Unpacking the Predictors of Loneliness: An Inferential Analysis from the INTERACT Study

Unpacking the Predictors of 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 Predictors of 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El-Osta, Mahmoud Al-Ammouri, Aos Alaa, Sami Altalib, Agustin Tristán-López, and 1 more This is a preprint; it has not been peer reviewed by a journal.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-6864203/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Loneliness is a pressing public health concern with wide-ranging impacts on mental, physical and social wellbeing. Building on the INTERACT Study-the largest UK-based investigation of loneliness-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 135,722 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 predictors of 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 0.65 and 0.59 respectively). Conclusions This study highlights consistent sociodemographic and social predictors of loneliness, as well as the protective role of social capital. Findings support the need for targeted public health interventions that address social connection among young adults, single people, the unemployed and individuals in poor health. Strategies that invest in neighbourhood cohesion and social infrastructure are vital for mitigating loneliness and strengthening community wellbeing. Loneliness Social isolation Social capital Public health Mental health Social cohesion Geographic disparities 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 was launched as the largest population-based investigation of loneliness and social disconnection ever conducted in the United Kingdom. The first paper in this series ( El-Osta et al., 2025a ) 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 intensity 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 the most extensive inferential analysis of loneliness risk factors to date. It applies advanced statistical modelling to quantify the independent associations between loneliness and key sociodemographic, socioeconomic and health-related characteristics across the life course. In doing so, it sheds new light on the nuanced drivers of loneliness among older people, 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 [ 4 ]. 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. This paper makes three primary contributions. First, it provides effect size estimates for a broad set of loneliness predictors, including often-overlooked 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 examines geographic disparities and urban-rural contrasts in loneliness prevalence, including the visibility of older adult loneliness in low-trust urban environments versus more cohesive rural communities. This paper supports a life-course approach to loneliness prevention and affirms the need for multigenerational public health strategies that acknowledge both the distinct and overlapping loneliness risks faced by younger and older people [ 4 , 5 ]. 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 quantify the independent associations between loneliness and key demographic, socioeconomic and social determinants. Specifically, we sought to (i) assess the prevalence of loneliness and social capital across sociodemographic and health subgroups. identify independent predictors of loneliness using both the UCLA [ 6 ] and DMOL [ 7 ] scales; and (iii) examine the social and demographic correlates of social capital to understand its protective role. Methods 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., 2025a). Participants and Sampling A total of 135,722 adults aged 16 years and older were recruited via NHS primary care networks, voluntary sector organizations and the 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 in (El-Osta et al., 2025a). 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). Additionally, a single-item DMOL recommended by the ONS was included in our study. To measure social capital, we used a seven-item Likert scale with four response categories. This scale is based on an instrument developed and validated by Sampson et al [ 8 ]. Social capital scores ranged from 0 to 7. Response categories were dichotomised such that “agree” or “strongly agree” were scored as 1, and “disagree” or “strongly disagree” as 0. Scores were then summed to produce an overall index. 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 median score in the sample data was 4. Therefore, scores ranging from 0 to 4 were classified as low social capital, while scores above 4 were classified as high. All scores for loneliness and social capital in this analysis were calculated using the classical test theory approach, by summing item responses. Rasch model-derived person measures in logits are reported separately in Paper 3 of this series ( Tristan et al., 2025c ). 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 . Statistical Analysis Separate analyses were conducted for the UCLA Loneliness Scale, DMOL and social capital score following ONS guidelines. Participant characteristics were summarized using frequencies and percentages to provide a clear overview of the sample population. To evaluate statistically significant differences, Pearson's Chi-square test was employed. This test is particularly useful for categorical data and helps identify significant associations between variables. To examine the independent effects of sociodemographic, health and social variables on loneliness and social capital, multivariable logistic regression analyses were conducted. Ordinal logistic regression was applied to model associations with loneliness, as measured by both the UCLA Loneliness Scale 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). 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 received a favourable opinion from the NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483). The Strengthening the Reporting of Observational Studies in Epidemiology [ 9 ] checklist and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) [ 10 ] were used to improve the quality of the reporting. Results 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 reported feeling lonely “often or always,” 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, reinforcing 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, underscoring potential marginalisation. 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 scores across various demographic and social characteristics is shown in Supplementary Table 3 highlighting the distribution of social capital scores dichotomised into low (≤ 4) and high (> 4) across demographic and social characteristics. The data was based on a total sample of 120,583 respondents and revealed a distinct social pattern in perceived trust, cohesion and support within neighbourhoods. Social capital, measured via a composite scale of neighbourhood trust, cohesion and support, followed an opposing distribution. High social capital was most common among older adults, married individuals and those with a large number of friends or relatives. Conversely, low social capital was concentrated among younger respondents, individuals living alone, the unemployed and those in poor health. 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 predictors of loneliness as measured by both the UCLA Loneliness Scale and DMOL and to examine factors associated with higher or lower levels of social capital. Chi-square analysis results Chi-square analysis revealed statistically significant associations between loneliness and all examined sociodemographic 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 justify 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 of logistic regression of UCLA, DMOL and Social Capital Scale. 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 4 & Fig. 1 . Age was the most consistent and potent protective factor. 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 yielded counterintuitive findings. 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). This may reflect occupational strain, urban residence or weaker neighbourhood ties among highly educated individuals. Employment status revealed stark inequalities. 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 protective benefit of 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, highlighting the protective role of intimate relationships. Social contact demonstrated a clear dose-response effect. 