across all covariates. All regression results presented are based on complete-case analysis, with imputed models yielding consistent estimates ( Supplementary File 1 ). Given the non-probability sampling design, findings are interpreted as within-sample associations rather than population-level estimates. Descriptive findings Prevalence and distribution of loneliness and social capital Among 135,725 respondents, loneliness and social capital were unequally distributed across demographic, social and health-related groups. Supplementary Table 1 presents the prevalence of low, moderate and severe loneliness as measured by the UCLA Loneliness Scale across key demographic and social subgroups (N = 135,725). Clear patterns emerged across age, gender, employment, relationship status and social contact indicators. Using the UCLA Loneliness Scale, 16.5% of participants were classified as experiencing severe loneliness, with the highest burden among younger adults. Only 1.5% of 16-25-year-olds reported no loneliness, compared to 48.7% of respondents aged ≥ 65 years. The proportion of individuals reporting severe loneliness decreased steadily with age, consistent with an inverse age-loneliness gradient. Patterns of loneliness measured using DMOL closely mirrored those observed with the UCLA scale but offered additional granularity. Supplementary Table 2 summarises the prevalence of low (never/hardly ever), moderate (occasionally/some of the time) and severe (often/always) loneliness across key demographic and social variables. Among respondents aged ≥ 65, 45.1% reported “never or hardly ever” feeling lonely, while 11.2% of 16-25-year-olds reported frequent loneliness. Females consistently reported higher levels of moderate and severe loneliness than males across both scales. Notably, individuals identifying as ‘Other’ gender had markedly elevated rates of loneliness relative to their group size, indicating higher reported loneliness within this group. Loneliness was also socially patterned by relationship status, employment and health. Single, divorced or widowed individuals reported the highest loneliness scores, while married or cohabiting participants reported the lowest. Unemployed individuals, those with disabilities and those with long-term conditions were significantly more likely to report moderate or severe loneliness across both UCLA and DMOL measures. The prevalence of low and high social capital across demographic and social characteristics is presented in Supplementary Table 3. Social capital scores were dichotomised into low (≤ 4) and high (> 4) across demographic and social characteristics in a sample of 120,583 respondents. The reduced sample size reflects restriction to observations with complete data for variables included in the social capital model. Social capital was measured using a composite scale of neighbourhood trust, cohesion and support. The reduced sample size reflects restriction to observations with complete data for variables included in the social capital model. Social capital was measured using a composite scale of neighbourhood trust, cohesion and support. Higher social capital was more frequently observed among older adults, married individuals and those with a greater number of friends or relatives. Conversely, lower social capital was more frequently observed among younger respondents, individuals living alone, the unemployed and those in poor health.. These differences were statistically significant across all examined variables (χ² p < 0.001), consistent with the descriptive patterns observed. Inferential statistics findings Inferential analyses were conducted to explore the independent associations between loneliness and a range of sociodemographic, health and social variables. This section presents findings from bivariate analyses using chi-square tests, followed by multivariable modelling using ordinal and binary logistic regression. These analyses aim to identify key factors associated with loneliness as measured by both the UCLA Loneliness Scale and DMOL and to examine factors associated with higher or lower levels of social capital. Differences observed across groups were formally assessed using chi-square tests, as presented below. Chi-square analysis results Consistent with the descriptive patterns observed above, chi-square analyses demonstrated statistically significant associations between loneliness and all examined variables for both the UCLA Loneliness Scale and DMOL (p < 0.001). Variables including age, gender, ethnicity, employment status, marital status, disability and long-term conditions were all significantly associated with levels of loneliness. Similarly, for the Social Capital Score, chi-square tests indicated significant variation in community trust and cohesion across demographic groups (p < 0.001), with lower social capital more prevalent among younger adults, ethnic minority groups and those with poorer health status. Full details are provided in Supplementary File 2 . These findings support the use of multivariable regression to further explore the independent effects of these factors. These findings are presented in the section below. Logistic regression findings Multivariable logistic regression was used to assess the independent effects of sociodemographic, health and social factors on loneliness and social capital. Ordinal models were applied to UCLA and DMOL scores and binary models to social capital. All models adjusted for age, gender, ethnicity, education, employment, marital status, social contacts and health status. This segment presents the findings from multivariable models for UCLA, DMOL and social capital. Ordinal regression analysis: UCLA Loneliness Scale Multivariable ordinal logistic regression modelling identified several independent predictors of greater loneliness as measured by the UCLA scale. The analysis of unadjusted and adjusted odds ratios (OR) for various demographic and social factors associated with loneliness (UCLA) is shown in Table 1 ; Fig. 1 . The adjusted odds ratios (aORs) reported here reflect associations after simultaneous adjustment for all included covariates and should be interpreted as conditional estimates rather than causal effects. Age showed the strongest inverse association with loneliness across models. Compared with individuals aged 16–25 (reference group), older participants reported substantially lower odds of higher loneliness scores. Adults aged 26–35 had 35% lower odds (aOR 0.65, 95% CI: 0.60–0.70), while those aged ≥ 65 had an 86% reduction in odds (aOR 0.14, 95% CI: 0.13–0.16, p < 0.001). Gender was significantly associated with loneliness. Males had reduced odds of loneliness compared to females (aOR 0.81, 95% CI: 0.79–0.83, p < 0.001). While the 'Other' gender category showed elevated unadjusted loneliness, the adjusted association was marginal and not statistically significant (aOR 1.17, 95% CI: 0.98–1.39, p = 0.08). Educational attainment showed a heterogeneous association with loneliness across measures. University graduates had higher odds of loneliness compared to those with secondary education (aOR 1.22, 95% CI: 1.19–1.26, p < 0.001). Unemployed participants had nearly twice the odds of loneliness (aOR 1.82, 95% CI: 1.71–1.94, p < 0.001). Unpaid carers also reported elevated loneliness (aOR 1.64, 95% CI: 1.51–1.78), while retirees had only marginally lower odds (aOR 0.95, 95% CI: 0.91–0.99, p = 0.028), suggesting some a modest protective association with later-life stability. Marital status was among the strongest social predictors. Being married or in a civil partnership was associated with markedly lower loneliness (aOR 0.44, 95% CI: 0.42–0.46, p < 0.001). In contrast, single individuals (aOR 2.33) and widowed respondents (aOR 1.34) had significantly higher odds of loneliness, consistent with lower odds of loneliness among those who were married or in a relationship. Social contact showed a graded association with loneliness. Respondents with 9 or more friends had an AOR of 0.09 (95% CI: 0.09–0.10), while those with 9 or more relatives had an AOR of 0.28 (95% CI: 0.26–0.30), were associated with substantially lower odds of loneliness. Participants reporting no social contacts had the highest loneliness burden. Health-related factors were also strongly associated with loneliness. Individuals with a disability had 77% higher odds (aOR 1.77), while those with long-term conditions had 64% higher odds (aOR 1.64), indicating higher odds of loneliness among individuals with chronic health conditions. Table 1 Association between demographic and social factors and loneliness as measured by UCLA Loneliness Scale; N = 122,257 observations in adjusted model. Age Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * 16–25 Ref. Ref. 26–35 0.70 (0.67, 0.74) < 0.001 0.65 (0.60, 0.70) < 0.001 36–45 0.59 (0.56, 0.62) < 0.001 0.48 (0.45, 0.52) < 0.001 46–55 0.43 (0.41, 0.45) < 0.001 0.32 (0.30, 0.35) < 0.001 56–65 0.26 (0.25, 0.28) < 0.001 0.22 (0.20, 0.24) < 0.001 > 65 0.15 (0.15, 0.16) < 0.001 0.14 (0.13, 0.16) < 0.001 Gender Female Ref. Ref. Male 0.79 (0.77, 0.80) < 0.001 0.81 (0.79, 0.83) < 0.001 Other 3.37 (2.93, 3.87) < 0.001 1.17 (0.98, 1.39) 0.080 Would rather not say 1.98 (1.68, 2.34) < 0.001 1.10 (0.85, 1.42) 0.490 Education Secondary school Ref. Ref. A levels/College 1.02 (1.00, 1.05) 0.092 1.11 (1.07, 1.15) < 0.001 University Degree or higher 0.80 (0.77, 0.82) < 0.001 1.22 (1.19, 1.26) < 0.001 Employment Employed full-time Ref. Ref. Employed part-time 0.79 (0.77, 0.83) < 0.001 1.08 (1.03, 1.12) < 0.001 Other 3.35 (3.13, 3.57) < 0.001 1.82 (1.68, 1.97) < 0.001 Retired 0.42 (0.41, 0.43) < 0.001 0.95 (0.91, 0.99) 0.028 Self-employed 0.68 (0.65, 0.72) < 0.001 1.10 (1.04, 1.16) < 0.001 Student (full or part-time) 2.28 (2.14, 2.43) < 0.001 1.25 (1.15, 1.36) < 0.001 Unemployed 4.71 (4.47, 4.96) < 0.001 1.82 (1.71, 1.94) < 0.001 Unpaid carer 2.64 (2.39, 2.92) < 0.001 2.24 (1.99, 2.51) < 0.001 Volunteer (full or part-time) 0.60 (0.54, 0.66) < 0.001 1.09 (0.98, 1.22) 0.125 Ethnicity White Ref. Ref. Asian/Asian British 1.79 (1.70, 1.88) < 0.001 1.23 (1.15, 1.31) < 0.001 British Black/African/Caribbean 1.74 (1.61, 1.88) < 0.001 1.08 (0.99, 1.19) 0.097 Mixed/Multiple ethnic groups 1.85 (1.70, 2.01) < 0.001 1.06 (0.96, 1.16) 0.277 Other ethnic group 1.60 (1.49, 1.72) < 0.001 1.06 (0.97, 1.15) 0.214 White and Black Caribbean 2.33 (1.96, 2.77) < 0.001 1.14 (0.93, 1.40) 0.213 Marital Status Single Ref. Ref. Divorced 0.62 (0.59, 0.64) < 0.001 1.05 (1.00, 1.10) 0.061 In a relationship 0.41 (0.39, 0.43) < 0.001 0.50 (0.48, 0.53) < 0.001 Married / Civil partnership 0.18 (0.17, 0.18) < 0.001 0.44 (0.42, 0.46) < 0.001 Other 0.76 (0.70, 0.81) < 0.001 0.97 (0.89, 1.06) 0.497 Widowed 0.45 (0.43, 0.47) < 0.001 1.34 (1.27, 1.42) < 0.001 Number of relatives [ 16 ] relatives Ref. Ref. 1 relative 0.92 (0.87, 0.98) 0.006 1.02 (0.95, 1.09) 0.567 2 relatives 0.72 (0.68, 0.75) < 0.001 0.85 (0.80, 0.90) < 0.001 3 or 4 relatives 0.37 (0.35, 0.39) < 0.001 0.57 (0.54, 0.60) < 0.001 5–8 relatives 0.16 (0.16, 0.17) < 0.001 0.39 (0.37, 0.42) < 0.001 9 or more relatives 0.09 (0.08, 0.09) < 0.001 0.28 (0.26, 0.30) < 0.001 Number of friends 0 friends Ref. Ref. 1 friend 0.80 (0.76, 0.84) < 0.001 0.80 (0.76, 0.85) < 0.001 2 friends 0.47 (0.45, 0.49) < 0.001 0.52 (0.49, 0.55) < 0.001 3 or 4 friends 0.22 (0.21, 0.23) < 0.001 0.29 (0.28, 0.31) < 0.001 5–8 friends 0.10 (0.10, 0.11) < 0.001 0.16 (0.15, 0.17) < 0.001 9 or more friends 0.05 (0.05, 0.06) < 0.001 0.09 (0.09, 0.10) < 0.001 Having pet No Ref. Ref. Yes 1.24 (1.22, 1.27) < 0.001 1.04 (1.02, 1.07) < 0.001 Number of household members 0 members Ref. Ref. 1 member 0.37 (0.36, 0.38) < 0.001 0.73 (0.70, 0.76) < 0.001 2–3 members 0.62 (0.60, 0.64) < 0.001 0.68 (0.65, 0.71) < 0.001 4–5 members 0.72 (0.68, 0.75) < 0.001 0.66 (0.62, 0.70) < 0.001 > 5 members 1.08 (0.98, 1.19) 0.130 0.69 (0.61, 0.78) < 0.001 Having children No Ref. Ref. Yes 0.48 (0.47, 0.49) < 0.001 1.06 (1.03, 1.10) < 0.001 Having disability No Ref. Ref. Yes 3.51 (3.42, 3.61) < 0.001 1.77 (1.71, 1.83) < 0.001 Would rather not say 3.49 (3.28, 3.73) < 0.001 1.59 (1.47, 1.71) < 0.001 Having long-term condition No Ref. Ref. Yes 2.76 (2.70, 2.82) < 0.001 1.64 (1.60, 1.69) < 0.001 Would rather not say 3.05 (2.85, 3.27) < 0.001 1.68 (1.55, 1.82) < 0.001 ¶ : Unadjusted (crude) odds ratios from models including each predictor individually † : Ordinal logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition. * : Significance level, with values < 0.05 considered statistically significant. Forest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in Table 1 . Values < 1 indicate lower odds of higher loneliness. Ordinal regression analysis: Direct Measure of Loneliness (DMOL) The analysis of unadjusted and adjusted odds ratios for various demographic and social factors associated with DMOL is shown in Table 2 ; Fig. 2 . Findings from the DMOL model were directionally consistent with the UCLA scale, though some differences in magnitude and statistical significance emerged. Age remained a strong predictor: respondents aged ≥ 65 had an AOR of 0.19 (95% CI: 0.18–0.21), confirming a lower risk of frequent loneliness in later life. Gender differences persisted, with males again showing lower odds than females (aOR 0.74, 95% CI: 0.72–0.76) and no significant association for ‘Other’ gender (aOR 0.94, p = 0.455). Educational attainment showed a small protective effect in the DMOL model (AOR for university degree: 0.96, 95% CI: 0.93–0.99, p = 0.008), diverging from the findings in the UCLA model. Unemployment was again a strong predictor (aOR 1.68, 95% CI: 1.59–1.78), with unpaid carers similarly affected (aOR 1.68). The effect of being single remained strong (aOR 2.46), while widowed respondents also had elevated risk (aOR 1.49, 95% CI: 1.41–1.57). Social contact maintained a robust protective role: respondents with 9 + friends had 84% lower odds (aOR 0.16, 95% CI: 0.15–0.17). Health variables mirrored previous findings. Disability (aOR 1.44) and long-term conditions (aOR 1.53) remained strongly associated with greater loneliness. Having children was associated with a slight increase in loneliness (aOR 1.19), while pet ownership was not a significant factor after adjustment. Table 2 Association between demographic and social factors and loneliness as measured by DMOL; N = 122,257 observations in adjusted model. Age Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * 16–25 Ref. Ref. 26–35 0.76 (0.72, 0.80) < 0.001 0.73 (0.68, 0.78) < 0.001 36–45 0.64 (0.61, 0.67) < 0.001 0.54 (0.52, 0.58) < 0.001 46–55 0.49 (0.46, 0.51) < 0.001 0.38 (0.36, 0.41) < 0.001 56–65 0.31 (0.29, 0.32) < 0.001 0.27 (0.25, 0.29) < 0.001 > 65 0.20 (0.19, 0.21) < 0.001 0.19 (0.18, 0.21) < 0.001 Gender Female Ref. Ref. Male 0.73 (0.72, 0.75) < 0.001 0.74 (0.72, 0.76) < 0.001 Other 2.42 (2.12, 2.76) < 0.001 0.94 (0.80, 1.10) 0.455 Would rather not say 1.73 (1.46, 2.04) < 0.001 0.85 (0.66, 1.09) 0.198 Education Secondary school Ref. Ref. A levels/College 0.93 (0.91, 0.96) < 0.001 0.96 (0.93, 0.99) 0.010 University Degree or higher 0.70 (0.68, 0.72) < 0.001 0.96 (0.93, 0.99) 0.008 Employment Employed full-time Ref. Ref. Employed part-time 0.87 (0.84, 0.90) < 0.001 1.08 (1.04, 1.13) < 0.001 Other 2.96 (2.78, 3.15) < 0.001 1.61 (1.50, 1.73) < 0.001 Retired 0.49 (0.47, 0.50) < 0.001 0.96 (0.92, 1.00) 0.057 Self-employed 0.71 (0.68, 0.75) < 0.001 1.08 (1.02, 1.13) 0.007 Student (full or part-time) 2.02 (1.89, 2.15) < 0.001 1.08 (0.99, 1.17) 0.071 Unemployed 4.15 (3.96, 4.35) < 0.001 1.68 (1.59, 1.78) < 0.001 Unpaid carer 2.18 (1.98, 2.40) < 0.001 1.68 (1.51, 1.87) < 0.001 Volunteer (full or part-time) 0.63 (0.57, 0.70) < 0.001 1.03 (0.92, 1.15) 0.599 Ethnicity White Ref. Ref. Asian/Asian British 1.88 (1.78, 1.98) < 0.001 1.41 (1.32, 1.49) < 0.001 British Black/African/Caribbean 1.83 (1.70, 1.98) < 0.001 1.23 (1.13, 1.35) < 0.001 Mixed/Multiple ethnic groups 1.71 (1.58, 1.86) < 0.001 1.06 (0.97, 1.17) 0.223 Other ethnic group 1.73 (1.60, 1.86) < 0.001 1.24 (1.14, 1.35) < 0.001 White and Black Caribbean 2.24 (1.88, 2.67) < 0.001 1.13 (0.93, 1.37) 0.237 Marital Status Single Ref. Ref. Divorced 0.65 (0.63, 0.68) < 0.001 0.99 (0.95, 1.04) 0.789 In a relationship 0.39 (0.37, 0.41) < 0.001 0.47 (0.45, 0.50) < 0.001 Married / Civil partnership 0.20 (0.19, 0.21) < 0.001 0.44 (0.42, 0.46) < 0.001 Other 0.82 (0.76, 0.88) < 0.001 1.01 (0.93, 1.10) 0.807 Widowed 0.58 (0.55, 0.61) < 0.001 1.49 (1.41, 1.57) < 0.001 Number of relatives 0 relatives Ref. Ref. 1 relative 0.88 (0.83, 0.93) < 0.001 0.94 (0.89, 1.01) 0.072 2 relatives 0.71 (0.67, 0.74) < 0.001 0.82 (0.77, 0.87) < 0.001 3 or 4 relatives 0.40 (0.38, 0.42) < 0.001 0.60 (0.57, 0.63) < 0.001 5–8 relatives 0.19 (0.18, 0.20) < 0.001 0.41 (0.39, 0.44) < 0.001 9 or more relatives 0.11 (0.10, 0.12) < 0.001 0.30 (0.28, 0.32) < 0.001 Number of friends 0 friends Ref. Ref. 1 friend 0.82 (0.78, 0.86) < 0.001 0.84 (0.79, 0.88) < 0.001 2 friends 0.53 (0.50, 0.55) < 0.001 0.61 (0.59, 0.65) < 0.001 3 or 4 friends 0.29 (0.27, 0.30) < 0.001 0.41 (0.39, 0.43) < 0.001 5–8 friends 0.15 (0.15, 0.16) < 0.001 0.26 (0.25, 0.28) < 0.001 9 or more friends 0.08 (0.08, 0.08) < 0.001 0.16 (0.15, 0.17) < 0.001 Having pet No Ref. Ref. Yes 1.16 (1.13, 1.18) < 0.001 1.00 (0.97, 1.02) 0.720 Number of household members 0 members Ref. Ref. 1 member 0.37 (0.36, 0.38) < 0.001 0.77 (0.74, 0.80) < 0.001 2–3 members 0.60 (0.59, 0.62) < 0.001 0.72 (0.69, 0.76) < 0.001 4–5 members 0.72 (0.69, 0.76) < 0.001 0.75 (0.70, 0.80) < 0.001 > 5 members 1.11 (1.01, 1.23) 0.037 0.84 (0.75, 0.95) 0.004 Having children No Ref. Ref. Yes 0.56 (0.55, 0.57) < 0.001 1.19 (1.15, 1.23) < 0.001 Having disability No Ref. Ref. Yes 2.89 (2.81, 2.97) < 0.001 1.44 (1.39, 1.49) < 0.001 Would rather not say 2.98 (2.78, 3.17) < 0.001 1.33 (1.24, 1.43) < 0.001 Having long-term condition No Ref. Ref. Yes 2.43 (2.38, 2.49) < 0.001 1.53 (1.49, 1.57) < 0.001 Would rather not say 2.73 (2.55, 2.92) < 0.001 1.53 (1.42, 1.66) ¶ : Unadjusted (crude) odds ratios from models including each predictor individually † : Ordinal logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition. * : Significance