{"paper_id":"689be12e-6fd9-4f24-b577-9e610a685e26","body_text":"Measuring Loneliness at an Unprecedented Scale: The INTERACT Study’s Approach and Initial Findings | 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 Measuring Loneliness at an Unprecedented Scale: The INTERACT Study’s Approach and Initial Findings Austen El-Osta, Aos Alaa, Mahmoud Al-Ammouri, Sami Altalib, Agustin Tristán-López, and 1 more This is a preprint; it has not been peer reviewed by a journal.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-6864072/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Loneliness is increasingly recognised as a major public health challenge with significant implications for mental, physical and social wellbeing. Despite growing interest, population-level data remains limited, particularly at the intersection of individual, community and geographic determinants. The Measuring Loneliness in the UK (INTERACT) Study was designed to map the prevalence, intensity and sociodemographic determinants of loneliness across diverse population groups in the UK. The aim of this first paper in a series is to describe the development, implementation and early findings of the INTERACT Survey, which to date is the largest population-based study of loneliness, social isolation and social capital conducted in the UK. Methods Between March and July 2023, 135,725 adults completed the online INTERACT Survey. The instrument included validated measures of loneliness (UCLA-3 & ONS Direct Measure), social capital indicators and demographic variables. Descriptive statistics were stratified by key subgroups. A novel geospatial analysis at Lower Super Output Area (LSOA) level was used to visualise clustering of loneliness across the UK. Results Loneliness was widespread, with 16.5% of participants reporting they often or always felt lonely. Younger adults, individuals from minority ethnic backgrounds, those who were single or unemployed and people with disabilities were more likely to report frequent loneliness. Social capital varied widely, with lower scores in urban areas and among groups with greater reported loneliness. The COVID-19 pandemic was reported as an amplifying factor, with 44% of respondents indicating increased loneliness during the pandemic. Geospatial mapping revealed distinct loneliness “hotspots” in densely populated urban regions, particularly London, Birmingham and Manchester. Conclusions The INTERACT Study provides a comprehensive national dataset on loneliness and social disconnection in the UK. Its scale, methodological rigour and spatial granularity offer valuable insight for designing targeted, place-based interventions. Future papers will present inferential analyses, explore lived experience and propose policy-relevant solutions to mitigate loneliness and promote social connection across communities. Loneliness Social isolation Social connection Public health Mental health Social capital Community health COVID-19 Figures Figure 1 Background Loneliness is commonly defined as a subjective, distressing experience resulting from a discrepancy between desired and actual social connections [ 1 , 2 ]. It is distinct from social isolation, which refers to an objective lack of social contact [ 3 ]. Loneliness and social isolation are nrecognised as major global public health challenges, with substantial implications for mental, physical and social wellbeing. Extensive research demonstrated strong associations between loneliness and adverse health outcomes, including depression, anxiety, cognitive decline, cardiovascular disease, hypertension and increased mortality risk [ 1 , 2 , 4 – 6 ]. Recognising the urgency of the issue, the World Health Organisation (WHO) has identified social isolation and loneliness as critical determinants of health, highlighting the need for evidence-based interventions and policy responses [ 7 , 8 ]. Beyond individual wellbeing, the economic burden of loneliness is equally profound, contributing to higher healthcare utilisation, lost workplace productivity and increased demand for social care services. In the United Kingdom (UK), loneliness costs employers up to £3.7 billion annually or at least £9,976 per person per year [ 9 , 10 ]. Contrary to popular belief, loneliness does not solely affect older adults. Recent research suggests that loneliness follows a U-shaped distribution, with peaks in adolescence and late adulthood[ 11 ]. Young adults (16–25 years) consistently report high levels of loneliness[ 12 ], possibly due to academic stress, employment insecurity and increased digital socialisation, which may not always translate into meaningful social bonds [ 13 , 14 ]. Meanwhile, in older populations, loneliness is frequently associated with bereavement, declining health and reduced social participation [ 15 ]. Geographic disparities in loneliness have also been widely reported, with urban environments exhibiting higher loneliness prevalence despite greater population density [ 16 , 17 ]. The \"urban paradox\" suggests that city life, while offering proximity to social and economic opportunities, may promote weaker community ties, higher mobility and lower neighbourhood trust, factors contributing to