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), indicating strong protective effects against 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), reinforcing the intersection between chronic illness and social vulnerability. Table 1 Association between demographic and social factors and loneliness as measured by UCLA Loneliness Scale Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * Age 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) 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 0 [ 11 ] 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 [ 11 ] 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) 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 ¶ : Crude ordinal logistic regression model. † : 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. Number of observations in the adjusted model = 122,257 observations 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 5 & 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 Direct Measure of Loneliness (DMOL) Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * Age 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) 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 [ 11 ] 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 [ 11 ] 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) 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) ¶ : Crude ordinal logistic regression model. † : 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. Number of observations in the adjusted model = 122,257 Binary regression analysis: Social capital Figure 3 presents unadjusted and adjusted odds ratios for various demographic and social factors associated with Social Capital Scale. Binary logistic regression identified significant predictors of high versus low social capital. Age was the most potent factor. Compared with the 16–25 reference group, those aged 40–64 had more than twice the odds of high social capital (aOR 2.29), while those aged ≥ 65 had nearly fourfold increased odds (aOR 3.89, 95% CI: 3.59–4.22). Marital status was also strongly predictive. Married individuals had more than double the odds of reporting high social capital (aOR 2.24), while widowed individuals had moderately higher odds (aOR 1.56). Being single was negatively associated with social capital. Employment status showed a marked divide. Retired individuals were significantly more likely to report high social capital (aOR 2.61), whereas unemployment was associated with reduced odds (aOR 0.45), as was unpaid caregiving (aOR 0.71). These findings suggest that stable social roles and community integration may enhance perceived neighbourhood cohesion. Gender effects were modest: males had slightly higher odds of high social capital (aOR 1.07), while individuals identifying as ‘Other’ gender had markedly lower odds (aOR 0.45). Higher education was positively associated with social capital (aOR 1.28) and social contact again emerged as a key correlate. Respondents who made contact with 9 + friends over the last month had nearly five times the odds of high social capital (aOR 4.88). Health-related disadvantage was linked to reduced social capital. Individuals with a disability (aOR 0.59) or long-term condition (aOR 0.65) were less likely to report strong neighbourhood trust, cohesion or support, suggesting a dual burden of social disconnection and poor health. Interestingly, larger household size was associated with higher social capital, whereas pet ownership and having children showed minimal or inconsistent associations. Table 3 Association between demographic and social factors and Social Capital Scale Unadjusted OR (CI) ¶ P value Adjusted OR (CI) † P value * Age 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) 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 [ 11 ] 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 [ 11 ] 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) 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 ¶ : Crude ordinal logistic regression model. † : 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. Number of observations in the adjusted model = 117,781 Converging predictors of loneliness and social capital Across all three models (UCLA Loneliness Scale, DMOL and social capital) several predictors emerged as consistently influential. 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 functioned as a protective buffer against loneliness across both loneliness measures, 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 This study presents one of the most comprehensive population-level analyses of loneliness and its predictors in the UK, drawing on data from over 135,000 adults. By shifting from the descriptive findings presented in paper 1 (El-Osta et al., 2025a) to prediction, this second paper in the INTERACT series lays the groundwork for targeted, equity-informed and context-sensitive interventions. Using two validated tools, the UCLA 3-item Loneliness Scale and the ONS 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 connection, measured by the number of close friends or relatives, emerged as the most consistent protective factor across all models. Notably, 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 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[ 12 , 13 ]. While individual factors such as age, disability or employment status matter, they operate within broader systems of opportunity, access and belonging. Among older adults, the protective association of retirement, stable relationships and high social capital points to the buffering role of community integration and relational continuity. However, this protective effect is not uniformly distributed - older individuals who were widowed, living alone or experiencing multimorbidity faced significantly elevated loneliness risk, echoing findings from the English Longitudinal Study of Ageing[ 14 ]. This highlights the need to disaggregate the "older adult" category and address intra-cohort heterogeneity in loneliness trajectories. Our inclusion of social capital as both an outcome and a mediating factor also draws from the social determinants of health framework advocated by the WHO [ 15 , 16 ]. Neighbourhood trust, civic participation and perceptions of safety are not just background variables - they shape individuals’ capacity to build and sustain meaningful relationships across the life course. 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 [ 17 , 18 ]. However, our large sample size and stratified analysis bring 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[ 19 ]. Our data add precision by quantifying this gradient and showing that even modest increases in social ties (e.g. having more than four friends or relatives) substantially reduce loneliness risk. Our findings on education and loneliness diverge from much of the existing literature. While prior studies have linked higher education with improved social and mental health outcomes [ 20 ], we found that individuals with university degrees reported higher loneliness on the UCLA scale. This may reflect generational differences in expectations of social fulfilment or modern dynamics such as work-related stress and digitally mediated relationships[ 21 ]. In relation to older adults, our findings echo those of Victor et al. in 2005 [ 22 ], who distinguished between emotional loneliness (absence of close attachment) and social loneliness (lack of broader networks) a distinction not captured in our measures but evident in subgroup patterns. For example, older adults who were widowed but socially embedded may experience less social loneliness but elevated emotional loneliness. Explaining anomalies and counterintuitive findings Several findings warrant closer examination. The association between higher education and increased loneliness may reflect heightened expectations for social connectedness among highly educated individuals, who may feel disconnected despite large, often weakly tied, social networks. Urban residency, career mobility and performance pressures may also erode the depth of personal connections. 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. Lastly, the lack of a significant protective association between pet ownership and loneliness may suggest that companion animals do not fully substitute for human social contact or that their protective effects are context-specific[ 23 ] 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 key strength lies in its scale and scope: it is the largest UK-based analysis of loneliness and social capital to date. It includes validated measures of loneliness and offers a novel approach by modelling social capital not just as a predictor, but as an outcome in its own right. This allows for a more integrated understanding of relational and structural dimensions of social connection. We also provide age-disaggregated insights, highlighting both resilience and risk among older adults. However, the study has a number of limitations which were itemised in Paper 1 ( El-Osta et al., 2025a ). The principal limitation is related to its cross-sectional design which prevents causal inference. We also acknowledge that self-report data may introduce the potential for recall and social desirability bias and online sampling may underrepresent individuals with cognitive or digital access barriers especially relevant for some older adults. Additionally, variables such as income, ethnicity and living arrangements were not included but are likely to interact with loneliness and warrant exploration in future research. Implications for policy and future research The policy implications are twofold: first, loneliness is a multigenerational challenge, 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. In line with the WHO’s Age-Friendly Communities framework, local authorities should 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[ 24 ]. 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 drivers of loneliness. Future research should 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. The next phase of this research, presented in the third paper of this series ( Tristan et al., 2025) , 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 (DIF) 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. Collectively, the three papers provide 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, layered experience that reflects not just individual disposition, but structural and relational disadvantage. While younger adults experience the highest reported loneliness, older adults-especially those who are widowed, disabled or living alone-remain at risk and must not be overlooked in national strategies. This study highlights the central role of social capital and community cohesion in mitigating loneliness and offers a data-driven foundation for developing more inclusive, responsive and life-course-sensitive public health interventions. Declarations Consent for publication Not applicable. 