level, with values < 0.05 considered statistically significant. Forest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in Table 2 . Values < 1 indicate lower odds of higher loneliness. Binary regression analysis: Social capital Table 3 presents adjusted odds ratios for various demographic and social factors associated with Social Capital Scale. The resulting forest plot is presented in Fig. 3 . Binary logistic regression identified several factors associated with higher versus lower social capital. Age showed a strong positive association. Compared with the 16–25 reference group, those aged 40–64 had more than twice the odds of high social capital (aOR 2.38), while those aged ≥ 65 had over threefold higher odds (aOR 3.21, 95% CI: 2.93–3.51). Marital status was also associated with social capital. Individuals who were married or in a civil partnership had higher odds of reporting high social capital (aOR 1.60), while widowed individuals also showed increased odds (aOR 1.39). In contrast, being single was associated with lower social capital. Employment status demonstrated a gradient. Retired individuals had higher odds of high social capital (aOR 1.49), whereas unemployment (aOR 0.91) and unpaid caregiving (aOR 0.86) were associated with reduced odds. Social contact remained strongly associated with social capital. Participants reporting more frequent contact with friends and relatives had progressively higher odds of high social capital, with those reporting nine or more friends showing markedly elevated odds (aOR 5.22). Health-related factors were inversely associated with social capital. Individuals with a disability (aOR 0.77) or a long-term condition (aOR 0.82) had lower odds of reporting high neighbourhood trust, cohesion and support. Table 3 Association between demographic and social factors and Social Capital Scale; N = 117,781 observations in adjusted model. Age Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * 16–25 Ref. Ref. 26–35 1.19 (1.11, 1.27) < 0.001 1.15 (1.06, 1.24) < 0.001 36–45 1.79 (1.68, 1.9) < 0.001 1.8 (1.66, 1.95) < 0.001 46–55 2.38 (2.25, 2.53) < 0.001 2.38 (2.2, 2.58) < 0.001 56–65 3.33 (3.14, 3.52) < 0.001 2.72 (2.51, 2.95) < 0.001 > 65 5.37 (5.08, 5.68) < 0.001 3.21 (2.93, 3.51) < 0.001 Gender Female Ref. Ref. Male 1.04 (1.02, 1.07) 0.001 1.04 (1.01, 1.07) 0.012 Other 0.31 (0.26, 0.37) < 0.001 0.75 (0.63, 0.91) 0.003 Would rather not say 0.56 (0.45, 0.7) < 0.001 1.14 (0.89, 1.46) 0.29 Education Secondary school Ref. Ref. A levels/College 1.09 (1.05, 1.12) < 0.001 1.14 (1.1, 1.18) < 0.001 University Degree or higher 1.3 (1.26, 1.34) < 0.001 1.15 (1.11, 1.19) < 0.001 Employment Employed full-time Ref. Ref. Employed part-time 1.44 (1.38, 1.49) < 0.001 1.19 (1.14, 1.24) < 0.001 Volunteer (full or part-time) 1.93 (1.73, 2.15) < 0.001 1.28 (1.13, 1.45) < 0.001 Retired 2.54 (2.47, 2.61) < 0.001 1.49 (1.42, 1.57) < 0.001 Self-employed 1.72 (1.63, 1.81) < 0.001 1.26 (1.19, 1.33) < 0.001 Student (full or part-time) 0.53 (0.49, 0.57) < 0.001 0.9 (0.82, 0.99) 0.028 Unemployed 0.42 (0.39, 0.44) < 0.001 0.91 (0.86, 0.97) 0.004 Unpaid carer 0.67 (0.6, 0.75) < 0.001 0.86 (0.76, 0.97) 0.012 Other 0.65 (0.61, 0.7) < 0.001 1.08 (1, 1.16) 0.066 Ethnicity White Ref. Ref. British Black/African/Caribbean 0.32 (0.29, 0.35) < 0.001 0.47 (0.43, 0.53) < 0.001 Mixed/Multiple ethnic groups 0.51 (0.46, 0.56) < 0.001 0.8 (0.72, 0.89) < 0.001 White and Black Caribbean 0.44 (0.36, 0.53) < 0.001 0.75 (0.61, 0.94) 0.011 Asian/Asian British 0.54 (0.51, 0.57) < 0.001 0.82 (0.76, 0.88) < 0.001 Other ethnic group 0.51 (0.47, 0.55) < 0.001 0.67 (0.61, 0.74) < 0.001 Marital status single Ref. Ref. Divorced 1.65 (1.58, 1.73) < 0.001 1.03 (0.97, 1.08) 0.336 In a relationship 1.54 (1.47, 1.6) < 0.001 1.22 (1.16, 1.29) < 0.001 Married / Civil partnership 3.31 (3.21, 3.41) < 0.001 1.6 (1.53, 1.67) < 0.001 Widowed 3.2 (3.05, 3.37) < 0.001 1.39 (1.31, 1.48) < 0.001 Other 1.26 (1.17, 1.37) < 0.001 0.98 (0.89, 1.07) 0.588 Number of relatives 0 relatives Ref. Ref. 1 relative 1.25 (1.17, 1.33) < 0.001 1.18 (1.1, 1.27) < 0.001 2 relatives 1.55 (1.46, 1.65) < 0.001 1.39 (1.31, 1.49) < 0.001 3 or 4 relatives 2.32 (2.2, 2.45) < 0.001 1.69 (1.59, 1.8) < 0.001 5–8 relatives 3.97 (3.75, 4.2) < 0.001 2.1 (1.96, 2.24) < 0.001 9 or more relatives 5.61 (5.27, 5.97) < 0.001 2.34 (2.17, 2.51) < 0.001 Number of friends 0 friends Ref. Ref. 1 friend 1.35 (1.28, 1.43) < 0.001 1.34 (1.26, 1.42) < 0.001 2 friends 1.99 (1.89, 2.1) < 0.001 1.79 (1.69, 1.89) < 0.001 3 or 4 friends 3.36 (3.2, 3.53) < 0.001 2.65 (2.52, 2.79) < 0.001 5–8 friends 5.69 (5.41, 5.98) < 0.001 3.88 (3.67, 4.1) < 0.001 9 or more friends 8.51 (8.08, 8.96) < 0.001 5.22 (4.92, 5.53) < 0.001 Having pet No Ref. Ref. Yes 1.03 (1, 1.05) 0.022 1.24 (1.21, 1.27) < 0.001 Number of household members 0 members Ref. Ref. 1 member 1.59 (1.54, 1.63) < 0.001 1.05 (1, 1.09) 0.038 2–3 members 1.11 (1.07, 1.14) < 0.001 1.13 (1.08, 1.19) < 0.001 4–5 members 0.92 (0.88, 0.97) < 0.001 1.09 (1.02, 1.17) 0.013 > 5 members 0.6 (0.54, 0.67) < 0.001 0.98 (0.86, 1.11) 0.729 Children No Ref. Ref. Yes 1.85 (1.8, 1.89) < 0.001 0.93 (0.9, 0.96) < 0.001 Disability No Ref. Ref. Yes 0.5 (0.48, 0.51) < 0.001 0.77 (0.74, 0.8) < 0.001 Would rather not say 0.39 (0.36, 0.42) < 0.001 0.68 (0.62, 0.74) < 0.001 Longterm conditions No Ref. Ref. Yes 0.56 (0.55, 0.58) < 0.001 0.82 (0.8, 0.85) < 0.001 Would rather not say 0.45 (0.41, 0.48) < 0.001 0.75 (0.69, 0.82) < 0.001 ¶ : Unadjusted (crude) odds ratios from models including each predictor individually. † : Binary logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition. * : Significance level, with values < 0.05 considered statistically significant. Forest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from binary logistic regression. Social capital was dichotomised at the median. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown. Values > 1 indicate higher odds of high social capital. Converging factors associated with loneliness and social capital Across all three models, several consistent patterns were observed. Younger age was the most robust and universal risk factor, strongly associated with both higher levels of loneliness and lower levels of social capital. Conversely, frequent social contact with friends and relatives was protective in every model, underscoring the importance of interpersonal relationships in fostering connection and reducing isolation. Other consistent predictors of vulnerability included being single or widowed, unemployment and disability, each linked to greater loneliness and reduced social capital, suggesting a compounding effect of social and structural disadvantage. Importantly, higher social capital itself was consistently associated with lower odds of loneliness. This pattern is consistent with a potential buffering hypothesis of social capital, reinforcing the central role of community cohesion in shaping psychosocial wellbeing. Some divergences were also observed: notably, educational attainment was associated with reduced loneliness in the DMOL and social capital models but paradoxically linked to greater loneliness in the UCLA model. This variation may reflect underlying differences in how the two loneliness measures capture emotional versus social aspects of disconnection or how education interacts with unmeasured contextual factors such as occupational stress or urbanicity. Discussion Summary of principal findings Drawing on data from over 135,000 adults, this study presents one of the largest inferential analyses of loneliness and social capital within a non-probability sample in the UK. By shifting from the descriptive characterisations presented in paper 1 (El-Osta et al., 2026a) to estimation of adjusted associations, this second paper in the INTERACT series lays the groundwork for targeted, equity-informed and context-sensitive interventions. However, given the non-probability sampling design, regression estimates represent within-sample associations rather than population-level effects and should be interpreted as conditional relationships within the responding cohort and as hypothesis-generating rather than generalisable estimates. Using two validated tools (UCLA-3 and DMOL), we identified clear and consistent sociodemographic and social gradients in loneliness. Younger adults, particularly those aged 16–25, reported the highest burden of loneliness, while older adults generally reported lower levels. However, within the older population (≥ 65), important disparities were observed, especially among those who were widowed, living alone or managing chronic health conditions. Social contact, measured by the number of close friends or relatives, a known indicator of social connection, emerged as one of the strongest and most consistent factors associated with lower loneliness across all models. Similarly, social capital, which is conceptualised as neighbourhood trust, cohesion and reciprocity, was also inversely associated with loneliness and was itself socially patterned, being most prevalent among older, married and retired individuals. Our inclusion of social capital as both an outcome and a contextual correlate draws from the social determinants of health framework advocated by the WHO [ 17 , 18 ]. Our findings show that neighbourhood trust, civic participation and perceptions of safety are not just background variables, but factors that shape individuals’ capacity to build and sustain meaningful relationships across the life course. Among older adults, the protective association of retirement, stable relationships and high social capital points to a potential buffering role of community integration and relational continuity. However, this seemingly protective effect was not uniformly distributed since older individuals who were widowed, living alone or experiencing multimorbidity faced significantly elevated loneliness risk, echoing findings from the English Longitudinal Study of Ageing[ 19 ]. This highlights the need to disaggregate the \"older adult\" category and address intra-cohort heterogeneity in loneliness trajectories. Collectively, these findings emphasise age-related differences in loneliness within a cross-sectional sample and support age-sensitive approaches, while longitudinal data are needed to examine trajectories over time. Nevertheless, our findings align with socioecological models of health which emphasise that loneliness is shaped by multilevel influences, including individual traits, interpersonal ties, community environments and structural forces [ 20 , 21 ]. While individual factors such as age, disability or employment status matter, they operate within broader systems of opportunity, access and belonging. Consistent with Paper 1 which demonstrated substantial heterogeneity in loneliness across subgroups, the present analysis quantifies these gradients and identifies converging associations across multiple measures. However, we emphasise that these findings reflect cross-sectional associations and do not establish causal or mediating relationships. Comparison with existing literature Our results corroborate prior evidence showing a reversed age-loneliness gradient in the general population, where younger adults report greater loneliness than older adults [ 22 , 23 ]. Our study’s large sample size brings new insight into the dual burden of loneliness in later life, where some older individuals demonstrate resilience, while others, particularly those who are widowed, isolated or unwell, remain at elevated risk. The strong protective role of social contact aligns with longitudinal findings from ELSA and international reviews [ 24 ]. Our data add precision by quantifying this gradient and showing that individuals reporting a greater number of social ties (e.g. more than four friends or relatives) had substantially lower odds of loneliness. In relation to older adults, our findings echo those of Victor et al. in 2005 [ 25 ], who distinguished between emotional loneliness (absence of close attachment) and social loneliness (lack of broader networks). Although this distinction not captured in our measures, it was evident in subgroup patterns and could explain why older adults who were widowed but socially embedded may experience less social loneliness but elevated emotional loneliness. Intriguingly, the observed association between higher educational attainment and increased loneliness on the UCLA measure contrasts with prior studies linking higher education to improved social and mental health outcomes[ 26 ]. This pattern may also be interpreted through expectation-discrepancy frameworks whereby individuals with greater educational attainment may hold higher expectations of social connectedness, increasing the likelihood of perceived deficits despite comparable objective networks. The observed divergence may also reflect a combination of measurement differences between indirect (UCLA) and direct (DMOL) instruments, as well as underlying contextual and structural factors. For example, higher educational attainment may be associated with different expectations of social connectedness, patterns of social mobility or the composition and stability of social networks. It may also reflect contextual influences such as work-related demands, urban residence, digitally mediated forms of interaction or differences in neighbourhood integration among more highly educated individuals [ 27 ]. However, these mechanisms cannot be directly assessed within the present analysis and should be interpreted cautiously. The slightly elevated loneliness observed among individuals with children-particularly in the DMOL model reflects the complex emotional demands of caregiving. For both younger and older parents, caregiving responsibilities can reduce time for reciprocal adult relationships, especially in the context of single parenting, chronic illness or financial stress. The lack of a significant protective association between pet ownership and loneliness was also interesting but unsurprising since companion animals do not fully substitute for human social contact or that their protective effects are context-specific [ 28 ]. We hypothesise that these dynamics may be more salient among older adults who live alone, highlighting the need for future research to differentiate between emotional and structural sources of connection. Strengths and limitations This study’s primary strength lies in its scale and scope, representing the largest UK-based analysis of loneliness and social capital to date. It includes validated measures of loneliness and adopts a novel approach by modelling social capital not only as a predictor but also as an outcome thus enabling a more integrated assessment of relational and structural dimensions of social connection. The analysis also provides age-disaggregated insights, highlighting both resilience and vulnerability among older adults. However, several limitations should be considered, some of which are detailed in Paper 1 ( El-Osta et al., 2026a ). The principal limitations of this study are the non-probability sampling design which limits generalisability and does not support population-level inference, and the cross-sectional design, which precludes causal inference. Self-reported data may introduce recall and social desirability bias, and online sampling may underrepresent individuals with cognitive or digital access barriers, particularly among older adults. Additionally, variables such as income and more granular measures of socioeconomic position were not included and may interact with loneliness, warranting further investigation. A further limitation is the reliance on established, yet inherently constrained measures of loneliness. While the UCLA-3 and ONS single-item scales were selected to ensure comparability with existing national datasets, they do not fully capture the multidimensional nature of loneliness. The UCLA scale, originally developed within a specific demographic context, provides limited insight into duration, contextual drivers and experiential aspects of loneliness. Similarly, the ONS measure captures frequency but does not specify a timeframe or underlying causes. Future research should prioritise the development and validation of contemporary, multidimensional measures that better reflect the complexity and lived experience of loneliness across diverse populations. These limitations are addressed in part through ongoing psychometric work reported elsewhere [ 11 ], and through parallel efforts to develop and validate novel measures of social connection (the INTERACTION Scale) as will be reported in a forthcoming paper elsewhere. We acknowledge that the absence of area-level contextual variables, such as rural-urban classification and neighbourhood deprivation within the present analytical models is also a limitation. These factors are recognised as important structural determinants of loneliness and may contribute to geographic and socioeconomic inequalities in social connectedness. While such variables were available within the INTERACT dataset and incorporated into geospatial analyses, they were not included in the current modelling framework. This reflects the intentional focus of this paper on individual-level socio-demographic, social and health-related correlates to provide a clear and interpretable analytical baseline. More detailed examination of area-level determinants, including rurality and deprivation, is being undertaken in complementary analyses within the INTERACT programme. Another important limitation is the reliance on main-effects models which estimate independent associations but do not capture the combined influence of intersecting socio-demographic characteristics. Although interaction modelling was considered, the inclusion of multiple higher-order terms across numerous predictors in a large dataset risks over-parameterisation, multicollinearity and reduced interpretability. Accordingly, the present analysis does not account for the compounded or intersectional nature of vulnerability, where co-occurring characteristics (e.g. age, disability and socioeconomic status) may interact in non-additive