greater loneliness[ 18 ]. By contrast, rural communities, despite geographic isolation, often benefit from stronger intergenerational networks and social cohesion[ 19 , 20 ]. Ethnicity and cultural background also play a significant role in loneliness. Studies indicate that ethnic minority groups experience higher loneliness levels due to structural inequalities, language barriers, discrimination and weaker social integration[ 21 ]. The COVID-19 pandemic also intensified loneliness and social isolation globally, with prolonged lockdowns, social distancing measures and economic disruptions contributing to worsening mental health outcomes [ 22 – 25 ]. Loneliness is a multidimensional construct influenced by individual, relational and structural factors, including personality traits, life transitions, socioeconomic conditions and community-level social capital [ 26 ]. The measurement of loneliness remains a significant methodological challenge. While self-reported scales such as the UCLA Loneliness Scale [ 27 ] and the Office for National Statistics (ONS) Direct Measure of Loneliness (DMOL) [ 28 ]are widely used, they often fail to capture neighbourhood-level social capital, community cohesion and trust[ 26 ], all of which play a crucial role in shaping social connectedness [ 29 ]. In the UK, loneliness was recognised as a national priority with the government launching the Loneliness Strategy in 2018, the first of its kind globally, to address the growing crisis [ 8 ]. However, despite this early leadership, large-scale, population-based studies of loneliness in the UK remain limited[ 30 – 32 ]. Much of the existing literature has focused on older adults or small, localised cohorts, with less attention paid to younger populations or the broader social and structural determinants of loneliness [ 33 , 34 ]. The Measuring Loneliness in the UK (INTERACT) Study was developed to address these critical gaps. It represents the largest population-level investigation of loneliness to date, not only within the UK but globally. By incorporating a multidimensional assessment of loneliness alongside measures of social capital, health status, digital connectivity and socioeconomic factors, INTERACT provides one of the most comprehensive post-pandemic assessments of social disconnection in the population. This is particularly important in the context of COVID-19, which exacerbated isolation and disrupted social networks in unprecedented ways. The INTERACT tool is a composite survey instrument developed to assess loneliness, social connection and related determinants at scale. It integrates validated items from established measures such as the UCLA Loneliness Scale and DMOL, alongside questions on digital connectivity, health status, social capital and demographic characteristics. The INTERACT tool was designed for large-scale deployment to help capture the intensity and dimensions of loneliness, allowing for detailed subgroup analyses. Its multidimensional structure enables a comprehensive evaluation of social disconnection and provides a robust platform for geospatial and longitudinal analysis across diverse UK populations. By transforming individual-level data into regional population-level patterns, geospatial heat maps can help visualise geographic disparities in loneliness across participating regions to help identify loneliness “hotspots” where targeted interventions may be most urgently needed. The primary objective of the INTERACT study is to map the prevalence and intensity of Loneliness across diverse population groups in the UK. Specifically, the study aimed to explore how loneliness varies across sociodemographic factors such as age, gender, ethnicity, marital status and employment. This paper is the first in a series of three manuscripts ( El-Osta et al., 2025b and Agustin et al., 2025) to report the initial findings of the INTERACT study and is intended primarily to provide a clear overview of the sample population. Methods Study Design The INTERACT study is a large-scale, cross-sectional, observational study designed to assess the prevalence, sociodemographic patterns and geographic distribution of loneliness and social isolation across the UK. The study employed a multi-methods quantitative approach, combining quantitative survey data with geospatial analysis to provide a comprehensive evaluation of loneliness and social connectedness at a population level. Only the quantitative study is conciered here. Study Setting and Recruitment The study was initially launched in England and collected data between 1 October 2021 and 3 June 2024. Participants were recruited via NHS Primary Care Networks (PCNs) where invitations were sent via general practices and NHS Trusts using digital outreach methods, including emails and SMS messages. Voluntary and Community Sector Organisations facilitated recruitment through partnerships with local charities, community groups and national organisations focused on mental health and social wellbeing. The NIHR Be Part of Research (BPoR) Network advertised through research engagement platforms to enhance participation across diverse population subgroups. Targeted recruitment was conducted via Twitter (@LonelinessStudy, @ImperialSCARU), LinkedIn and institutional websites to reach younger adults and digitally engaged populations. To maximise sample representativeness, stratified recruitment strategies were employed, ensuring balanced participation across age groups, ethnic backgrounds and socioeconomic status. Potentially eligible participants who received a link with an invitation to participate could learn more about the study by reading the participant information sheet (PIS) which could be accessed on the survey introduction page. Inclusion and Exclusion Criteria Participants were eligible to take part if they were 16 years or older, resided in England and were either registered as NHS patients or affiliated with NHS Trusts. The survey was open and participation was voluntary to participants. Inclusion also required participants to have access to an internet-enabled device (such as a smartphone, tablet or computer) to complete the online survey and to provide informed electronic consent before participation. Given the study's focus on general population-based loneliness assessments, recruitment targeted a diverse range of individuals, including those from different age groups, ethnic backgrounds and socioeconomic statuses. Exclusion criteria included individuals with severe cognitive impairments (e.g., dementia, schizophrenia or psychosis) that could compromise their ability to provide informed consent. Additionally, individuals receiving end-of-life care or experiencing severe mental health conditions requiring institutionalisation were excluded from the study. Participants who refused or were unable to provide informed consent were also ineligible. Given that the survey was conducted entirely online, individuals without internet access or digital literacy skills may have been inadvertently excluded, a limitation acknowledged in the study’s discussion. Survey Development and Measures The INTERACT Scale was developed as a composite of various existing items, including validated loneliness measures, social capital indicators and community trust scales ( Table 1 ). The final survey instrument included 27 items over two pages, comprising 14 socio-demographic questions and 13 validated scale items to assess loneliness and social connectedness. To promote inclusivity, the survey was translated into 11 languages (the most commonly used across the UK). Respondents were able to review their responses before submission. The survey is included in Supplementary File 1 and can be accessed by following this link: https://imperial.eu.qualtrics.com/jfe/form/SV_2uIyxcCD7JYbKyq. The survey underwent pilot testing with 38 participants, leading to minor revisions for clarity and accessibility. The survey structure is shown in Table 1 . Table 1: Survey Structure Survey block Description Study introduction, link to PIS & Informed Consent Participants provided digital consent before proceeding. Age, Gender & Postcode Age, gender, full postcode Loneliness Measures UCLA Loneliness Scale, ONS Direct Measure of Loneliness (DMOL) Social Connection Frequency of contact with family and friends. Social Capital Indicators Neighbourhood trust, community cohesion and perceived social support. COVID Covid specific questions Demographics Ethnicity, marital status, number of children, pet ownership, employment, disability and household size. Consent to contact Respondents could leave their contact details should they wish to volunteer to partake in a personal interview. Loneliness Assessment Measurement development and evaluation procedures were grounded in internationally recognised frameworks to ensure objectivity, validity and reliability, including the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) criteria for health-related instruments [35] and the psychometric guidelines outlined in the American Educational Research Associations, American Psychological Association and National Council on Measurement in Education (AERA-APA-NCME) Standards for Educational and Psychological Testing [36]. Loneliness was measured using two validated instruments. The UCLA Loneliness Scale (3-item version) [27] with the following three questions: “How often do you feel that you lack companionship?”, “How often do you feel left out?” and, “How often do you feel isolated from others?”. Response options for this scale were: 1 (Hardly ever), 2 (Some of the time), and 3 (Often). Total scores range from 3 to 9, with higher scores indicating greater loneliness. The ONS Direct Measure of Loneliness (DMOL) [28]“How often do you feel lonely?” question allowed response options: 1 (Never), 2 (Hardly ever), 3 (Occasionally), 4 (Some of the time) or 5 (Often or always) where higher scores indicate greater loneliness frequency. The survey also incorporated social capital and community trust indicators adapted from validated scales: (i) the Neighbourhood Cohesion Scale [37, 38] and Social Trust Indicators [37]. Participants rated agreement with statements regarding neighbour relations, perceived social support and willingness to assist others . Data Collection The survey was hosted on the Qualtrics online platform, accessible via secure, encrypted links distributed through NHS, community networks and