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. Author Contribution All authors (AEO, AA, MA, SA, and AM) provided substantial contributions to the conception, design, acquisition (AEO) and interpretation (MA, SA, AT, AM and AEO) of study data. AEO took the lead in planning the study with support from co-authors. MA and SA carried out the data analysis with support from AT and AEO. AEO developed the manuscript with support from co-authors. AEO is the guarantor. Acknowledgement The authors are grateful to Professor Pamela Qualter for suggesting we include the Social Capital Scale in the INTERACT data collection tool. The authors also appreciate the insightful comments by Dr. John Michael Linacre regarding the one-dimensionality of the variable reported in the Winsteps® software. The authors also thank Mr Aos Alaa (INTERACT Study Coordinator), Mrs Sandra O’Sullivan, Dr Arti Sharma, the NIHR Research Delivery Networks and the NIHR Be Part of Research (BPoR) Network for their support with the recruitment of study participants. Data Availability The anonymised dataset generated and analysed during the current study is not publicly available at this stage due to ongoing recruitment, data harmonisation and cross-national extension efforts. However, the authors are committed to responsible data sharing in accordance with ethical approvals and institutional governance protocols. Upon completion of the broader INTERACT programme, a curated version of the dataset, including metadata and codebooks, will be made available to qualified researchers upon reasonable request.Researchers interested in collaborating or using the INTERACT tool in other settings are invited to contact the corresponding author. Translation protocols, technical guidance and scale validation support can be provided to facilitate international replication and comparative studies. 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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. 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. 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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-6864203","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473179145,"identity":"c7114ef2-583a-444b-b261-c4768130976d","order_by":0,"name":"Austen El-Osta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie2RwUoDMRCGJwS2l0iuU5TuK2QRBFHwVeLJa8HLHmSJFHPqA9THEF9gyoB72QeoLIgieO6xIIqJFi+S1mMP+S6TDHzM/AlAJrObCPoug2sXi4LBT9tscqKCoOZrRf5bQbu+blO0k0RQN42+fb25H9dPB3oCYrkCPkwpSIUl6BixP/f9rLtUyCCHU+Cj9FbK8IcnhKjseauAAfYB+DRllKSXJHyD5eM8KJ9WlWHK+ybFkIKgSDQLERRnw1Ao4pTkYhUXJmYZ3nUxy4NVFQt/PDUXyfijdvLyHF5Mj9r2rR9f2bPQ4cWqPqlcypF/z8Jt+ciEnslkMplfvgAtBVO5eaETNQAAAABJRU5ErkJggg==","orcid":"","institution":"Imperial College London","correspondingAuthor":true,"prefix":"","firstName":"Austen","middleName":"","lastName":"El-Osta","suffix":""},{"id":473179146,"identity":"6eadcba1-ed33-42c2-83ce-dff35bb03ead","order_by":1,"name":"Mahmoud Al-Ammouri","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Al-Ammouri","suffix":""},{"id":473179147,"identity":"c66e7bec-53e9-43f9-bd41-720b984f529f","order_by":2,"name":"Aos Alaa","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Aos","middleName":"","lastName":"Alaa","suffix":""},{"id":473179148,"identity":"99638134-d374-429d-afa0-4a088c31263e","order_by":3,"name":"Sami Altalib","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Altalib","suffix":""},{"id":473179149,"identity":"22865cc9-2d5b-464b-b091-aedee01a08d6","order_by":4,"name":"Agustin Tristán-López","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Agustin","middleName":"","lastName":"Tristán-López","suffix":""},{"id":473179150,"identity":"9874c29c-c8cd-4f31-8413-205cac188b2e","order_by":5,"name":"Azeem Majeed","email":"","orcid":"","institution":"Imperial College London","correspondingAuthor":false,"prefix":"","firstName":"Azeem","middleName":"","lastName":"Majeed","suffix":""}],"badges":[],"createdAt":"2025-06-10 14:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6864203/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6864203/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85068881,"identity":"6a712055-c96e-4b25-b6d1-6e8a626e5786","added_by":"auto","created_at":"2025-06-20 15:25:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1395189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot\u003c/strong\u003e \u003cstrong\u003eof association between demographic \u0026amp; social factors and loneliness as measured by the UCLA Loneliness Scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/2e0440f29b0c66e00385f9ac.png"},{"id":85068883,"identity":"ea01037b-4548-4a20-985b-0211693c6a8e","added_by":"auto","created_at":"2025-06-20 15:25:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1514951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of association between demographic \u0026amp; social factors and loneliness as measured by the DMOL Loneliness Scale\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/521b33fa5ea166422c710cfa.png"},{"id":85068885,"identity":"ace7baee-de4d-4a83-9cee-0be293ef7a12","added_by":"auto","created_at":"2025-06-20 15:25:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1431362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of association between demographic \u0026amp; social factors and social cohesion as measured by social capital score\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/a9ebfb77a74bc3adbe221b16.png"},{"id":106404566,"identity":"cdf3354e-d072-4fdd-ba9d-28352be5b4d0","added_by":"auto","created_at":"2026-04-08 09:16:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5303248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/f35ca3c7-ac2f-4437-b4da-94a69a6b6cfc.pdf"},{"id":85068903,"identity":"3731def8-3f8d-427c-aabb-7f99ac8e64f6","added_by":"auto","created_at":"2025-06-20 15:25:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":59140,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/75d01adf9ab1da4f2ce5cea6.docx"},{"id":85070015,"identity":"c0e0481e-4eaf-4313-a686-12c3f675cfe4","added_by":"auto","created_at":"2025-06-20 15:33:53","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":59264,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/b2b9e4e401c105d93cf08b8b.docx"},{"id":85070016,"identity":"e19fe5d8-74e5-4c10-971c-8ca76c965b00","added_by":"auto","created_at":"2025-06-20 15:33:54","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":55823,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/2e21f212dda0f9aee0e99fdf.docx"},{"id":85068891,"identity":"e29222f9-a141-4a2c-a544-2b4cd801d641","added_by":"auto","created_at":"2025-06-20 15:25:52","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":3877144,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFILE1copy.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/dd4cedcbb250c68bbdddfe28.docx"},{"id":85068886,"identity":"167205f2-195b-4527-8d6a-cda24685099b","added_by":"auto","created_at":"2025-06-20 15:25:52","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":93473,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFILE2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6864203/v1/8ddc13ad131d845416448c2b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unpacking the Predictors of 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 was launched as the largest population-based investigation of loneliness and social disconnection ever conducted in the United Kingdom. The first paper in this series (\u003cb\u003eEl-Osta et al., 2025a\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 intensity 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 the most extensive inferential analysis of loneliness risk factors to date. It applies advanced statistical modelling to quantify the independent associations between loneliness and key sociodemographic, socioeconomic and health-related characteristics across the life course. In doing so, it sheds new light on the nuanced drivers of loneliness among older people, 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=\"CR4\" class=\"CitationRef\"\u003e4\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\u003eThis paper makes three primary contributions. First, it provides effect size estimates for a broad set of loneliness predictors, including often-overlooked 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 examines geographic disparities and urban-rural contrasts in loneliness prevalence, including the visibility of older adult loneliness in low-trust urban environments versus more cohesive rural communities. This paper supports a life-course approach to loneliness prevention and affirms the need for multigenerational public health strategies that acknowledge both the distinct and overlapping loneliness risks faced by younger and older people [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\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\u003ch2\u003eStudy aims\u003c/h2\u003e\u003cp\u003eThe primary aim of this study was to quantify the independent associations between loneliness and key demographic, socioeconomic and social determinants. Specifically, we sought to (i) assess the prevalence of loneliness and social capital across sociodemographic and health subgroups. identify independent predictors of loneliness using both the UCLA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and DMOL [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] scales; and (iii) examine the social and demographic correlates of social capital to understand its protective role.\u003c/p\u003e"},{"header":"Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \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., 2025a).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Sampling\u003c/h3\u003e\n\u003cp\u003eA total of 135,722 adults aged 16 years and older were recruited via NHS primary care networks, voluntary sector organizations and the 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 in \u003cb\u003e(El-Osta et al., 2025a).