ways. Future research should apply theoretically specified interaction models or alternative approaches such as latent class or intersectional frameworks to better capture these complex patterns. Furthermore, the use of regression models within a non-probability sample does not support population-level inference and observed associations may reflect underlying selection mechanisms rather than true population relationships. Finaly, although this analysis focuses on individual-level determinants to provide interpretable baseline estimates, area-level factors, including rural-urban classification and neighbourhood deprivation, represent important contextual extensions. These are examined in complementary analyses within the wider INTERACT programme (manuscript in preparation). Implications for policy and future research Given the cross-sectional and non-probability nature of the data, the following implications should be interpreted as hypothesis-informed rather than prescriptive. Given that loneliness is a multigenerational challenge and not confined to any one age group, while younger adults represent an emerging high-risk group, the enduring vulnerability of certain subgroups of older adults particularly those who are widowed, unpartnered, disabled or experiencing multimorbidity must remain a priority in research and policy responses. To address this, and in line with the WHO’s Age-Friendly Communities framework, local authorities could invest in inclusive public infrastructure, intergenerational programmes and community hubs to support social connection among older adults. Approaches such as group-based activities, community choirs, neighbourhood walking groups or digital literacy training have shown promise in reducing isolation and improving wellbeing in later life [ 29 ]. At the same time, efforts to build social capital through safer, more cohesive and civically active neighbourhoods will have spillover benefits across the life course. Housing design, transport planning and digital access all matter. Interventions must be place-based, equity-informed and rooted in community engagement to address both the symptoms and actors associated with loneliness. Future research should also adopt longitudinal and mixed method designs to understand how loneliness evolves with life transitions (e.g. retirement, bereavement) and how community infrastructure and policy reforms shape these trajectories. Expanding the psychometric validation of new social capital tools, particularly for diverse ageing populations, is also a priority. To this end, the INTERACT programme of work reflects a staged measurement strategy, where established instruments support comparability in early phases, followed by iterative psychometric refinement and instrument development. The next phase of this research presented in the third paper of this series [ 11 ], employed the Rasch Rating Scale Model to examine the psychometric properties of the loneliness, social capital, and COVID-19 context questionnaires. This modern measurement approach was used to evaluate item fit, person reliability, response category functioning, and the one-dimensionality of each construct. Rasch analysis also enabled the identification of response biases, disordered thresholds, and differential item functioning across key sociodemographic groups, particularly age and gender, providing a complementary perspective to the findings reported in this article. In addition, person-item targeting was assessed to determine how well the item difficulties matched the distribution of respondents’ trait levels, and separation indices were computed to quantify the instrument’s precision in distinguishing between different levels of the latent traits. As data collection continues, including from under-served populations and so called “hard to reach” communities, the INTERACT programme aims to provides one of the most comprehensive investigations into the epidemiology and measurement of loneliness to date, delivering robust, data-driven insights to inform public health policy and intervention design. Conclusion Loneliness is a complex and layered experience that reflects individual disposition, as well as structural and relational disadvantage. This study provides large-scale evidence on the distribution of factors associated with loneliness within a non-probability sample. Findings highlight consistent gradients across age, social relationships and health status, and underscore the importance of considering both individual and contextual dimensions of social disconnection. These results provide a foundation for future longitudinal and intervention-focused research. Declarations Ethics approval and consent to participate The INTERACT study was registered on the NIHR Portfolio (CPMS#52230). Ethical approval was granted from the NHS Research Ethics Committee (#21IC6950) and the Imperial College London Research Ethics Committee (ICREC #305483). All participants provided informed electronic consent prior to participation. Consent for publication Not applicable. Availability of data and materials Due to ethical and data protection constraints, individual-level data cannot be publicly shared. A de-identified dataset and accompanying codebook will be made available upon reasonable request from the corresponding author, subject to institutional approvals and data protection requirements. Competing interests The authors declare that they have no competing interests. Funding This research received no funding. Austen El-Osta is grateful for support by the National Institute for Health & Care Research (NIHR) Applied Research Collaboration NorthWest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or Department of Health and Social Care. Authors’ contributions AEO conceived the study. AEO, MA-A, AA, SA, AT-L and AM contributed to study design, data analysis and interpretation. AEO drafted the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. AEO is the guarantor. Acknowledgements The authors thank all participants who contributed to the INTERACT study and the partner organisations, including NHS networks and voluntary sector organisations that supported recruitment and dissemination. Austen El-Osta is grateful to Professor Pamela Qualter for her suggestion to include a social capital scale, and to Dr Nina Goldmann for insightful discussions. References Holt-Lunstad, J., Social connection as a critical factor for mental and physical health: evidence, trends, challenges, and future implications . World Psychiatry, 2024. 23(3): p. 312–332. Hong, J.H., et al., Are loneliness and social isolation equal threats to health and well-being? An outcome-wide longitudinal approach . SSM - Population Health, 2023. 23: p. 101459. Sampson, R.J. and C. 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Proceedings of the National Academy of Sciences, 2013. 110(15): p. 5797–5801. Sampson, R.J., S.W. Raudenbush, and F. Earls, Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy . Science, 1997. 277(5328): p. 918–924. Martin, K.S., et al., Social capital is associated with decreased risk of hunger . Soc Sci Med, 2004. 58(12): p. 2645–54. Tristán-López, A., A. Majeed, and A. El-Osta, A RASCH-BASED PSYCHOMETRIC EVALUATION OF A 13-ITEM LONELINESS AND SOCIAL CONNECTION SCALES IN OVER 135000 ADULTS. 2026. NHS. Chapter 6: social determinants of health . 2017; Available from: https://www.gov.uk/government/publications/health-profile-for-england/chapter-6-social-determinants-of-health . Schnittger, R.I., et al., Risk factors and mediating pathways of loneliness and social support in community-dwelling older adults . Aging Ment Health, 2012. 16(3): p. 335–46. STROBE. STROBE Check List . 2024; Available from: https://www.strobe-statement.org/checklists/ . Eysenbach, G., Improving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) . Journal of medical Internet research, 2004. 6(3): p. e34-e34. Faustino, B., et al., Psychometric and rash analysis of the UCLA Loneliness Scale-16 in a Portuguese sample of older adults . Psychological Studies, 2019. 64: p. 140–146. National Academies of Sciences, E. and Medicine, Frameworks for Addressing the Social Determinants of Health , in A Framework for Educating Health Professionals to Address the Social Determinants of Health . 2016, National Academies Press (US). Organization, W.H., A conceptual framework for action on the social determinants of health , in A conceptual framework for action on the social determinants of health . 2010. Victor, C.R. and K. Yang, The prevalence of loneliness among adults: a case study of the United Kingdom . J Psychol, 2012. 146(1–2): p. 85–104. Hardcastle, B., et al., The Ecology of Human Development—Experiments by Nature and Design by Urie Bronfenbrenner. Cambridge, Ma.: Harvard University Press , 1979. 330 pp. $16.50 . 1981, Taylor & Francis. McLeroy, K.R., et al., An ecological perspective on health promotion programs . Health Educ Q, 1988. 15(4): p. 351–77. Pyle, E. and D. Evans, Loneliness-what characteristics and circumstances are associated with feeling lonely . Newport: Office for National Statistics, 2018. Wigfield, A., Campaign to end loneliness. Holt-Lunstad, J., et al., Loneliness and Social Isolation as Risk Factors for Mortality:A Meta-Analytic Review . Perspectives on Psychological Science, 2015. 10(2): p. 227–237. Victor, C.R., et al., The prevalence of, and risk factors for, loneliness in later life: a survey of older people in Great Britain . Ageing and Society, 2005. 25(6): p. 357–375. Pinquart, M. and S. and Sorensen, Influences on Loneliness in Older Adults: A Meta-Analysis . Basic and Applied Social Psychology, 2001. 23(4): p. 245–266. Barreto, M., et al., Loneliness around the world: Age, gender, and cultural differences in loneliness . Personality and Individual Differences, 2021. 169: p. 110066. Carr, S. and C. Fang, A gradual separation from the world: a qualitative exploration of existential loneliness in old age . Ageing and Society, 2023. 43(6): p. 1436–1456. Gardiner, C., G. Geldenhuys, and M. Gott, Interventions to reduce social isolation and loneliness among older people: an integrative review . Health Soc Care Community, 2018. 26(2): p. 147–157. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFILE1.docx SupplementaryFILE2.docx Supplementarytable1.docx Supplementarytable2.docx Supplementarytable3.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Editor assigned by journal 11 May, 2026 Reviewers invited by journal 13 Apr, 2026 Submission checks completed at journal 13 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9370594\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":624978136,\"identity\":\"1c9ce0f8-48a3-433a-9973-00107ba68aec\",\"order_by\":0,\"name\":\"Austen El-Osta\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2RMWoDMRBFRwjWjYLaMQ7eK2gxBEICvopcpTWk2SIsAhOl8QGcY4RcQGYg2+wBbBaC3aR2aQgJkbLgxshOmUKvGWngMfMlgETif8Lcb+k9bUIR0Ova6pQTFASxNJ3C/6zgxHTXc4o03Dkoq0o+bx9fp+X7pZwB2+2BRjEFXaYdNITYTmy7aO4FEvD+HOgqvpVQ9GUdQlAurBZAAAMAuo0ZuZM7x2yF+XrplW8tcj/l85SinACvcFQr5hWj/VDIwpToYgVlKmTpvzQhy5sWBTF7PVd30fjDerbd+BeTw7r+aKcPeuw7tNqXN4WJOfz4zMyZj4zoiUQikTjwA4alVJ4ABnGFAAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Austen\",\"middleName\":\"\",\"lastName\":\"El-Osta\",\"suffix\":\"\"},{\"id\":624978138,\"identity\":\"a275ee07-e7d8-420a-8371-9db8567931bf\",\"order_by\":1,\"name\":\"Mahmoud Al-Ammouri\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mahmoud\",\"middleName\":\"\",\"lastName\":\"Al-Ammouri\",\"suffix\":\"\"},{\"id\":624978141,\"identity\":\"e756fd1b-6381-41ec-9ea9-452d85f51e8a\",\"order_by\":2,\"name\":\"Aos Alaa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Aos\",\"middleName\":\"\",\"lastName\":\"Alaa\",\"suffix\":\"\"},{\"id\":624978143,\"identity\":\"982dfed6-a826-4c7a-8295-2bb96603700e\",\"order_by\":3,\"name\":\"Sami Altalib\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sami\",\"middleName\":\"\",\"lastName\":\"Altalib\",\"suffix\":\"\"},{\"id\":624978144,\"identity\":\"a1a1eff1-f779-4c67-9336-438c77b1be54\",\"order_by\":4,\"name\":\"Agustin Tristán-López\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Agustin\",\"middleName\":\"\",\"lastName\":\"Tristán-López\",\"suffix\":\"\"},{\"id\":624978145,\"identity\":\"83a4d0cf-7385-4be7-8ef1-44a73d345246\",\"order_by\":5,\"name\":\"Azeem Majeed\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Azeem\",\"middleName\":\"\",\"lastName\":\"Majeed\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-09 15:40:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9370594/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9370594/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":107357345,\"identity\":\"04ecd6b0-95ab-4f5f-94d2-11cf6d44dda3\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 17:10:35\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1395189,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdjusted associations between socio-demographic, social and health-related factors and loneliness (UCLA-3); N = 122,257\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in \\u003cstrong\\u003eTable 1\\u003c/strong\\u003e. Values \\u0026lt;1 indicate lower odds of higher loneliness.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/b2d59c4a229b820df466f4d1.png\"},{\"id\":107486165,\"identity\":\"359f9fa4-cfc6-4bb8-9133-b03170db1f3c\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 02:37:33\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1514951,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdjusted associations between socio-demographic, social and health-related factors and loneliness (DMOL); N = 122,257\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in Table 2. Values \\u0026lt;1 indicate lower odds of higher loneliness.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/a8ad5e596c0fbff4fcca322a.png\"},{\"id\":107487408,\"identity\":\"c13bea5a-f1c8-4524-8c29-6eada85de583\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 02:41:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1431362,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAdjusted associations between socio-demographic, social and health-related factors and social capital; N = 117,781\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from binary logistic regression. Social capital was dichotomised at the median. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown\\u003cstrong\\u003e.\\u003c/strong\\u003e Values \\u0026gt;1 indicate higher odds of high social capital.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"F3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/29ff93b0a366652649696e3f.png\"},{\"id\":107489136,\"identity\":\"ebc9edc4-6af2-423f-af4c-d44c61c54d8d\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 02:46:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4640834,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/823c1087-3ff8-472d-833c-f8f0451f4528.pdf\"},{\"id\":107357344,\"identity\":\"23d86d24-546b-4ecc-8754-656c3147900b\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 17:10:35\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":3877144,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFILE1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/6666d9e241bb37cce99fa367.docx\"},{\"id\":107487394,\"identity\":\"0632db7b-84fd-4580-9fb1-cb988596f610\",\"added_by\":\"auto\",\"created_at\":\"2026-04-22 02:41:08\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":93485,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFILE2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/8f76a771b5200e4729bc8487.docx\"},{\"id\":107357346,\"identity\":\"ac4a7063-7987-4afb-b5ac-2432af3139f6\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 17:10:35\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":59140,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarytable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/ce069d588da5339667c9e567.docx\"},{\"id\":107357347,\"identity\":\"f5f9d44b-836f-4e71-8b98-4eb984b0e5a6\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 17:10:35\",\"extension\":\"docx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":59118,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarytable2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/6db126590d1e8c11d4c1e90b.docx\"},{\"id\":107357349,\"identity\":\"21895709-f519-494a-ada4-190ab4f5f929\",\"added_by\":\"auto\",\"created_at\":\"2026-04-20 17:10:35\",\"extension\":\"docx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":55823,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementarytable3.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9370594/v1/017e0c3701831eb739ab844d.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Unpacking the Factors Associated with Loneliness: An Inferential Analysis from the INTERACT Study\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eLoneliness is increasingly recognised as a critical public health challenge, associated with profound implications for mental, physical and social wellbeing[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Accumulating evidence links loneliness to depression, anxiety, cardiovascular disease, cognitive decline and elevated mortality risk highlighting its significance as a biopsychosocial determinant of health[\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. While the COVID-19 pandemic intensified public awareness and policy interest, the epidemiology of loneliness remains underexplored, particularly in terms of its social determinants, distribution across diverse populations and modifiable protective factors such as social capital[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eTo address this evidence gap, the Measuring Loneliness in the UK (INTERACT) Study which uses UCLA-3 item Loneliness Scale [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e] and Office for National Statistics (ONS) Direct Measure of Loneliness (DMOL) [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]scales to measure subjective loneliness was launched as one of the largest volunteer-based investigations of loneliness and social disconnection conducted in the United Kingdom. The first paper in this series (\\u003cb\\u003eEl-Osta et al., 2026a\\u003c/b\\u003e) reported descriptive findings from over 135,000 adults, highlighting that loneliness is both widespread and unequally distributed. Young adults, ethnic minorities, urban residents and those living with disability or unemployment were identified as high-risk groups. Older adults, especially those aged 65 and over, also emerged as a population of interest. Although they reported lower average loneliness scores than younger individuals, the absolute number of older people affected by chronic loneliness was substantial. Within this group, widowed individuals, those living alone and people with long-term conditions or disabilities exhibited significantly higher levels of social disconnection. Importantly, the descriptive findings highlighted stark variations in loneliness levels even among individuals with seemingly similar sociodemographic characteristics, suggesting the influence of underlying structural and contextual factors such as social capital.