social media channels. No personally identifiable information (e.g., IP addresses) was collected. Survey responses were pseudonymised at entry, stored on Imperial College London’s secure servers and handled in compliance with GDPR and the Data Protection Act 2018. Statistical Analysis Separate analyses were conducted for the UCLA Loneliness Scale, the DMOL and social capital score, following ONS guidelines. Only completed surveys were analyzed. Participant characteristics were summarised using frequencies and percentages to provide a clear overview of the sample population. In a second phase, Rasch analysis was applied to the complete questionnaire, revealing two item clusters - UCLA/DMOL and social capital - forming a unidimensional scale (see Tristan et al., 2025 ). The Rasch model, in line with best practices outlined by COSMIN and other international frameworks, provides psychometric support for the integrated use of the full instrument in assessing loneliness. This supports the identification of loneliness traits for screening and the development of targeted, objective interventions. All statistical analyses were performed using R software version 4.2.2. Geospatial Analysis and Heat Mapping To visualise the spatial distribution of loneliness across the UK, we employed kernel density estimation (KDE) using postcode-level data provided by participants. Responses were geocoded at the Lower Super Output Area (LSOA) level, allowing for aggregation while preserving anonymity. KDE was used to generate smoothed heat maps of loneliness prevalence for both the UCLA Loneliness Scale and DMOL. Spatial analyses were conducted using QGIS (version 3.36.1 and R (version 4.2.2) with relevant geospatial packages. Areas with higher concentrations of participants reporting high loneliness scores were rendered as intensity “hotspots”. Geographic disparities were further examined in relation to rural-urban classifications and regional deprivation indices. Ethical Considerations The INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study received a favourable opinion from NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483). Survey respondents provided consent electronically prior to participation. Data confidentiality and anonymity were maintained throughout the study process. To protect participants' privacy, all responses were pseudonymised at the point of entry. No personal identifiers, such as IP addresses, were collected and all data were stored on secure, encrypted servers at Imperial College London, in compliance with GDPR and the Data Protection Act 2018. Data will be securely archived for a minimum of 10 years following the completion of the study. The Checklist for Reporting Results of Internet E-Surveys (CHERRIES) was used to improve the quality of reporting [39]; Supplementaty File 2 . Results Participant Characteristics A total of 135,725 individuals participated in the INTERACT study and included in the analysis. The majority of respondents was female (61.6%) and identified as White (82.6%). The largest proportion of participants were over 65 years old (32.4%), followed by those aged 56-65 (24.5%) and 46-55 (16.2%). Respondent characteristics are presented in Table 2. Individuals aged 46-65 represented a significant portion (40.7%) of the sample, with participants aged 65+ comprising the largest group. The majority were female (61.6%), 37.4% were male, and 0.6% identified as other or chose not to disclose their gender. The study captured a predominantly White population (82.6%), with Asian/Asian British participants comprising 3.8%, Black/African/Caribbean 1.7% and mixed or multiple ethnic groups 1.5%. Approximately 8.0% of participants did not disclose their ethnicity. Nearly half (44.7%) of participants were married or in a civil partnership, 19.9% were single and 9.4% were divorced. A small proportion (7.1%) were widowed. Nearly half (42.7%) of participants held a university degree or higher, with 27.0% having attained A-levels/college education and 22.5% having secondary school as their highest educational level. A third (33.1%) of participants were retired, 28.3% were employed full-time and 10.76% were employed part-time. Additionally, 5.6% were self-employed, 2.8% were students and 1.2% were unpaid carers. A significant 18.4% reported having a disability and 45.71% had a long-term health condition ( Table 2 ). Pet ownership was relatively common among participants, with 40.8% reporting that they had one or more pets ( Table 2 ). While the survey did not collect species-specific data, the presence of a pet may represent an important, albeit underexplored, source of emotional support and companionship. Given the established association between pet ownership and reduced feelings of loneliness in some subgroups, further analysis may help elucidate whether the protective effects of companion animals vary by age, living arrangement or intensity of social isolation. Inferential findings of the INTERACT cohort are presented in El-Osta et al., 2025 b. Table 2: Respondent characteristics Variable (N = 135,725) N (%) Age 16-25 8,352 (6.2) 26-35 13,057 (9.6) 36-45 15,006 (11.1) 46-55 22,031 (16.2) 56-65 33,235 (24.5) >65 43,927 (32.4) Missing 117 (0.1) Gender Female 83,661 (61.6) Male 50,729 (37.4) Would rather not say 498 (0.4) Other 804 (0.6) Missing 33 (0.0) Ethnicity Asian/Asian British 5,162 (3.8) British Black/African/Caribbean 2,310 (1.7) Mixed/Multiple ethnic groups 2,025 (1.5) White 112,164 (82.6) White and Black Caribbean 462 (0.3) Other ethnic group 2,722 (2.0) Missing 10,880 (8.0) Marital status Divorced 12,761 (9.4) In a relationship 12,404 (9.1) Married / Civil partnership 60,612 (44.7) Single 26,983 (19.9) Widowed 9,625 (7.1) Other 2,992 (2.2) Missing 10,348 (7.6) Highest level of education A levels/College 36,700 (27.0) Secondary School 30,481 (22.5) University Degree or higher 58,008 (42.7) Missing 10,536 (7.8) Employment status Employed full-time 38,388 (28.3) Employed part-time 14,604 (10.8) Furloughed 54 Retired 45,038 (33.2) Self-employed 7,608 (5.6) Student (full or part-time) 3,733 (2.8) Unemployed 7,629 (5.6) Unpaid carer 1,560 (1.2) Volunteer (full or part-time) 1,459 (1.1) Other 4,083 (3.0) Missing 11,569 (8.5) Do you have any pets? Yes 55,413 (40.8) No 69,895 (51.5) Missing 10,417 (7.7) Number of people live in your household other than you 0 33,489 (24.7) 1 52,760 (38.9) 2-3 30,407 (22.4) 4-5 7,278 (5.4) More than 5 1,494 (1.1) Missing 10,297 (7.6) Do you have any children Yes 84,561 (62.3) No 40,684 (30.0) Missing 10,480 (7.7) Having children aged 16 years or under Yes 19,287 (22.8) No 64,991 (76.9) Missing 283 (0.3) Disability Yes 24,915 (18.4) No 96,817 (71.3) Prefer not to say 3,584 (2.6) Missing 10,409 (7.7) Long-term conditions Yes 62,038 (45.7) No 60,260 (44.4) Prefer not to say 3,085 (2.3) Missing 10,342 (7.6) Main survey findings The main survey findings are presented in Table 3 . UCLA Loneliness Scale and Direct Measure of Loneliness (DMOL) Findings from the UCLA 3-item Loneliness Scale showed substantial levels of social disconnection across the sample ( Table 3 ). When asked how often they felt a lack of companionship, 40.5% responded \"hardly ever or never,\" 37.5% said \"some of the time,\" and 21.9% reported feeling this way \"often”. Similar trends emerged in response to feelings of being left out, with 37.6% indicating they hardly ever or never felt excluded, 41.4% reporting this occurred some of the time and 21.0% stating it happened often. Regarding feelings of isolation, 40.7% reported hardly ever or never feeling isolated, 36.8% felt this way some of the time and 22.5% said they often experienced isolation. Results from the DMOL mirrored these findings ( Table 3 ). A total of 16.5% of respondents reported feeling lonely \"often or always,\" while 26.4% experienced loneliness \"some of the time\" and 22.9% \"occasionally”. A smaller proportion, 22.2%, felt lonely \"hardly ever,\" and only 12.0% said they \"never\" felt lonely. Overall, the data suggest that while loneliness manifests in varying degrees, it is a prevalent experience for the majority of participants ( Table 3 ). More than two-thirds reported feeling lonely at least occasionally and nearly one in six experienced chronic, high-intensity loneliness. These results highlight the importance of recognising loneliness as a widespread and persistent public health concern. Social connections among participants Participants reported varying frequencies of contact with relatives and friends ( Table 3 ). For relatives, 5.3% reported no contact, while others reported contact with 1 (8.9%), 2 (15.5%), 3-4 (29.2%), 5-8 (21.9%) and 9 or more relatives (11.7%) in the last month. Similarly, for friends 8.8% reported no contact, with others reporting contact with 1 (10.2%), 2 (14.1%), 3-4 (23.3%), 5-8 (18.6%) and 9 or more friends (17.3%). Table 3: Main findings Variable (N = 135,725) N (%) UCLA Loneliness Scale How often do you feel that you lack companionship (UCLA 1)? Hardly ever or never 55,034 (40.5) Some of the time 50,897 (37.5) Often 29,791 (21.9) Missing 3 (<0.01) How often do you feel left out (UCLA 2)? Hardly ever or never 51,044 (37.6) Some of the time 56,243 (41.4) Often 28,435 (21.0) Missing 3 (<0.01) How often do you feel isolated from others (UCLA 3)? Hardly ever or never 55,180 (40.7) Some of the time 50,009 (36.8) Often 30,533 (22.5) Missing 3 (<0.01) Direct Measure of Loneliness (DMOL) Often or always 22,366 (16.5) Some of the time 35,868 (26.4) Occasionally 31,133 (22.9) Hardly ever 30,122 (22.2) Never 16,233 (12) Missing 3 (<0.01) How many RELATIVES did you see or hear from in the last month? 0 7,195 (5.3) 1 12,101 (8.9) 2 20,994 (15.5) 3 or 4 39,644 (29.2) 5-8 29,656 (21.9) 9 or more 15,825 (11.7) Missing 10,310 (7.6) How many FRIENDS did you see or hear from in the last month? 