\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 = 3), moderate loneliness (score = 4–6) and severe loneliness (score = 7–9). Additionally, a single-item DMOL recommended by the ONS was included in our study.\u003c/p\u003e \u003cp\u003eTo measure social capital, we used a seven-item Likert scale with four response categories. This scale is based on an instrument developed and validated by Sampson et al [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Social capital scores ranged from 0 to 7. Response categories were dichotomised such that “agree” or “strongly agree” were scored as 1, and “disagree” or “strongly disagree” as 0. Scores were then summed to produce an overall index. 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 median score in the sample data was 4. Therefore, scores ranging from 0 to 4 were classified as low social capital, while scores above 4 were classified as high. All scores for loneliness and social capital in this analysis were calculated using the classical test theory approach, by summing item responses. Rasch model-derived person measures in logits are reported separately in Paper 3 of this series (\u003cb\u003eTristan et al., 2025c\u003c/b\u003e).\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’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 ‘\u003cem\u003emice\u003c/em\u003e’ 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 = 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 \u003cb\u003eSupplementary file 1\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSeparate analyses were conducted for the UCLA Loneliness Scale, DMOL and social capital score following ONS guidelines. Participant characteristics were summarized using frequencies and percentages to provide a clear overview of the sample population. To evaluate statistically significant differences, Pearson's Chi-square test was employed. This test is particularly useful for categorical data and helps identify significant associations between variables.\u003c/p\u003e \u003cp\u003eTo examine the independent effects of sociodemographic, health and social variables on loneliness and social capital, multivariable logistic regression analyses were conducted. Ordinal logistic regression was applied to model associations with loneliness, as measured by both the UCLA Loneliness Scale 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). 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 received a favourable opinion from 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=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] checklist and the Checklist for Reporting Results of Internet E-Surveys (CHERRIES) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were used to improve the quality of the reporting.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive findings\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003ePrevalence and distribution of loneliness and social capital\u003c/h2\u003e \u003cp\u003eAmong 135,725 respondents, loneliness and social capital were unequally distributed across demographic, social and health-related groups. \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\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 reported feeling lonely \u0026ldquo;often or always,\u0026rdquo; 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, reinforcing an inverse age-loneliness gradient.\u003c/p\u003e \u003cp\u003ePatterns of loneliness measured using DMOL closely mirrored those observed with the UCLA scale but offered additional granularity. \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\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, underscoring potential marginalisation. 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 \u003cp\u003eThe prevalence of low and high social capital scores across various demographic and social characteristics is shown in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e highlighting the distribution of social capital scores dichotomised into low (\u0026le;\u0026thinsp;4) and high (\u0026gt;\u0026thinsp;4) across demographic and social characteristics. The data was based on a total sample of 120,583 respondents and revealed a distinct social pattern in perceived trust, cohesion and support within neighbourhoods. Social capital, measured via a composite scale of neighbourhood trust, cohesion and support, followed an opposing distribution. High social capital was most common among older adults, married individuals and those with a large number of friends or relatives. Conversely, low social capital was concentrated among younger respondents, individuals living alone, the unemployed and those in poor health.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInferential statistics findings\u003c/h2\u003e \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 predictors of loneliness as measured by both the UCLA Loneliness Scale and DMOL and to examine factors associated with higher or lower levels of social capital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eChi-square analysis results\u003c/h2\u003e \u003cp\u003eChi-square analysis revealed statistically significant associations between loneliness and all examined sociodemographic 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 \u003cb\u003eSupplementary File 2\u003c/b\u003e. These findings justify 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 \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression findings\u003c/h2\u003e \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 of logistic regression of UCLA, DMOL and Social Capital Scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eOrdinal regression analysis: UCLA Loneliness Scale\u003c/h2\u003e \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 \u003cb\u003eTable\u0026nbsp;4 \u0026amp;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Age was the most consistent and potent protective factor. 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 'Other' 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 \u003cp\u003eEducational attainment yielded counterintuitive findings. 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). This may reflect occupational strain, urban residence or weaker neighbourhood ties among highly educated individuals. Employment status revealed stark inequalities. 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 protective benefit of later-life stability.\u003c/p\u003e \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, highlighting the protective role of intimate relationships. Social contact demonstrated a clear dose-response effect. 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), indicating strong protective effects against loneliness. Participants reporting no social contacts had the highest loneliness burden.\u003c/p\u003e \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), reinforcing the intersection between chronic illness and social vulnerability.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between demographic and social factors and loneliness as measured by UCLA Loneliness Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR (CI) \u0026para;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (CI) \u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value *\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.67, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.60, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59 (0.56, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.45, 0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43 (0.41, 0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.30, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26 (0.25, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22 (0.20, 0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.15, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14 (0.13, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.77, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.79, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37 (2.93, 3.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.98, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98 (1.68, 2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (0.85, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA levels/College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.07, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.77, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.19, 1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed full-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79 (0.77, 0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.03, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.35 (3.13, 3.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.68, 1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.41, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.91, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (0.65, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.04, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.28 (2.14, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25 (1.15, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.71 (4.47, 4.