\\u003c/p\\u003e \\u003cp\\u003eThis second paper builds on those foundational findings by conducting one of the largest inferential analyses of loneliness risk factors to date, drawing on a sample of over 135,000 participants and a broad set of socio-demographic, social and health-related variables across two validated loneliness measures. It applies advanced statistical modelling to estimate adjusted associations between loneliness and key sociodemographic, socioeconomic and health-related characteristics across the life course without implying predictive performance or causal relationships. In doing so, it sheds new light on the nuanced drivers of loneliness among diverse age groups, situating ageing within the broader epidemiology of loneliness.\\u003c/p\\u003e \\u003cp\\u003eA key focus of this study is the role of social capital[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], measured through indicators such as neighbourhood trust, perceived support and community cohesion as a protective factor that may buffer against loneliness risk. This is particularly relevant to older adults, who often face reduced mobility, bereavement and shrinking social networks[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. The hypothesis that place-based, relational and community-level assets can offset loneliness among older populations is explored using adjusted regression models and spatial analysis.\\u003c/p\\u003e \\u003cp\\u003eBuilding on the descriptive architecture established in Paper 1 (\\u003cb\\u003eEl-Osta et al., 2026a\\u003c/b\\u003e), this analysis shifts from pattern characterisation to estimation of conditional associations within the sample, enabling quantification of gradients and the identification of converging correlates across measures. This second paper makes three primary contributions. First, it provides effect size estimates for a broad set of loneliness predictors, including common variables such as household composition, caregiving roles and frequency of social contact. Second, it offers empirical validation of the theorised buffering effect of social capital, an insight with direct relevance to ageing populations. Third, it explores variation in loneliness and social capital across population subgroups, considering contextual factors where measured. Crucially, this paper highlights age-related differences in loneliness across the adult population and supports the need for age-sensitive and life-stage-informed approaches to loneliness prevention, affirming the need for multigenerational public health strategies that acknowledge both the distinct and overlapping loneliness risks faced by younger and older people[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The findings presented here offer essential direction for designing effective, targeted and equity-informed interventions, particularly those aimed at improving social connectedness and resilience in an ageing society.\\u003c/p\\u003e\\n\\u003ch3\\u003eStudy aims\\u003c/h3\\u003e\\n\\u003cp\\u003eThe primary aim of this study was to estimate adjusted associations between loneliness and key demographic, socioeconomic, social and health-related factors within a large non-probability sample. Secondary aims were to (i) describe the distribution of loneliness and social capital across sociodemographic and health-related subgroups, (ii) examine associations between these factors and subjective loneliness using two validated measures, (iii) assess the relationship between social capital and loneliness, and (iv) identify consistent patterns across models to inform hypothesis generation.\\u003c/p\\u003e \"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003cp\\u003eSocial connection is conceptualised as an overarching construct encompassing subjective loneliness, structural social contact (e.g., number of friends or relatives) and contextual dimensions captured through social capital. These components are analytically distinguished, with loneliness treated as a subjective outcome, social contact as a structural exposure, and social capital as a contextual construct.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStudy Design and Data Source\\u003c/h3\\u003e\\n\\u003cp\\u003eFull methodological details, including study design, recruitment and data collection procedures, are provided in Paper 1 of this series \\u003cb\\u003e(El-Osta et al., 2026a).\\u003c/b\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eParticipants and Sampling\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 135,725 adults aged 16 years and older were recruited via NHS primary care networks, voluntary sector organizations and the National Institute for Health and Care Research (NIHR) Be Part of Research Network. The recruitment strategy aimed at demographic and geographic diversity, with targeted outreach to underrepresented populations. Eligibility criteria and exclusion details have been described elsewhere \\u003cb\\u003e(El-Osta et al., 2026a).\\u003c/b\\u003e\\u003c/p\\u003e\\n\\u003ch3\\u003eMeasures\\u003c/h3\\u003e\\n\\u003cp\\u003eLoneliness was assessed indirectly using the validated UCLA Loneliness Scale, with response options categorized as never/hardly ever (scored as 1), some of the time (scored as 2) and often (scored as 3). Each question was scored from 1 to 3 and the total score ranged from 3 to 9. The total scores were further categorized into three levels of loneliness: no loneliness (score\\u0026thinsp;=\\u0026thinsp;3), moderate loneliness (score\\u0026thinsp;=\\u0026thinsp;4\\u0026ndash;6) and severe loneliness (score\\u0026thinsp;=\\u0026thinsp;7\\u0026ndash;9). Given the absence of universally agreed cut-offs for the UCLA-3, scores were categorised pragmatically to reflect increasing severity while preserving ordinal structure for analysis. This pragmatic categorisation approach is consistent with prior population-based studies that have grouped UCLA-3 scores to reflect increasing severity while preserving ordinal structure for regression modelling[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. A single-item DMOL recommended by the ONS was also included in our study. Crucially, the primary analyses retained the ordinal nature of the outcome, minimising reliance on arbitrary thresholds. Differences between UCLA-3 and DMOL estimates reflect established methodological differences between indirect multi-item and direct single-item measures, rather than inconsistency in findings.\\u003c/p\\u003e \\u003cp\\u003eTo measure social capital, we used a seven-item Likert scale with four response categories, based on an instrument developed by Sampson et al [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Response categories were dichotomised such that \\u0026ldquo;agree\\u0026rdquo; or \\u0026ldquo;strongly agree\\u0026rdquo; were scored as 1, and \\u0026ldquo;disagree\\u0026rdquo; or \\u0026ldquo;strongly disagree\\u0026rdquo; as 0, reflecting the presence versus absence of key neighbourhood attributes (e.g., trust, cohesion, shared values), consistent with prior applications of collective efficacy frameworks. Scores were summed to produce an overall index ranging from 0 to 7. Two negatively worded items (\\u0026ldquo;People in this neighbourhood generally don\\u0026rsquo;t get along with each other\\u0026rdquo; and \\u0026ldquo;People in this neighbourhood do not share the same values\\u0026rdquo;) were reverse-coded. The summed index was subsequently dichotomised at the median (score\\u0026thinsp;=\\u0026thinsp;4) to classify low (0\\u0026ndash;4) versus high (\\u0026gt;\\u0026thinsp;4) social capital, facilitating interpretation and binary logistic regression modelling of high versus low social capital. Dichotomisation was used to facilitate interpretability in regression models; however, we acknowledge this may reduce variability and should be interpreted as a pragmatic modelling decision rather than a theoretically derived threshold. While this approach may reduce granularity, it supports comparability with existing literature and population-level analyses [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAll scores for loneliness and social capital in this analysis were derived using a classical test theory approach by summing item responses. Rasch model-derived person measures in logits are reported separately in Paper 3 of this series [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn addition to loneliness and social capital, a range of socio-demographic, social and health-related variables were included as predictors selected a priori based on established theoretical frameworks (see Statistical Analysis). These comprised age group, gender, ethnicity, educational attainment, employment status and marital status, as well as indicators of social connectedness (number of friends and relatives, household size, presence of children and pet ownership) and health status (self-reported disability and long-term conditions). All variables were self-reported and categorised as described in the Statistical Analysis section and corresponding tables.\\u003c/p\\u003e\\n\\u003ch3\\u003eHandling of missing data\\u003c/h3\\u003e\\n\\u003cp\\u003eWe assessed the extent of missing data across all variables in the dataset. Overall, 6.8% of the dataset values were missing. Prior to imputation, we used Little\\u0026rsquo;s MCAR test to examine the missing data mechanism, which suggested that the data were not missing completely at random. Therefore, we proceeded with multiple imputations using the Multivariate Imputation by Chained Equations (MICE) algorithm, implemented via the \\u0026lsquo;\\u003cem\\u003emice\\u003c/em\\u003e\\u0026rsquo; package in R for comparison and robustness checking with complete case analysis. The missing data pattern was inspected using the \\u003cem\\u003emd.pattern() function\\u003c/em\\u003e and methods were tailored based on variable types: polytomous regression (\\u003cem\\u003epolyreg\\u003c/em\\u003e) for nominal categorical variables (e.g., gender, ethnicity), proportional odds models (\\u003cem\\u003epolr\\u003c/em\\u003e) for ordinal variables (e.g., age group, number of relatives and friends, household size) and logistic regression (\\u003cem\\u003elogreg\\u003c/em\\u003e) for binary variables (e.g., having children, pet ownership). We generated five imputed datasets (m\\u0026thinsp;=\\u0026thinsp;5) with 40 iterations per dataset (maxit\\u0026thinsp;=\\u0026thinsp;40), setting a random seed (123) for reproducibility. Diagnostic plots confirmed algorithm convergence, showing stable mean and standard deviation trends across iterations. Post-imputation, analyses were conducted on each dataset individually and the results were pooled using Rubin\\u0026rsquo;s rules to account for imputation uncertainty and derive final estimates \\u003cb\\u003eSupplementary file 1\\u003c/b\\u003e. The primary analyses presented in this paper are based on complete-case models, with multiply imputed analyses conducted as sensitivity analyses to assess robustness.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical Analysis\\u003c/h2\\u003e \\u003cp\\u003ePredictor variables were selected a priori based on established theoretical frameworks, including the Social Determinants of Health [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e] and biopsychosocial models of social isolation [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. These frameworks informed the inclusion of structural (e.g., education, employment), demographic (e.g., age, gender, ethnicity), relational (e.g., marital status, number of friends and relatives, household composition), behavioural/contextual (e.g., pet ownership, caregiving roles) and health-related factors (e.g., disability, long-term conditions), each representing distinct pathways through which social connection and loneliness may be shaped.\\u003c/p\\u003e \\u003cp\\u003eSeparate analyses were conducted for the UCLA-3, DMOL and social capital score following ONS guidance. Participant characteristics were summarized using frequencies and percentages. Differences between groups were assessed using Pearson\\u0026rsquo;s chi-square test.\\u003c/p\\u003e \\u003cp\\u003ePrimary analyses were conducted on complete-case data, with multiply imputed models used for sensitivity analyses. Sample sizes vary across analyses due to variable availability and complete-case restrictions; these are reported for each model. Multivariable regression analyses were conducted to examine associations between sociodemographic, health and social variables and loneliness and social capital.\\u003c/p\\u003e \\u003cp\\u003eOrdinal logistic regression was applied to model associations with loneliness (UCLA-3 and DMOL), while binary logistic regression was used to identify predictors of high versus low social capital. Models were adjusted for key covariates including age, gender, ethnicity, education, employment, marital status, social contacts and health status, with estimates reported as adjusted odds ratios (aORs) with 95% confidence intervals (CI). Unadjusted odds ratios represent crude associations between each predictor and the outcome, while adjusted odds ratios are estimated from multivariable models controlling for all included covariates. Given the non-probability sampling design, estimates represent within-sample associations and are not intended for population-level inference. All statistical analyses were performed using R software version 4.2.2.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eEthical Considerations\\u003c/h3\\u003e\\n\\u003cp\\u003eThe INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study was approved by the NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483). The Strengthening the Reporting of Observational Studies in Epidemiology [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] checklist and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e] were used to improve the quality of the reporting.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eResults are presented descriptively, with interpretation reserved for the Discussion. Given the large sample size (N\\u0026thinsp;=\\u0026thinsp;135,725), statistical significance should be interpreted alongside effect sizes rather than p-values alone. Descriptive distributions of social capital were based on 120,583 respondents with available social capital data, whereas the adjusted regression model included 117,781 complete observations across all covariates. All regression results presented are based on complete-case analysis, with imputed models yielding consistent estimates (\\u003cstrong\\u003eSupplementary File 1\\u003c/strong\\u003e). Given the non-probability sampling design, findings are interpreted as within-sample associations rather than population-level estimates.\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eDescriptive findings\\u003c/h2\\u003e\\n \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003ePrevalence and distribution of loneliness and social capital\\u003c/h2\\u003e\\n \\u003cp\\u003eAmong 135,725 respondents, loneliness and social capital were unequally distributed across demographic, social and health-related groups. \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;1\\u003c/strong\\u003e presents the prevalence of low, moderate and severe loneliness as measured by the UCLA Loneliness Scale across key demographic and social subgroups (N\\u0026thinsp;=\\u0026thinsp;135,725). Clear patterns emerged across age, gender, employment, relationship status and social contact indicators. Using the UCLA Loneliness Scale, 16.5% of participants were classified as experiencing severe loneliness, with the highest burden among younger adults. Only 1.5% of 16-25-year-olds reported no loneliness, compared to 48.7% of respondents aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years. The proportion of individuals reporting severe loneliness decreased steadily with age, consistent with an inverse age-loneliness gradient.\\u003c/p\\u003e\\n \\u003cp\\u003ePatterns of loneliness measured using DMOL closely mirrored those observed with the UCLA scale but offered additional granularity. \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;2\\u003c/strong\\u003e summarises the prevalence of low (never/hardly ever), moderate (occasionally/some of the time) and severe (often/always) loneliness across key demographic and social variables. Among respondents aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65, 45.1% reported \\u0026ldquo;never or hardly ever\\u0026rdquo; feeling lonely, while 11.2% of 16-25-year-olds reported frequent loneliness. Females consistently reported higher levels of moderate and severe loneliness than males across both scales. Notably, individuals identifying as \\u0026lsquo;Other\\u0026rsquo; gender had markedly elevated rates of loneliness relative to their group size, indicating higher reported loneliness within this group. Loneliness was also socially patterned by relationship status, employment and health. Single, divorced or widowed individuals reported the highest loneliness scores, while married or cohabiting participants reported the lowest. Unemployed individuals, those with disabilities and those with long-term conditions were significantly more likely to report moderate or severe loneliness across both UCLA and DMOL measures.