0 11,936 (8.8) 1 13,850 (10.2) 2 19,188 (14.1) 3 or 4 31,679 (23.3) 5-8 25,299 (18.6) 9 or more 23,465 (17.3) Missing 10,308 (7.6) The COVID-19 pandemic and lockdowns made me feel more LONELY Strongly disagree 18,295 (13.5) Disagree 45,953 (33.9) Agree 38,662 (28.5) Strongly agree 21,040 (15.5) Missing 11,775 (8.7) The COVID-19 pandemic and lockdown made me feel more ISOLATED Strongly disagree 17,033 (12.5) Disagree 40,937 (30.2) Agree 43,513 (32.1) Strongly agree 21,915 (16.1) Missing 12,327 (9.1) Participants' perceptions regarding their neighbourhood dynamics, cohesion and trust (Social Capital Scale) Participants’ perceptions of their local neighbourhoods revealed a complex and often fragmented picture of social capital ( Table 3 ). When asked whether people in their area were willing to help one another, just over half (51.7%) agreed and only 12.3% strongly agreed, suggesting that while mutual aid exists it may not be deeply embedded. Conversely, more than one in four respondents either disagreed (21.0%) or strongly disagreed (7.3%), indicating significant deficits in perceived neighbourly support. Views on community cohesion were similarly mixed ( Table 3 ). Only 6.7% strongly agreed that their neighbourhood was close-knit, with a further 38.1% agreeing. In contrast, 35.7% disagreed and 11.3% strongly disagreed-pointing to a widespread sense of social fragmentation, particularly in urban areas. The ability to turn to neighbours in times of need was also uncertain: while 36.2% believed they could borrow £30 from a neighbour in an emergency, an almost equal proportion (55.3%) disagreed or strongly disagreed. Perceptions of interpersonal trust reflected this ambivalence ( Table 3 ). A total of 58.9% of participants agreed that people in their neighbourhood could be trusted, with only 9.5% strongly agreeing. Meanwhile, 22.7% expressed mistrust, reporting either disagreement (18.1%) or strong disagreement (4.6%) with the statement. When asked whether neighbours would help with basic tasks such as shopping for groceries during illness, just over half (52.5%) felt confident they could rely on local support, but over 38% were doubtful. Finally, when asked about shared values, nearly one-third (29.5%) agreed that people in their neighbourhood did not share the same values as them, while 53.1% disagreed ( Table 3 ). This divergence reflects the broader social heterogeneity of contemporary communities and its potential impact on social cohesion. Collectively, these findings highlight inconsistent and, in many cases, weak perceptions of local solidarity, trust and mutual support which are important dimensions of social capital that are likely to shape and, in some cases, amplify individuals’ experiences of loneliness and isolation. Impact of COVID-19 Pandemic During the COVID-19 pandemic, participants reported experiencing heightened feelings of loneliness and isolation ( Table 3 ). In terms of loneliness, 15.5% of respondents strongly agreed that the pandemic exacerbated their sense of loneliness, while 28.5% agreed. On the other hand, 13.5% strongly disagreed and 33.9% disagreed with this sentiment. Similarly, regarding isolation, 16.1% strongly agreed that the pandemic intensified their feelings of isolation and 32.1% agreed. In contrast, 12.5% strongly disagreed and 30.2% disagreed with this statement. These findings highlight the varied psychological impacts of the pandemic on individuals' perceptions of loneliness and isolation. Prevalence of Loneliness and Social Isolation Summary statistics for the UCLA Loneliness Scale, DMOLand Social Capital Score are presented in Table 4. Table 4: Summary statistics for UCLA Loneliness Scale, Direct Measure of Loneliness and Social Capital Score Median IQR Min. Max. UCLA Loneliness Scale 6 3 - 7 3 9 Direct Measure of Loneliness (DMOL)* 3 2 - 4 1 5 Social Capital Scale 4 2- 6 0 7 Never=1; Hardly ever=2; Occasionally=3; Some of the time=4; Often or always=5 UCLA Loneliness Scale The prevalence of loneliness in the INTERACT cohort was widespread and heterogeneous ( Tables 3 & 4 ). Responses to the UCLA 3-item Loneliness Scale resulted in a median score of 6 (IQR: 3-7), suggesting that most participants experienced at least moderate levels of loneliness; Table 4 . The scale’s full range (3-9) was observed, reflecting a broad distribution of subjective experiences, from minimal to chronic loneliness. Across the three items, approximately 1 in 5 participants consistently reported feeling ‘often’ lonely, left out or isolated, while the largest proportion felt this way “some of the time” ( Table 3 ). Direct Measure of Loneliness (DMOL) The ONS DMOL further provided additional insights into participants’ experiences of loneliness and corroborated these trends ( Tables 3 & 4 ). The median DMOL score was 3 (IQR: 2-4), corresponding to loneliness felt “occasionally” to “some of the time”. However, 16.5% of respondents reported feeling lonely ‘often or always’, indicating a substantial burden of persistent loneliness. By contrast, only 12% of participants reported never feeling lonely, highlighting the rarity of complete social connectedness even in this large, diverse sample. Social Connections and Frequency of Contact In addition to individual-level risk factors, participants’ experiences of loneliness were shaped by neighbourhood context and perceived social support. Perceived social capital, which was measured via a composite index of neighbourhood trust, cohesion and perceived support, also varied widely. The median score was 4 (IQR: 2-6) on a 0-7 scale ( Table 4 ). A small percentage (5.3%) reported no contact with relatives in the last month, while 8.9% had contact with one relative, 15.5% with two relatives and 29.2% with 3-4 relatives ( Table 3 ). A similar trend was observed