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82 (1.71, 1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpaid carer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64 (2.39, 2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.24 (1.99, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteer (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.54, 0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.98, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79 (1.70, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.15, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBritish Black/African/Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.74 (1.61, 1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.99, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed/Multiple ethnic groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.70, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.96, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60 (1.49, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.97, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite and Black Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33 (1.96, 2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.93, 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.59, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.00, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.39, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.48, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried / Civil partnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 (0.17, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.42, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.70, 0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97 (0.89, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.43, 0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.27, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of relatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.87, 0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.95, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.68, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.80, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.35, 0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57 (0.54, 0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16 (0.16, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39 (0.37, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.08, 0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (0.26, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of friends\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 friend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.80 (0.76, 0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.76, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.45, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52 (0.49, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.22 (0.21, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29 (0.28, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.10, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.15, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.05, 0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.09, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving pet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.22, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.02, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of household members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.36, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.70, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.62 (0.60, 0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.65, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.68, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.66 (0.62, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.08 (0.98, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69 (0.61, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.47, 0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.03, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving disability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.51 (3.42, 3.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.71, 1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.49 (3.28, 3.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59 (1.47, 1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving long-term condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.76 (2.70, 2.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.60, 1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05 (2.85, 3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.55, 1.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026para;\u003c/b\u003e: Crude ordinal logistic regression model.\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026dagger;\u003c/b\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 \u003cp\u003e\u003cb\u003e*\u003c/b\u003e: Significance level, with values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eNumber of observations in the adjusted model\u0026thinsp;=\u0026thinsp;122,257 observations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eOrdinal regression analysis: Direct Measure of Loneliness (DMOL)\u003c/h2\u003e \u003cp\u003eThe analysis of unadjusted and adjusted odds ratios for various demographic and social factors associated with DMOL is shown in \u003cb\u003eTable\u0026nbsp;5 \u0026amp;\u003c/b\u003e Fig.\u0026nbsp;\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 \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 \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 \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between demographic and social factors and loneliness as measured by Direct Measure of Loneliness (DMOL)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR (CI) \u0026para;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (CI) \u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value *\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76 (0.72, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (0.68, 0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.64 (0.61, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.54 (0.52, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.46, 0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38 (0.36, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.29, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27 (0.25, 0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.19, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19 (0.18, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.73 (0.72, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74 (0.72, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.42 (2.12, 2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.80, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73 (1.46, 2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.66, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA levels/College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.91, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.93, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70 (0.68, 0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.93, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed full-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.84, 0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.04, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96 (2.78, 3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.50, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.47, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.92, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.68, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.02, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.02 (1.89, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.99, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.15 (3.96, 4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.59, 1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpaid carer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.18 (1.98, 2.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.68 (1.51, 1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteer (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.57, 0.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.92, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.88 (1.78, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41 (1.32, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBritish Black/African/Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83 (1.70, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.13, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed/Multiple ethnic groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 (1.58, 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.97, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73 (1.60, 1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.14, 1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite and Black Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24 (1.88, 2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (0.93, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.63, 0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.95, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.37, 0.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.45, 0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried / Civil partnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20 (0.19, 0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44 (0.42, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.76, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.93, 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 (0.55, 0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.41, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of relatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.83, 0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94 (0.89, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.71 (0.67, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.77, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40 (0.38, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60 (0.57, 0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (0.18, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.39, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.10, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30 (0.28, 0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of friends\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 friend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.78, 0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.79, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.50, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61 (0.59, 0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29 (0.27, 0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41 (0.39, 0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.15 (0.15, 0.