\\u003c/p\\u003e\\n \\u003cp\\u003eThe prevalence of low and high social capital across demographic and social characteristics is presented in \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;3.\\u003c/strong\\u003e Social capital scores were dichotomised into low (\\u0026le;\\u0026thinsp;4) and high (\\u0026gt;\\u0026thinsp;4) across demographic and social characteristics in a sample of 120,583 respondents.\\u003c/p\\u003e\\n \\u003cp\\u003eThe reduced sample size reflects restriction to observations with complete data for variables included in the social capital model. Social capital was measured using a composite scale of neighbourhood trust, cohesion and support. The reduced sample size reflects restriction to observations with complete data for variables included in the social capital model. Social capital was measured using a composite scale of neighbourhood trust, cohesion and support. Higher social capital was more frequently observed among older adults, married individuals and those with a greater number of friends or relatives. Conversely, lower social capital was more frequently observed among younger respondents, individuals living alone, the unemployed and those in poor health.. These differences were statistically significant across all examined variables (\\u0026chi;\\u0026sup2; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), consistent with the descriptive patterns observed.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eInferential statistics findings\\u003c/h2\\u003e\\n \\u003cp\\u003eInferential analyses were conducted to explore the independent associations between loneliness and a range of sociodemographic, health and social variables. This section presents findings from bivariate analyses using chi-square tests, followed by multivariable modelling using ordinal and binary logistic regression. These analyses aim to identify key factors associated with loneliness as measured by both the UCLA Loneliness Scale and DMOL and to examine factors associated with higher or lower levels of social capital. Differences observed across groups were formally assessed using chi-square tests, as presented below.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eChi-square analysis results\\u003c/h2\\u003e\\n \\u003cp\\u003eConsistent with the descriptive patterns observed above, chi-square analyses demonstrated statistically significant associations between loneliness and all examined variables for both the UCLA Loneliness Scale and DMOL (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Variables including age, gender, ethnicity, employment status, marital status, disability and long-term conditions were all significantly associated with levels of loneliness. Similarly, for the Social Capital Score, chi-square tests indicated significant variation in community trust and cohesion across demographic groups (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), with lower social capital more prevalent among younger adults, ethnic minority groups and those with poorer health status. Full details are provided in \\u003cstrong\\u003eSupplementary File 2\\u003c/strong\\u003e. These findings support the use of multivariable regression to further explore the independent effects of these factors. These findings are presented in the section below.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eLogistic regression findings\\u003c/h2\\u003e\\n \\u003cp\\u003eMultivariable logistic regression was used to assess the independent effects of sociodemographic, health and social factors on loneliness and social capital. Ordinal models were applied to UCLA and DMOL scores and binary models to social capital. All models adjusted for age, gender, ethnicity, education, employment, marital status, social contacts and health status. This segment presents the findings from multivariable models for UCLA, DMOL and social capital.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eOrdinal regression analysis: UCLA Loneliness Scale\\u003c/h2\\u003e\\n \\u003cp\\u003eMultivariable ordinal logistic regression modelling identified several independent predictors of greater loneliness as measured by the UCLA scale. The analysis of unadjusted and adjusted odds ratios (OR) for various demographic and social factors associated with loneliness (UCLA) is shown in Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e; Fig. \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\n \\u003cp\\u003eThe adjusted odds ratios (aORs) reported here reflect associations after simultaneous adjustment for all included covariates and should be interpreted as conditional estimates rather than causal effects. Age showed the strongest inverse association with loneliness across models. Compared with individuals aged 16\\u0026ndash;25 (reference group), older participants reported substantially lower odds of higher loneliness scores. Adults aged 26\\u0026ndash;35 had 35% lower odds (aOR 0.65, 95% CI: 0.60\\u0026ndash;0.70), while those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 had an 86% reduction in odds (aOR 0.14, 95% CI: 0.13\\u0026ndash;0.16, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Gender was significantly associated with loneliness. Males had reduced odds of loneliness compared to females (aOR 0.81, 95% CI: 0.79\\u0026ndash;0.83, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). While the \\u0026apos;Other\\u0026apos; gender category showed elevated unadjusted loneliness, the adjusted association was marginal and not statistically significant (aOR 1.17, 95% CI: 0.98\\u0026ndash;1.39, p\\u0026thinsp;=\\u0026thinsp;0.08).\\u003c/p\\u003e\\n \\u003cp\\u003eEducational attainment showed a heterogeneous association with loneliness across measures. University graduates had higher odds of loneliness compared to those with secondary education (aOR 1.22, 95% CI: 1.19\\u0026ndash;1.26, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Unemployed participants had nearly twice the odds of loneliness (aOR 1.82, 95% CI: 1.71\\u0026ndash;1.94, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Unpaid carers also reported elevated loneliness (aOR 1.64, 95% CI: 1.51\\u0026ndash;1.78), while retirees had only marginally lower odds (aOR 0.95, 95% CI: 0.91\\u0026ndash;0.99, p\\u0026thinsp;=\\u0026thinsp;0.028), suggesting some a modest protective association with later-life stability.\\u003c/p\\u003e\\n \\u003cp\\u003eMarital status was among the strongest social predictors. Being married or in a civil partnership was associated with markedly lower loneliness (aOR 0.44, 95% CI: 0.42\\u0026ndash;0.46, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In contrast, single individuals (aOR 2.33) and widowed respondents (aOR 1.34) had significantly higher odds of loneliness, consistent with lower odds of loneliness among those who were married or in a relationship. Social contact showed a graded association with loneliness. Respondents with 9 or more friends had an AOR of 0.09 (95% CI: 0.09\\u0026ndash;0.10), while those with 9 or more relatives had an AOR of 0.28 (95% CI: 0.26\\u0026ndash;0.30), were associated with substantially lower odds of loneliness. Participants reporting no social contacts had the highest loneliness burden.\\u003c/p\\u003e\\n \\u003cp\\u003eHealth-related factors were also strongly associated with loneliness. Individuals with a disability had 77% higher odds (aOR 1.77), while those with long-term conditions had 64% higher odds (aOR 1.64), indicating higher odds of loneliness among individuals with chronic health conditions.\\u0026nbsp;\\u003c/p\\u003e\\u0026nbsp;\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eAssociation between demographic and social factors and loneliness as measured by UCLA Loneliness Scale; N\\u0026thinsp;=\\u0026thinsp;122,257 observations in adjusted model.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eUnadjusted OR (CI) \\u0026para;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eAdjusted OR (CI) \\u0026dagger;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eP value *\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e16\\u0026ndash;25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e26\\u0026ndash;35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.70 (0.67, 0.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.65 (0.60, 0.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e36\\u0026ndash;45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.59 (0.56, 0.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.48 (0.45, 0.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e46\\u0026ndash;55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.43 (0.41, 0.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.32 (0.30, 0.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e56\\u0026ndash;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.26 (0.25, 0.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.22 (0.20, 0.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.15 (0.15, 0.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.14 (0.13, 0.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.79 (0.77, 0.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.81 (0.79, 0.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.37 (2.93, 3.87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.17 (0.98, 1.39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.080\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.98 (1.68, 2.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.10 (0.85, 1.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.490\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEducation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSecondary school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eA levels/College\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.02 (1.00, 1.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.092\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.11 (1.07, 1.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUniversity Degree or higher\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.80 (0.77, 0.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.22 (1.19, 1.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEmployment\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed full-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed part-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.79 (0.77, 0.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (1.03, 1.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.35 (3.13, 3.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.82 (1.68, 1.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eRetired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.42 (0.41, 0.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.95 (0.91, 0.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.68 (0.65, 0.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.10 (1.04, 1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eStudent (full or part-time)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.28 (2.14, 2.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.25 (1.15, 1.36)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnemployed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e4.71 (4.47, 4.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.82 (1.71, 1.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnpaid carer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.64 (2.39, 2.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.24 (1.99, 2.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eVolunteer (full or part-time)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.60 (0.54, 0.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.09 (0.98, 1.22)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.125\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEthnicity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAsian/Asian British\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.79 (1.70, 1.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.23 (1.15, 1.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBritish Black/African/Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.74 (1.61, 1.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (0.99, 1.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.097\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMixed/Multiple ethnic groups\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.85 (1.70, 2.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.06 (0.96, 1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.277\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther ethnic group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.60 (1.49, 1.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.06 (0.97, 1.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.214\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite and Black Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.33 (1.96, 2.77)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.14 (0.93, 1.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.213\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital Status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSingle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDivorced\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.62 (0.59, 0.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.05 (1.00, 1.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.061\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIn a relationship\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.41 (0.39, 0.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.50 (0.48, 0.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMarried / Civil partnership\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.18 (0.17, 0.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.44 (0.42, 0.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.76 (0.70, 0.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.97 (0.89, 1.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.497\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWidowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.45 (0.43, 0.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.34 (1.27, 1.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of relatives\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 relative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.92 (0.87, 0.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.02 (0.95, 1.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.567\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.68, 0.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.85 (0.80, 0.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.37 (0.35, 0.39)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.57 (0.54, 0.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.16 (0.16, 0.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.39 (0.37, 0.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.09 (0.08, 0.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.28 (0.26, 0.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of friends\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 friend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.80 (0.76, 0.84)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.80 (0.76, 0.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.47 (0.45, 0.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.52 (0.49, 0.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.22 (0.21, 0.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.29 (0.28, 0.31)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.10 (0.10, 0.11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.16 (0.15, 0.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.05 (0.05, 0.06)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.09 (0.09, 0.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving pet\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.24 (1.22, 1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.04 (1.02, 1.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of household members\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 member\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.37 (0.36, 0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.73 (0.70, 0.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u0026ndash;3 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.62 (0.60, 0.64)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.68 (0.65, 0.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e4\\u0026ndash;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.68, 0.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.66 (0.62, 0.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.08 (0.98, 1.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.130\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.69 (0.61, 0.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving children\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.48 (0.47, 0.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.06 (1.03, 1.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving disability\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.51 (3.42, 3.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.77 (1.71, 1.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.49 (3.28, 3.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.59 (1.47, 1.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving long-term condition\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.76 (2.70, 2.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.64 (1.60, 1.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.05 (2.85, 3.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.68 (1.55, 1.