with friends, where 8.8% reported no contact, 10.2% had contact with one friend, 14.1% with two friends and 23.3% with 3-4 friends ( Table 3). Thus, while some participants perceived their communities as supportive, a considerable proportion indicated weak or fragmented social ties. These patterns were particularly pronounced in urban settings, as elaborated in the geospatial analysis. Impact of the COVID-19 Pandemic on Loneliness and Isolation Participants’ responses suggest that the COVID-19 pandemic played a significant role in amplifying feelings of loneliness and social isolation. Nearly 44% agreed or strongly agreed that their sense of loneliness increased during the pandemic, while a similar proportion (48.2%) reported heightened feelings of social isolation ( Table 3) . These effects were not uniform; approximately one-third of participants did not perceive any negative impact, highlighting a differential experience of pandemic-related social disruption. Subgroup Analysis Descriptive analysis showed that loneliness was not evenly distributed across the sample. It was most prevalent among young adults (16-25 years), ethnic minority groups and those who were single, divorced or unemployed. Women reported slightly higher levels of loneliness than men and participants from Asian/Asian British backgrounds reported greater loneliness than White participants. These patterns suggest the importance of age, relationship status, ethnicity and socioeconomic context in shaping experiences of loneliness. Detailed multivariable and interaction analyses are presented in El-Osta, et al., 2025. b . Geospatial Patterns of Loneliness Geospatial analysis highlighted striking regional variation in reported loneliness across England. Kernel density heat maps generated from participants’ postcode-level data highlighted distinct urban “hotspots” with elevated loneliness scores. As shown in Figure 1 , major metropolitan areas, including London, Birmingham and Manchester exhibited the highest concentrations of individuals reporting frequent loneliness as measured by both the UCLA 3-item scale and DMOL. In contrast, rural and semi-rural areas in the South West and East of England tended to report lower levels of loneliness, reflecting greater community cohesion and higher social capital scores in these regions. These spatial differences persisted even when stratified by age and social capital indicators, reinforcing the notion of an “urban paradox”: densely populated areas characterised by weak interpersonal ties, social anonymity and reduced trust. The visualisation of loneliness at the LSOA level provides a powerful tool for identifying areas with concentrated social disconnection. This spatial lens offers actionable insight for local authorities and health systems aiming to prioritise place-based interventions. While the present map serves as a proof-of-concept, a dedicated geospatial analysis incorporating indices of deprivation, digital exclusion and service accessibility is planned in a follow-up manuscript. Discussion This study presents the largest and most comprehensive descriptive epidemiological investigation of loneliness and social disconnection undertaken in the United Kingdom to date. Drawing on data from over 135,000 community-dwelling adults, the INTERACT study reveals that loneliness is a widespread and deeply patterned phenomenon, affecting individuals across the life course and social spectrum. Remarkably, we found that 16.5% of participants reported feeling lonely “often or always,” while more than two-thirds indicated experiencing loneliness at least occasionally. These rates are substantially higher than previously estimated in UK national statistics and suggest a critical public health burden[ 40 ]. Our results also confirm that loneliness is not confined to older adults, a widely held misconception in public discourse. Instead, younger adults (16–25 years) reported the highest levels of loneliness, aligning with emerging evidence that loneliness peaks in adolescence and early adulthood[ 30 , 41 ]. While our data show that loneliness is most prevalent among younger adults, we also identified a substantial burden of loneliness among older adults aged 65 and above, who comprised the largest age group in our cohort (32%). Approximately 17% of participants in this age group reported severe loneliness on the UCLA scale and over one-third experienced loneliness at least occasionally according to the DMOL. These findings are especially concerning given the established association between loneliness and adverse outcomes in later life, including increased risk of cognitive decline, cardiovascular disease, functional impairment and premature mortality[ 42 , 43 ]. Interestingly, despite high absolute numbers, older adults reported lower loneliness scores on average compared to younger cohorts, suggesting a potential resilience effect, possibly linked to stronger existing social ties, more stable relationships or better coping strategies. However, this apparent resilience may mask important subgroup variation. For example, widowed individuals, those living alone and those with long-term health conditions or disabilities were significantly more