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26 (0.25, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.08, 0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16 (0.15, 0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving pet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.13, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.97, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of household members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (0.36, 0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.74, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.60 (0.59, 0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72 (0.69, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.69, 0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.70, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.01, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.75, 0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving children\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.55, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.15, 1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving disability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.89 (2.81, 2.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44 (1.39, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98 (2.78, 3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.24, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving long-term condition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.43 (2.38, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.49, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.73 (2.55, 2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53 (1.42, 1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026para;\u003c/b\u003e: Crude ordinal logistic regression model.\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026dagger;\u003c/b\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 \u003cp\u003e\u003cb\u003e*\u003c/b\u003e: Significance level, with values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eNumber of observations in the adjusted model\u0026thinsp;=\u0026thinsp;122,257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBinary regression analysis: Social capital\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents unadjusted and adjusted odds ratios for various demographic and social factors associated with Social Capital Scale. Binary logistic regression identified significant predictors of high versus low social capital. Age was the most potent factor. Compared with the 16\u0026ndash;25 reference group, those aged 40\u0026ndash;64 had more than twice the odds of high social capital (aOR 2.29), while those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 had nearly fourfold increased odds (aOR 3.89, 95% CI: 3.59\u0026ndash;4.22). Marital status was also strongly predictive. Married individuals had more than double the odds of reporting high social capital (aOR 2.24), while widowed individuals had moderately higher odds (aOR 1.56). Being single was negatively associated with social capital.\u003c/p\u003e \u003cp\u003eEmployment status showed a marked divide. Retired individuals were significantly more likely to report high social capital (aOR 2.61), whereas unemployment was associated with reduced odds (aOR 0.45), as was unpaid caregiving (aOR 0.71). These findings suggest that stable social roles and community integration may enhance perceived neighbourhood cohesion.\u003c/p\u003e \u003cp\u003eGender effects were modest: males had slightly higher odds of high social capital (aOR 1.07), while individuals identifying as \u0026lsquo;Other\u0026rsquo; gender had markedly lower odds (aOR 0.45). Higher education was positively associated with social capital (aOR 1.28) and social contact again emerged as a key correlate. Respondents who made contact with 9\u0026thinsp;+\u0026thinsp;friends over the last month had nearly five times the odds of high social capital (aOR 4.88).\u003c/p\u003e \u003cp\u003eHealth-related disadvantage was linked to reduced social capital. Individuals with a disability (aOR 0.59) or long-term condition (aOR 0.65) were less likely to report strong neighbourhood trust, cohesion or support, suggesting a dual burden of social disconnection and poor health. Interestingly, larger household size was associated with higher social capital, whereas pet ownership and having children showed minimal or inconsistent associations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between demographic and social factors and Social Capital Scale\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted OR (CI) \u0026para;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted OR (CI) \u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value *\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19 (1.11, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.06, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79 (1.68, 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.66, 1.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.38 (2.25, 2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38 (2.2, 2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.33 (3.14, 3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.72 (2.51, 2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37 (5.08, 5.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.21 (2.93, 3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.02, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04 (1.01, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 (0.26, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.63, 0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.45, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (0.89, 1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA levels/College\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.05, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.1, 1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity Degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (1.26, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.11, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed full-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed part-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.38, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19 (1.14, 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolunteer (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.93 (1.73, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28 (1.13, 1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54 (2.47, 2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.42, 1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.63, 1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26 (1.19, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent (full or part-time)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.49, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9 (0.82, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42 (0.39, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.86, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnpaid carer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.67 (0.6, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86 (0.76, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65 (0.61, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBritish Black/African/Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.29, 0.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.43, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed/Multiple ethnic groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.46, 0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8 (0.72, 0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite and Black Caribbean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.44 (0.36, 0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.61, 0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian/Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.51, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.76, 0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51 (0.47, 0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67 (0.61, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65 (1.58, 1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.97, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.54 (1.47, 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (1.16, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried / Civil partnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.31 (3.21, 3.