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026para;\\u003c/strong\\u003e: Unadjusted (crude) odds ratios from models including each predictor individually\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026dagger;\\u003c/strong\\u003e: Ordinal logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e: Significance level, with values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered statistically significant.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Values\\u0026thinsp;\\u0026lt;\\u0026thinsp;1 indicate lower odds of higher loneliness.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eOrdinal regression analysis: Direct Measure of Loneliness (DMOL)\\u003c/h2\\u003e\\n \\u003cp\\u003eThe analysis of unadjusted and adjusted odds ratios for various demographic and social factors associated with DMOL is shown in Table \\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e; Fig. \\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Findings from the DMOL model were directionally consistent with the UCLA scale, though some differences in magnitude and statistical significance emerged. Age remained a strong predictor: respondents aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 had an AOR of 0.19 (95% CI: 0.18\\u0026ndash;0.21), confirming a lower risk of frequent loneliness in later life. Gender differences persisted, with males again showing lower odds than females (aOR 0.74, 95% CI: 0.72\\u0026ndash;0.76) and no significant association for \\u0026lsquo;Other\\u0026rsquo; gender (aOR 0.94, p\\u0026thinsp;=\\u0026thinsp;0.455). Educational attainment showed a small protective effect in the DMOL model (AOR for university degree: 0.96, 95% CI: 0.93\\u0026ndash;0.99, p\\u0026thinsp;=\\u0026thinsp;0.008), diverging from the findings in the UCLA model.\\u003c/p\\u003e\\n \\u003cp\\u003eUnemployment was again a strong predictor (aOR 1.68, 95% CI: 1.59\\u0026ndash;1.78), with unpaid carers similarly affected (aOR 1.68). The effect of being single remained strong (aOR 2.46), while widowed respondents also had elevated risk (aOR 1.49, 95% CI: 1.41\\u0026ndash;1.57). Social contact maintained a robust protective role: respondents with 9\\u0026thinsp;+\\u0026thinsp;friends had 84% lower odds (aOR 0.16, 95% CI: 0.15\\u0026ndash;0.17).\\u003c/p\\u003e\\n \\u003cp\\u003eHealth variables mirrored previous findings. Disability (aOR 1.44) and long-term conditions (aOR 1.53) remained strongly associated with greater loneliness. Having children was associated with a slight increase in loneliness (aOR 1.19), while pet ownership was not a significant factor after adjustment.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eAssociation between demographic and social factors and loneliness as measured by DMOL; N\\u0026thinsp;=\\u0026thinsp;122,257 observations in adjusted model.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eUnadjusted OR (CI) \\u0026para;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eAdjusted OR (CI) \\u0026dagger;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eP value *\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e16\\u0026ndash;25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e26\\u0026ndash;35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.76 (0.72, 0.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.73 (0.68, 0.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e36\\u0026ndash;45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.64 (0.61, 0.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.54 (0.52, 0.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e46\\u0026ndash;55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.49 (0.46, 0.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.38 (0.36, 0.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e56\\u0026ndash;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.31 (0.29, 0.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.27 (0.25, 0.29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.20 (0.19, 0.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.19 (0.18, 0.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.73 (0.72, 0.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.74 (0.72, 0.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.42 (2.12, 2.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.94 (0.80, 1.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.455\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.46, 2.04)\\u003c/p\\u003e\\n 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\\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSecondary school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eA levels/College\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.93 (0.91, 0.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.96 (0.93, 0.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.010\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUniversity Degree or higher\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.70 (0.68, 0.72)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.96 (0.93, 0.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEmployment\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed full-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed part-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.87 (0.84, 0.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (1.04, 1.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.96 (2.78, 3.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.61 (1.50, 1.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eRetired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.49 (0.47, 0.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.96 (0.92, 1.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.057\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.71 (0.68, 0.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (1.02, 1.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.007\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eStudent (full or part-time)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.02 (1.89, 2.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (0.99, 1.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.071\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnemployed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e4.15 (3.96, 4.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.68 (1.59, 1.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnpaid carer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.18 (1.98, 2.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n 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colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEthnicity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAsian/Asian British\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.88 (1.78, 1.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.41 (1.32, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBritish Black/African/Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.83 (1.70, 1.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.23 (1.13, 1.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMixed/Multiple ethnic groups\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.71 (1.58, 1.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.06 (0.97, 1.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.223\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther ethnic group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.73 (1.60, 1.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.24 (1.14, 1.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite and Black Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.24 (1.88, 2.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.13 (0.93, 1.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.237\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital Status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSingle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDivorced\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.65 (0.63, 0.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.99 (0.95, 1.04)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.789\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIn a relationship\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.39 (0.37, 0.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.47 (0.45, 0.50)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMarried / Civil partnership\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.20 (0.19, 0.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.44 (0.42, 0.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.76, 0.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.01 (0.93, 1.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.807\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWidowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.58 (0.55, 0.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.49 (1.41, 1.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of relatives\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 relative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.88 (0.83, 0.93)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.94 (0.89, 1.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.072\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.71 (0.67, 0.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.77, 0.87)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.40 (0.38, 0.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.60 (0.57, 0.63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.19 (0.18, 0.20)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.41 (0.39, 0.44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.11 (0.10, 0.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.30 (0.28, 0.32)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of friends\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 friend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.78, 0.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.84 (0.79, 0.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.53 (0.50, 0.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.61 (0.59, 0.65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.29 (0.27, 0.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.41 (0.39, 0.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.15 (0.15, 0.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.26 (0.25, 0.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.08 (0.08, 0.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.16 (0.15, 0.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving pet\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.16 (1.13, 1.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.00 (0.97, 1.02)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.720\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of household members\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 member\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.37 (0.36, 0.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.77 (0.74, 0.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u0026ndash;3 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.60 (0.59, 0.62)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.69, 0.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e4\\u0026ndash;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.72 (0.69, 0.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.75 (0.70, 0.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.11 (1.01, 1.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.037\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.84 (0.75, 0.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving children\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.56 (0.55, 0.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.19 (1.15, 1.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving disability\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.89 (2.81, 2.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.44 (1.39, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.98 (2.78, 3.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.33 (1.24, 1.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving long-term condition\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.43 (2.38, 2.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.53 (1.49, 1.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.73 (2.55, 2.92)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.53 (1.42, 1.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026para;\\u003c/strong\\u003e: Unadjusted (crude) odds ratios from models including each predictor individually\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026dagger;\\u003c/strong\\u003e: Ordinal logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e: Significance level, with values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered statistically significant.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from ordinal logistic regression. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Values\\u0026thinsp;\\u0026lt;\\u0026thinsp;1 indicate lower odds of higher loneliness.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eBinary regression analysis: Social capital\\u003c/h2\\u003e\\n \\u003cp\\u003eTable \\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e presents adjusted odds ratios for various demographic and social factors associated with Social Capital Scale. The resulting forest plot is presented in Fig. \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Binary logistic regression identified several factors associated with higher versus lower social capital. Age showed a strong positive association. Compared with the 16\\u0026ndash;25 reference group, those aged 40\\u0026ndash;64 had more than twice the odds of high social capital (aOR 2.38), while those aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 had over threefold higher odds (aOR 3.21, 95% CI: 2.93\\u0026ndash;3.51). Marital status was also associated with social capital. Individuals who were married or in a civil partnership had higher odds of reporting high social capital (aOR 1.60), while widowed individuals also showed increased odds (aOR 1.39). In contrast, being single was associated with lower social capital. Employment status demonstrated a gradient. Retired individuals had higher odds of high social capital (aOR 1.49), whereas unemployment (aOR 0.91) and unpaid caregiving (aOR 0.86) were associated with reduced odds.\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eSocial contact remained strongly associated with social capital. Participants reporting more frequent contact with friends and relatives had progressively higher odds of high social capital, with those reporting nine or more friends showing markedly elevated odds (aOR 5.22).\\u003c/p\\u003e\\n \\u003cp\\u003eHealth-related factors were inversely associated with social capital. Individuals with a disability (aOR 0.77) or a long-term condition (aOR 0.82) had lower odds of reporting high neighbourhood trust, cohesion and support.\\u0026nbsp;\\u003c/p\\u003e\\n \\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eAssociation between demographic and social factors and Social Capital Scale; N\\u0026thinsp;=\\u0026thinsp;117,781 observations in adjusted model.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eAge\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eUnadjusted OR (CI) \\u0026para;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003eP value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eAdjusted OR (CI) \\u0026dagger;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003eP value *\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e16\\u0026ndash;25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e26\\u0026ndash;35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.19 (1.11, 1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.15 (1.06, 1.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e36\\u0026ndash;45\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.79 (1.68, 1.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.8 (1.66, 1.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e46\\u0026ndash;55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.38 (2.25, 2.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.38 (2.2, 2.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e56\\u0026ndash;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.33 (3.14, 3.52)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.72 (2.51, 2.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;65\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5.37 (5.08, 5.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e3.21 (2.93, 3.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.04 (1.02, 1.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.04 (1.01, 1.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.31 (0.26, 0.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.75 (0.63, 0.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.56 (0.45, 0.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.14 (0.89, 1.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEducation\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSecondary school\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eA levels/College\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.09 (1.05, 1.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.14 (1.1, 1.18)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUniversity Degree or higher\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.3 (1.26, 1.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.15 (1.11, 1.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEmployment\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed full-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eEmployed part-time\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.44 (1.38, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.19 (1.14, 1.