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 (1.53, 1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (3.05, 3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.31, 1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26 (1.17, 1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.89, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of relatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 relative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25 (1.17, 1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (1.1, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.46, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.39 (1.31, 1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.32 (2.2, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69 (1.59, 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.97 (3.75, 4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1 (1.96, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.61 (5.27, 5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34 (2.17, 2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of friends\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 friend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.28, 1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.26, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99 (1.89, 2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.79 (1.69, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 or 4 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.36 (3.2, 3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.65 (2.52, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8 friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.69 (5.41, 5.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.88 (3.67, 4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 or more friends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.51 (8.08, 8.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.22 (4.92, 5.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaving pet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1, 1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24 (1.21, 1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of household members\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59 (1.54, 1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1, 1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.07, 1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.08, 1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.88, 0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09 (1.02, 1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6 (0.54, 0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.86, 1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChildren\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85 (1.8, 1.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.9, 0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.48, 0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.74, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.39 (0.36, 0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.68 (0.62, 0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLongterm conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.56 (0.55, 0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.8, 0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWould rather not say\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.41, 0.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75 (0.69, 0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e\u0026para;\u003c/b\u003e: Crude ordinal logistic regression model.\u003c/p\u003e \u003cp\u003e\u003cb\u003e\u0026dagger;\u003c/b\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 \u003cp\u003e\u003cb\u003e*\u003c/b\u003e: Significance level, with values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eNumber of observations in the adjusted model\u0026thinsp;=\u0026thinsp;117,781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eConverging predictors of loneliness and social capital\u003c/h2\u003e \u003cp\u003eAcross all three models (UCLA Loneliness Scale, DMOL and social capital) several predictors emerged as consistently influential. 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 \u003cp\u003eImportantly, higher social capital itself functioned as a protective buffer against loneliness across both loneliness measures, 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 \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSummary of principal findings\u003c/h2\u003e \u003cp\u003eThis study presents one of the most comprehensive population-level analyses of loneliness and its predictors in the UK, drawing on data from over 135,000 adults. By shifting from the descriptive findings presented in paper 1 \u003cb\u003e(El-Osta et al., 2025a)\u003c/b\u003e to prediction, this second paper in the INTERACT series lays the groundwork for targeted, equity-informed and context-sensitive interventions.\u003c/p\u003e \u003cp\u003eUsing two validated tools, the UCLA 3-item Loneliness Scale and the ONS 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.\u003c/p\u003e \u003cp\u003eSocial connection, measured by the number of close friends or relatives, emerged as the most consistent protective factor across all models. Notably, 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.\u003c/p\u003e \u003cp\u003eOur 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=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While individual factors such as age, disability or employment status matter, they operate within broader systems of opportunity, access and belonging.\u003c/p\u003e \u003cp\u003eAmong older adults, the protective association of retirement, stable relationships and high social capital points to the buffering role of community integration and relational continuity. However, this protective effect is not uniformly distributed - 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=\"CR14\" class=\"CitationRef\"\u003e14\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\u003eOur inclusion of social capital as both an outcome and a mediating factor also draws from the social determinants of health framework advocated by the WHO [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Neighbourhood trust, civic participation and perceptions of safety are not just background variables - they shape individuals\u0026rsquo; capacity to build and sustain meaningful relationships across the life course.\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=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, our large sample size and stratified analysis bring 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=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Our data add precision by quantifying this gradient and showing that even modest increases in social ties (e.g. having more than four friends or relatives) substantially reduce loneliness risk.\u003c/p\u003e \u003cp\u003eOur findings on education and loneliness diverge from much of the existing literature. While prior studies have linked higher education with improved social and mental health outcomes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we found that individuals with university degrees reported higher loneliness on the UCLA scale. This may reflect generational differences in expectations of social fulfilment or modern dynamics such as work-related stress and digitally mediated relationships[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn relation to older adults, our findings echo those of Victor et al. in 2005 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], who distinguished between emotional loneliness (absence of close attachment) and social loneliness (lack of broader networks) a distinction not captured in our measures but evident in subgroup patterns. For example, older adults who were widowed but socially embedded may experience less social loneliness but elevated emotional loneliness.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eExplaining anomalies and counterintuitive findings\u003c/h2\u003e \u003cp\u003eSeveral findings warrant closer examination. The association between higher education and increased loneliness may reflect heightened expectations for social connectedness among highly educated individuals, who may feel disconnected despite large, often weakly tied, social networks. Urban residency, career mobility and performance pressures may also erode the depth of personal connections.\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.\u003c/p\u003e \u003cp\u003eLastly, the lack of a significant protective association between pet ownership and loneliness may suggest that companion animals do not fully substitute for human social contact or that their protective effects are context-specific[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] 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 \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s key strength lies in its scale and scope: it is the largest UK-based analysis of loneliness and social capital to date. It includes validated measures of loneliness and offers a novel approach by modelling social capital not just as a predictor, but as an outcome in its own right. This allows for a more integrated understanding of relational and structural dimensions of social connection.\u003c/p\u003e \u003cp\u003eWe also provide age-disaggregated insights, highlighting both resilience and risk among older adults. However, the study has a number of limitations which were itemised in Paper 1 (\u003cb\u003eEl-Osta et al., 2025a\u003c/b\u003e). The principal limitation is related to its cross-sectional design which prevents causal inference. We also acknowledge that self-report data may introduce the potential for recall and social desirability bias and online sampling may underrepresent individuals with cognitive or digital access barriers especially relevant for some older adults. Additionally, variables such as income, ethnicity and living arrangements were not included but are likely to interact with loneliness and warrant exploration in future research.