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eVolunteer (full or part-time)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.93 (1.73, 2.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.28 (1.13, 1.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eRetired\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.54 (2.47, 2.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.49 (1.42, 1.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.72 (1.63, 1.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.26 (1.19, 1.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eStudent (full or part-time)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.53 (0.49, 0.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.9 (0.82, 0.99)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnemployed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.42 (0.39, 0.44)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.91 (0.86, 0.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eUnpaid carer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.67 (0.6, 0.75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.86 (0.76, 0.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.012\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.65 (0.61, 0.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.08 (1, 1.16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.066\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEthnicity\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eBritish Black/African/Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.32 (0.29, 0.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.47 (0.43, 0.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMixed/Multiple ethnic groups\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.51 (0.46, 0.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.8 (0.72, 0.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWhite and Black Caribbean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.44 (0.36, 0.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.75 (0.61, 0.94)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eAsian/Asian British\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.54 (0.51, 0.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.76, 0.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther ethnic group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.51 (0.47, 0.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.67 (0.61, 0.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003esingle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eDivorced\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.65 (1.58, 1.73)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.03 (0.97, 1.08)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.336\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eIn a relationship\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.54 (1.47, 1.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.22 (1.16, 1.29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eMarried / Civil partnership\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.31 (3.21, 3.41)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.6 (1.53, 1.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWidowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.2 (3.05, 3.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.39 (1.31, 1.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.26 (1.17, 1.37)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.98 (0.89, 1.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.588\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of relatives\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 relative\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.25 (1.17, 1.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.18 (1.1, 1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.55 (1.46, 1.65)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.39 (1.31, 1.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e2.32 (2.2, 2.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.69 (1.59, 1.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.97 (3.75, 4.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.1 (1.96, 2.24)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more relatives\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5.61 (5.27, 5.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.34 (2.17, 2.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of friends\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 friend\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.35 (1.28, 1.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.34 (1.26, 1.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.99 (1.89, 2.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.79 (1.69, 1.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e3 or 4 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e3.36 (3.2, 3.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e2.65 (2.52, 2.79)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e5\\u0026ndash;8 friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e5.69 (5.41, 5.98)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e3.88 (3.67, 4.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e9 or more friends\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e8.51 (8.08, 8.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e5.22 (4.92, 5.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving pet\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.03 (1, 1.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e0.022\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.24 (1.21, 1.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of household members\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e0 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e1 member\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.59 (1.54, 1.63)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.05 (1, 1.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e2\\u0026ndash;3 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.11 (1.07, 1.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.13 (1.08, 1.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e4\\u0026ndash;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.92 (0.88, 0.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e1.09 (1.02, 1.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;\\u0026thinsp;5 members\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.6 (0.54, 0.67)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.98 (0.86, 1.11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e0.729\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eChildren\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e1.85 (1.8, 1.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.93 (0.9, 0.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDisability\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.5 (0.48, 0.51)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.77 (0.74, 0.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.39 (0.36, 0.42)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.68 (0.62, 0.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLongterm conditions\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003eRef.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.56 (0.55, 0.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.82 (0.8, 0.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\n \\u003cp\\u003e0.45 (0.41, 0.48)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\n \\u003cp\\u003e0.75 (0.69, 0.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c5\\\" namest=\\\"c1\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026para;\\u003c/strong\\u003e: Unadjusted (crude) odds ratios from models including each predictor individually.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026dagger;\\u003c/strong\\u003e: Binary logistic regression model adjusted for age, gender, education, employment, ethnicity, marital status, having relatives, having friends, having pets, household size, having children, having disability and having long-term condition.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e*\\u003c/strong\\u003e: Significance level, with values\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 considered statistically significant.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eForest plot showing adjusted odds ratios (aORs) and 95% confidence intervals from binary logistic regression. Social capital was dichotomised at the median. Models adjust for age, gender, ethnicity, education, employment, marital status, social contacts, household size, pet ownership, children, disability and long-term conditions. Reference categories are shown. Values\\u0026thinsp;\\u0026gt;\\u0026thinsp;1 indicate higher odds of high social capital.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eConverging factors associated with loneliness and social capital\\u003c/h2\\u003e\\n \\u003cp\\u003eAcross all three models, several consistent patterns were observed. Younger age was the most robust and universal risk factor, strongly associated with both higher levels of loneliness and lower levels of social capital. Conversely, frequent social contact with friends and relatives was protective in every model, underscoring the importance of interpersonal relationships in fostering connection and reducing isolation. Other consistent predictors of vulnerability included being single or widowed, unemployment and disability, each linked to greater loneliness and reduced social capital, suggesting a compounding effect of social and structural disadvantage.\\u003c/p\\u003e\\n \\u003cp\\u003eImportantly, higher social capital itself was consistently associated with lower odds of loneliness. This pattern is consistent with a potential buffering hypothesis of social capital, reinforcing the central role of community cohesion in shaping psychosocial wellbeing. Some divergences were also observed: notably, educational attainment was associated with reduced loneliness in the DMOL and social capital models but paradoxically linked to greater loneliness in the UCLA model. This variation may reflect underlying differences in how the two loneliness measures capture emotional versus social aspects of disconnection or how education interacts with unmeasured contextual factors such as occupational stress or urbanicity.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSummary of principal findings\\u003c/h2\\u003e \\u003cp\\u003eDrawing on data from over 135,000 adults, this study presents one of the largest inferential analyses of loneliness and social capital within a non-probability sample in the UK. By shifting from the descriptive characterisations presented in paper 1 \\u003cb\\u003e(El-Osta et al., 2026a)\\u003c/b\\u003e to estimation of adjusted associations, this second paper in the INTERACT series lays the groundwork for targeted, equity-informed and context-sensitive interventions. However, given the non-probability sampling design, regression estimates represent within-sample associations rather than population-level effects and should be interpreted as conditional relationships within the responding cohort and as hypothesis-generating rather than generalisable estimates.\\u003c/p\\u003e \\u003cp\\u003eUsing two validated tools (UCLA-3 and DMOL), we identified clear and consistent sociodemographic and social gradients in loneliness. Younger adults, particularly those aged 16\\u0026ndash;25, reported the highest burden of loneliness, while older adults generally reported lower levels. However, within the older population (\\u0026ge;\\u0026thinsp;65), important disparities were observed, especially among those who were widowed, living alone or managing chronic health conditions. Social contact, measured by the number of close friends or relatives, a known indicator of social connection, emerged as one of the strongest and most consistent factors associated with lower loneliness across all models. Similarly, social capital, which is conceptualised as neighbourhood trust, cohesion and reciprocity, was also inversely associated with loneliness and was itself socially patterned, being most prevalent among older, married and retired individuals. Our inclusion of social capital as both an outcome and a contextual correlate draws from the social determinants of health framework advocated by the WHO [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Our findings show that neighbourhood trust, civic participation and perceptions of safety are not just background variables, but factors that shape individuals\\u0026rsquo; capacity to build and sustain meaningful relationships across the life course.\\u003c/p\\u003e \\u003cp\\u003eAmong older adults, the protective association of retirement, stable relationships and high social capital points to a potential buffering role of community integration and relational continuity. However, this seemingly protective effect was not uniformly distributed since older individuals who were widowed, living alone or experiencing multimorbidity faced significantly elevated loneliness risk, echoing findings from the English Longitudinal Study of Ageing[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. This highlights the need to disaggregate the \\\"older adult\\\" category and address intra-cohort heterogeneity in loneliness trajectories.\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings emphasise age-related differences in loneliness within a cross-sectional sample and support age-sensitive approaches, while longitudinal data are needed to examine trajectories over time. Nevertheless, our findings align with socioecological models of health which emphasise that loneliness is shaped by multilevel influences, including individual traits, interpersonal ties, community environments and structural forces [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. While individual factors such as age, disability or employment status matter, they operate within broader systems of opportunity, access and belonging. Consistent with Paper 1 which demonstrated substantial heterogeneity in loneliness across subgroups, the present analysis quantifies these gradients and identifies converging associations across multiple measures. However, we emphasise that these findings reflect cross-sectional associations and do not establish causal or mediating relationships.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eComparison with existing literature\\u003c/h2\\u003e \\u003cp\\u003eOur results corroborate prior evidence showing a reversed age-loneliness gradient in the general population, where younger adults report greater loneliness than older adults [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Our study\\u0026rsquo;s large sample size brings new insight into the dual burden of loneliness in later life, where some older individuals demonstrate resilience, while others, particularly those who are widowed, isolated or unwell, remain at elevated risk.\\u003c/p\\u003e \\u003cp\\u003eThe strong protective role of social contact aligns with longitudinal findings from ELSA and international reviews [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Our data add precision by quantifying this gradient and showing that individuals reporting a greater number of social ties (e.g. more than four friends or relatives) had substantially lower odds of loneliness.\\u003c/p\\u003e \\u003cp\\u003eIn relation to older adults, our findings echo those of Victor et al. in 2005 [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e], who distinguished between emotional loneliness (absence of close attachment) and social loneliness (lack of broader networks). Although this distinction not captured in our measures, it was evident in subgroup patterns and could explain why older adults who were widowed but socially embedded may experience less social loneliness but elevated emotional loneliness.\\u003c/p\\u003e \\u003cp\\u003eIntriguingly, the observed association between higher educational attainment and increased loneliness on the UCLA measure contrasts with prior studies linking higher education to improved social and mental health outcomes[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. This pattern may also be interpreted through expectation-discrepancy frameworks whereby individuals with greater educational attainment may hold higher expectations of social connectedness, increasing the likelihood of perceived deficits despite comparable objective networks. The observed divergence may also reflect a combination of measurement differences between indirect (UCLA) and direct (DMOL) instruments, as well as underlying contextual and structural factors. For example, higher educational attainment may be associated with different expectations of social connectedness, patterns of social mobility or the composition and stability of social networks. It may also reflect contextual influences such as work-related demands, urban residence, digitally mediated forms of interaction or differences in neighbourhood integration among more highly educated individuals [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. However, these mechanisms cannot be directly assessed within the present analysis and should be interpreted cautiously.\\u003c/p\\u003e \\u003cp\\u003eThe slightly elevated loneliness observed among individuals with children-particularly in the DMOL model reflects the complex emotional demands of caregiving. For both younger and older parents, caregiving responsibilities can reduce time for reciprocal adult relationships, especially in the context of single parenting, chronic illness or financial stress. The lack of a significant protective association between pet ownership and loneliness was also interesting but unsurprising since companion animals do not fully substitute for human social contact or that their protective effects are context-specific [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. We hypothesise that these dynamics may be more salient among older adults who live alone, highlighting the need for future research to differentiate between emotional and structural sources of connection.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003eStrengths and limitations\\u003c/h2\\u003e \\u003cp\\u003eThis study\\u0026rsquo;s primary strength lies in its scale and scope, representing the largest UK-based analysis of loneliness and social capital to date. It includes validated measures of loneliness and adopts a novel approach by modelling social capital not only as a predictor but also as an outcome thus enabling a more integrated assessment of relational and structural dimensions of social connection. The analysis also provides age-disaggregated insights, highlighting both resilience and vulnerability among older adults. However, several limitations should be considered, some of which are detailed in Paper 1 (\\u003cb\\u003eEl-Osta et al., 2026a\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe principal limitations of this study are the non-probability sampling design which limits generalisability and does not support population-level inference, and the cross-sectional design, which precludes causal inference. Self-reported data may introduce recall and social desirability bias, and online sampling may underrepresent individuals with cognitive or digital access barriers, particularly among older adults. Additionally, variables such as income and more granular measures of socioeconomic position were not included and may interact with loneliness, warranting further investigation.\\u003c/p\\u003e \\u003cp\\u003eA further limitation is the reliance on established, yet inherently constrained measures of loneliness. While the UCLA-3 and ONS single-item scales were selected to ensure comparability with existing national datasets, they do not fully capture the multidimensional nature of loneliness. The UCLA scale, originally developed within a specific demographic context, provides limited insight into duration, contextual drivers and experiential aspects of loneliness. Similarly, the ONS measure captures frequency but does not specify a timeframe or underlying causes. Future research should prioritise the development and validation of contemporary, multidimensional measures that better reflect the complexity and lived experience of loneliness across diverse populations. These limitations are addressed in part through ongoing psychometric work reported elsewhere [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], and through parallel efforts to develop and validate novel measures of social connection (the INTERACTION Scale) as will be reported in a forthcoming paper elsewhere.\\u003c/p\\u003e \\u003cp\\u003eWe acknowledge that the absence of area-level contextual variables, such as rural-urban classification and neighbourhood deprivation within the present analytical models is also a limitation. These factors are recognised as important structural determinants of loneliness and may contribute to geographic and socioeconomic inequalities in social connectedness. While such variables were available within the INTERACT dataset and incorporated into geospatial analyses, they were not included in the current modelling framework. This reflects the intentional focus of this paper on individual-level socio-demographic, social and health-related correlates to provide a clear and interpretable analytical baseline. More detailed examination of area-level determinants, including rurality and deprivation, is being undertaken in complementary analyses within the INTERACT programme.\\u003c/p\\u003e \\u003cp\\u003eAnother important limitation is the reliance on main-effects models which estimate independent associations but do not capture the combined influence of intersecting socio-demographic characteristics. Although interaction modelling was considered, the inclusion of multiple higher-order terms across numerous predictors in a large dataset risks over-parameterisation, multicollinearity and reduced interpretability. Accordingly, the present analysis does not account for the compounded or intersectional nature of vulnerability, where co-occurring characteristics (e.g. age, disability and socioeconomic status) may interact in non-additive ways. Future research should apply theoretically specified interaction models or alternative approaches such as latent class or intersectional frameworks to better capture these complex patterns. Furthermore, the use of regression models within a non-probability sample does not support population-level inference and observed associations may reflect underlying selection mechanisms rather than true population relationships.\\u003c/p\\u003e \\u003cp\\u003eFinaly, although this analysis focuses on individual-level determinants to provide interpretable baseline estimates, area-level factors, including rural-urban classification and neighbourhood deprivation, represent important contextual extensions. These are examined in complementary analyses within the wider INTERACT programme (manuscript in preparation).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplications for policy and future research\\u003c/h2\\u003e \\u003cp\\u003eGiven the cross-sectional and non-probability nature of the data, the following implications should be interpreted as hypothesis-informed rather than prescriptive. Given that loneliness is a multigenerational challenge and not confined to any one age group, while younger adults represent an emerging high-risk group, the enduring vulnerability of certain subgroups of older adults particularly those who are widowed, unpartnered, disabled or experiencing multimorbidity must remain a priority in research and policy responses. To address this, and in line with the WHO\\u0026rsquo;s Age-Friendly Communities framework, local authorities could invest in inclusive public infrastructure, intergenerational programmes and community hubs to support social connection among older adults. Approaches such as group-based activities, community choirs, neighbourhood walking groups or digital literacy training have shown promise in reducing isolation and improving wellbeing in later life [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. At the same time, efforts to build social capital through safer, more cohesive and civically active neighbourhoods will have spillover benefits across the life course. Housing design, transport planning and digital access all matter. Interventions must be place-based, equity-informed and rooted in community engagement to address both the symptoms and actors associated with loneliness.\\u003c/p\\u003e \\u003cp\\u003eFuture research should also adopt longitudinal and mixed method designs to understand how loneliness evolves with life transitions (e.g. retirement, bereavement) and how community infrastructure and policy reforms shape these trajectories. Expanding the psychometric validation of new social capital tools, particularly for diverse ageing populations, is also a priority. To this end, the INTERACT programme of work reflects a staged measurement strategy, where established instruments support comparability in early phases, followed by iterative psychometric refinement and instrument development. The next phase of this research presented in the third paper of this series [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], employed the Rasch Rating Scale Model to examine the psychometric properties of the loneliness, social capital, and COVID-19 context questionnaires. This modern measurement approach was used to evaluate item fit, person reliability, response category functioning, and the one-dimensionality of each construct. Rasch analysis also enabled the identification of response biases, disordered thresholds, and differential item functioning across key sociodemographic groups, particularly age and gender, providing a complementary perspective to the findings reported in this article. In addition, person-item targeting was assessed to determine how well the item difficulties matched the distribution of respondents\\u0026rsquo; trait levels, and separation indices were computed to quantify the instrument\\u0026rsquo;s precision in distinguishing between different levels of the latent traits. As data collection continues, including from under-served populations and so called \\u0026ldquo;hard to reach\\u0026rdquo; communities, the INTERACT programme aims to provides one of the most comprehensive investigations into the epidemiology and measurement of loneliness to date, delivering robust, data-driven insights to inform public health policy and intervention design.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eLoneliness is a complex and layered experience that reflects individual disposition, as well as structural and relational disadvantage. This study provides large-scale evidence on the distribution of factors associated with loneliness within a non-probability sample. Findings highlight consistent gradients across age, social relationships and health status, and underscore the importance of considering both individual and contextual dimensions of social disconnection. These results provide a foundation for future longitudinal and intervention-focused research.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe INTERACT study was registered on the NIHR Portfolio (CPMS#52230). Ethical approval was granted from the NHS Research Ethics Committee (#21IC6950) and the Imperial College London Research Ethics Committee (ICREC #305483). All participants provided informed electronic consent prior to participation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDue to ethical and data protection constraints, individual-level data cannot be publicly shared. A de-identified dataset and accompanying codebook will be made available upon reasonable request from the corresponding author, subject to institutional approvals and data protection requirements.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research received no funding. Austen El-Osta is grateful for support by the National Institute for Health \\u0026amp; Care Research (NIHR) Applied Research Collaboration NorthWest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or Department of Health and Social Care.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAEO conceived the study. AEO, MA-A, AA, SA, AT-L and AM contributed to study design, data analysis and interpretation. AEO drafted the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. AEO is the guarantor.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors thank all participants who contributed to the INTERACT study and the partner organisations, including NHS networks and voluntary sector organisations that supported recruitment and dissemination. Austen El-Osta is grateful to Professor Pamela Qualter for her suggestion to include a social capital scale, and to Dr Nina Goldmann for insightful discussions.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eHolt-Lunstad, J., \\u003cem\\u003eSocial connection as a critical factor for mental and physical health: evidence, trends, challenges, and future implications\\u003c/em\\u003e. World Psychiatry, 2024. 23(3): p. 312\\u0026ndash;332.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHong, J.H., et al., \\u003cem\\u003eAre loneliness and social isolation equal threats to health and well-being? An outcome-wide longitudinal approach\\u003c/em\\u003e. SSM - Population Health, 2023. 23: p. 101459.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSampson, R.J. and C. 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Newport: Office for National Statistics, 2018.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWigfield, A., \\u003cem\\u003eCampaign to end loneliness.\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHolt-Lunstad, J., et al., \\u003cem\\u003eLoneliness and Social Isolation as Risk Factors for Mortality:A Meta-Analytic Review\\u003c/em\\u003e. Perspectives on Psychological Science, 2015. 10(2): p. 227\\u0026ndash;237.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVictor, C.R., et al., \\u003cem\\u003eThe prevalence of, and risk factors for, loneliness in later life: a survey of older people in Great Britain\\u003c/em\\u003e. Ageing and Society, 2005. 25(6): p. 357\\u0026ndash;375.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePinquart, M. and S. and Sorensen, \\u003cem\\u003eInfluences on Loneliness in Older Adults: A Meta-Analysis\\u003c/em\\u003e. Basic and Applied Social Psychology, 2001. 23(4): p. 245\\u0026ndash;266.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBarreto, M., et al., \\u003cem\\u003eLoneliness around the world: Age, gender, and cultural differences in loneliness\\u003c/em\\u003e. Personality and Individual Differences, 2021. 169: p. 110066.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCarr, S. and C. Fang, \\u003cem\\u003eA gradual separation from the world: a qualitative exploration of existential loneliness in old age\\u003c/em\\u003e. Ageing and Society, 2023. 43(6): p. 1436\\u0026ndash;1456.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGardiner, C., G. Geldenhuys, and M. Gott, \\u003cem\\u003eInterventions to reduce social isolation and loneliness among older people: an integrative review\\u003c/em\\u003e. Health Soc Care Community, 2018. 26(2): p. 147\\u0026ndash;157.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-global-and-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)\",\"snPcode\":\"44263\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44263/3\",\"title\":\"BMC Global and Public Health\",\"twitterHandle\":\"@BMC_GPH\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Loneliness, Social isolation, Social capital, Public health, Mental health, Social cohesion, Community interventions\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9370594/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9370594/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eLoneliness is a pressing public health concern with wide-ranging impacts on mental, physical and social wellbeing. Building on the INTERACT Study which represents one of the largest volunteer-based studies of loneliness and social disconnection conducted in the UK, this paper explores demographic, social and health-related predictors of loneliness and social capital, using multiple validated measures.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe analysed cross-sectional data from 135,725 community-dwelling adults across England. Loneliness was assessed using both the UCLA 3-item Loneliness Scale and the ONS Direct Measure of Loneliness (DMOL). Social capital was measured using a composite scale of neighbourhood trust, cohesion and reciprocity. Multivariable ordinal logistic regression was used to examine factors associated with loneliness; binary logistic regression was used to analyse correlates of high versus low social capital.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eYounger age (particularly 16\\u0026ndash;25), being single, unemployed or living with disability were consistently associated with higher loneliness across both scales. In contrast, greater social contact (having nine or more friends or relatives) was strongly protective (UCLA: aOR 0.09; DMOL: aOR 0.16). University education was associated with higher loneliness on the UCLA scale but lower loneliness on the DMOL. High social capital was more prevalent among older, married and retired individuals and strongly predicted lower loneliness. Respondents with long-term conditions or disability had reduced odds of high social capital (aORs 0.82 and 0.77 respectively).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eThis study highlights consistent sociodemographic and social factors associated with loneliness, as well as the protective role of social capital. Findings highlight population subgroups that may warrant prioritisation in future intervention research and policy prescriptions that address social connection among young adults, single people, the unemployed and individuals in poor health. Given the non-probability sampling design, findings are not intended to estimate population parameters, but support further evaluation of strategies that strengthen neighbourhood cohesion and social infrastructure to mitigate loneliness and strengthen community wellbeing.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Unpacking the Factors Associated with Loneliness: An Inferential Analysis from the INTERACT Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-20 17:10:30\",\"doi\":\"10.21203/rs.3.rs-9370594/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-12T11:33:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"197280555797044816036205148719662125353\",\"date\":\"2026-05-12T08:05:22+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-12T02:59:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-13T09:49:47+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-04-13T09:26:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Global and Public Health\",\"date\":\"2026-04-13T06:38:43+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-global-and-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)\",\"snPcode\":\"44263\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44263/3\",\"title\":\"BMC Global and Public Health\",\"twitterHandle\":\"@BMC_GPH\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c21953ee-b873-41ab-99d8-e037407d5342\",\"owner\":[],\"postedDate\":\"April 20th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-12T11:33:38+00:00\",\"index\":21,\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"197280555797044816036205148719662125353\",\"date\":\"2026-05-12T08:05:22+00:00\",\"index\":20,\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-12T02:59:19+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-20T17:10:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-20 17:10:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9370594\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9370594\",\"identity\":\"rs-9370594\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}