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eImplications for policy and future research\u003c/h2\u003e \u003cp\u003eThe policy implications are twofold: first, loneliness is a multigenerational challenge, 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.\u003c/p\u003e \u003cp\u003eIn line with the WHO\u0026rsquo;s Age-Friendly Communities framework, local authorities should 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=\"CR24\" class=\"CitationRef\"\u003e24\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 drivers of loneliness.\u003c/p\u003e \u003cp\u003eFuture research should 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.\u003c/p\u003e \u003cp\u003eThe next phase of this research, presented in the third paper of this series (\u003cb\u003eTristan et al., 2025)\u003c/b\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 (DIF) 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. Collectively, the three papers provide 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 \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eLoneliness is a complex, layered experience that reflects not just individual disposition, but structural and relational disadvantage. While younger adults experience the highest reported loneliness, older adults-especially those who are widowed, disabled or living alone-remain at risk and must not be overlooked in national strategies. This study highlights the central role of social capital and community cohesion in mitigating loneliness and offers a data-driven foundation for developing more inclusive, responsive and life-course-sensitive public health interventions.\u003c/p\u003e"},{"header":"Declarations","content":" \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \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\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors (AEO, AA, MA, SA, and AM) provided substantial contributions to the conception, design, acquisition (AEO) and interpretation (MA, SA, AT, AM and AEO) of study data. AEO took the lead in planning the study with support from co-authors. MA and SA carried out the data analysis with support from AT and AEO. AEO developed the manuscript with support from co-authors. AEO is the guarantor.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are grateful to Professor Pamela Qualter for suggesting we include the Social Capital Scale in the INTERACT data collection tool. The authors also appreciate the insightful comments by Dr. John Michael Linacre regarding the one-dimensionality of the variable reported in the Winsteps\u0026reg; software. The authors also thank Mr Aos Alaa (INTERACT Study Coordinator), Mrs Sandra O\u0026rsquo;Sullivan, Dr Arti Sharma, the NIHR Research Delivery Networks and the NIHR Be Part of Research (BPoR) Network for their support with the recruitment of study participants.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe anonymised dataset generated and analysed during the current study is not publicly available at this stage due to ongoing recruitment, data harmonisation and cross-national extension efforts. However, the authors are committed to responsible data sharing in accordance with ethical approvals and institutional governance protocols. Upon completion of the broader INTERACT programme, a curated version of the dataset, including metadata and codebooks, will be made available to qualified researchers upon reasonable request.Researchers interested in collaborating or using the INTERACT tool in other settings are invited to contact the corresponding author. Translation protocols, technical guidance and scale validation support can be provided to facilitate international replication and comparative studies. Future updates will be posted on the SCARU project website: https://www.imperial.ac.uk/school-public-health/primary-care-and-public-health/research/scaru/\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. Graif, \u003cem\u003eNeighborhood Social Capital as Differential Social Organization: Resident and Leadership Dimensions\u003c/em\u003e. American Behavioral Scientist, 2009. 52(11): p. 1579\u0026ndash;1605.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVictor, C.R., et al., \u003cem\u003eOlder adults' experiences of loneliness over the lifecourse: An exploratory study using the BBC loneliness experiment\u003c/em\u003e. Arch Gerontol Geriatr, 2022. 102: p. 104740.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah, H.A. and M. Househ, \u003cem\u003eUnderstanding loneliness in younger people: Review of the opportunities and challenges for loneliness interventions\u003c/em\u003e. Interactive Journal of Medical Research, 2023. 12(1): p. e45197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRussell, D.W., \u003cem\u003eUCLA Loneliness Scale (Version 3): Reliability, Validity, and Factor Structure\u003c/em\u003e. Journal of Personality Assessment, 1996. 66(1): p. 20\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnape, D. and G. Martin, \u003cem\u003eMeasuring loneliness: guidance for use of the national indicators on surveys\u003c/em\u003e. Office for National Statistics, 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSampson, R.J., S.W. Raudenbush, and F. Earls, \u003cem\u003eNeighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy\u003c/em\u003e. Science, 1997. 277(5328): p. 918\u0026ndash;924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSTROBE. \u003cem\u003eSTROBE Check List\u003c/em\u003e. 2024; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.strobe-statement.org/checklists/\u003c/span\u003e\u003cspan address=\"https://www.strobe-statement.org/checklists/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEysenbach, G., \u003cem\u003eImproving the quality of Web surveys: the Checklist for Reporting Results of Internet E-Surveys (CHERRIES)\u003c/em\u003e. Journal of medical Internet research, 2004. 6(3): p. e34-e34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaustino, B., et al., \u003cem\u003ePsychometric and rash analysis of the UCLA Loneliness Scale-16 in a Portuguese sample of older adults\u003c/em\u003e. Psychological Studies, 2019. 64: p. 140\u0026ndash;146.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardcastle, B., et al., \u003cem\u003eThe Ecology of Human Development\u0026mdash;Experiments by Nature and Design by Urie Bronfenbrenner. Cambridge, Ma.: Harvard University Press\u003c/em\u003e, 1979. \u003cem\u003e330 pp. $16.50\u003c/em\u003e. 1981, Taylor \u0026amp; Francis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcLeroy, K.R., et al., \u003cem\u003eAn ecological perspective on health promotion programs\u003c/em\u003e. Health Educ Q, 1988. 15(4): p. 351\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVictor, C.R. and K. Yang, \u003cem\u003eThe prevalence of loneliness among adults: a case study of the United Kingdom\u003c/em\u003e. J Psychol, 2012. 146(1\u0026ndash;2): p. 85\u0026ndash;104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Academies of Sciences, E. and Medicine, \u003cem\u003eFrameworks for Addressing the Social Determinants of Health\u003c/em\u003e, in \u003cem\u003eA Framework for Educating Health Professionals to Address the Social Determinants of Health\u003c/em\u003e. 2016, National Academies Press (US).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization, W.H., \u003cem\u003eA conceptual framework for action on the social determinants of health\u003c/em\u003e, in \u003cem\u003eA conceptual framework for action on the social determinants of health\u003c/em\u003e. 2010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePyle, E. and D. Evans, \u003cem\u003eLoneliness-what characteristics and circumstances are associated with feeling lonely\u003c/em\u003e. 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\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\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\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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Loneliness, Social isolation, Social capital, Public health, Mental health, Social cohesion, Geographic disparities, Community interventions","lastPublishedDoi":"10.21203/rs.3.rs-6864203/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6864203/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-the largest UK-based investigation of loneliness-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 135,722 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 predictors of 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 0.65 and 0.59 respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study highlights consistent sociodemographic and social predictors of loneliness, as well as the protective role of social capital. Findings support the need for targeted public health interventions that address social connection among young adults, single people, the unemployed and individuals in poor health. Strategies that invest in neighbourhood cohesion and social infrastructure are vital for mitigating loneliness and strengthening community wellbeing.\u003c/p\u003e","manuscriptTitle":"Unpacking the Predictors of Loneliness: An Inferential Analysis from the INTERACT Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-20 15:25:47","doi":"10.21203/rs.3.rs-6864203/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c21953ee-b873-41ab-99d8-e037407d5342","owner":[],"postedDate":"June 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T07:13:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-20 15:25:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6864203","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6864203","identity":"rs-6864203","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}