likely to report chronic loneliness. Previous studies have highlighted how cultural and structural factors, including language barriers, social discrimination and weaker community networks, may contribute to higher loneliness among ethnic minorities[ 44 ]. Ethnic minority groups, particularly Asian/Asian British participants in our study reported significantly higher loneliness scores compared to White participants. This highlights the need for culturally sensitive interventions, particularly within urban areas where minority populations are more concentrated. Our geospatial analysis revealed striking geographic disparities in the distribution of loneliness across the UK with pronounced “hotspots” concentrated in major urban centres such as London, Birmingham and Manchester. These findings reinforce the growing body of literature describing the ‘urban paradox’ - the counterintuitive phenomenon in which high population density does not equate to greater social connectedness. Despite physical proximity, urban environments may foster fragmentation, social anonymity and reduced trust, especially in areas with lower social capital[ 44 , 45 ]. Conversely, rural and semi-rural areas appeared to exhibit greater social cohesion and lower levels of loneliness, potentially due to stronger intergenerational ties and community integration[ 18 ]. These results highlight the need for place-based interventions that account for spatial context, particularly in post-pandemic urban recovery efforts. Local authorities and public health teams can leverage heat mapping tools, such as those demonstrated in this study, to prioritise and tailor interventions in high-burden areas. The heat maps presented here offer only a preliminary view to demonstrate a proof-of-concept. A dedicated follow-up manuscript is planned to expand this analysis in detail, integrating additional geodemographic, deprivation and service accessibility variables to further understand the spatial epidemiology of loneliness and guide targeted action. While loneliness in older adults has been extensively studied, younger populations may face unique socio-emotional challenges, including academic pressure, employment instability and social comparison via digital platforms[ 14 , 46 ]. Collectively, these findings highlight the dual reality that while younger adults may be at greatest risk, older adults remain a critical group for intervention, particularly in the context of multimorbidity, bereavement and structural disconnection. Our study expands on existing research by combining validated loneliness scales with a large, demographically diverse sample, allowing for more granular insights. Previous large-scale studies including the English Longitudinal Study of Ageing (ELSA), have focused predominantly on older adults[ 47 ], whereas INTERACT provides a broader, population-wide perspective. Moreover, our findings align with the UK Government's Loneliness Strategy (2018), which identified single and divorced individuals as high-risk groups. In our study, single and divorced individuals exhibited significantly higher loneliness scores compared to married participants, reaffirming the role of relationship status as a protective factor against loneliness[ 48 ]. That the COVID-19 pandemic further amplified loneliness levels, with 44% of our study respondents reporting that pandemic-related restrictions increased their feelings of loneliness, aligns with findings from Bu et al. in 2020[ 49 ], who demonstrated that social distancing measures disproportionately affected individuals with pre-existing loneliness. Clearly, the long-term mental health consequences of pandemic-related social isolation remain an urgent research priority, particularly as societies transition into post-pandemic recovery phases[ 24 ]. Strengths and limitations The INTERACT Study represents the largest population-based investigation of loneliness, social isolation and social capital ever conducted in the United Kingdom, with over 135,000 community-dwelling adults participating. This unprecedented sample size is a major strength, enabling subgroup analysis across age, gender, ethnicity, socioeconomic status and health-related variables with sufficient statistical power to detect meaningful differences. Furthermore, the study’s diverse, multi-channel recruitment strategy, including NHS primary care networks, voluntary sector partners, social media and research platforms such as the NIHR Be Part of Research Network enhanced the inclusivity and demographic reach of the sample, improving its national relevance. Methodologically, the study is grounded in the use of validated instruments, including the 3-item UCLA Loneliness Scale and the ONS DMOL, both of which ensure alignment with existing literature and facilitate benchmarking against national and international datasets. Importantly, the integration of social capital indicators, assessing trust, cohesion and perceived neighbourhood support, enables a multidimensional understanding of loneliness, moving beyond individual-level explanations to consider the influence of structural and contextual factors. The geospatial kernel density mapping conducted at the LSOA level adds a novel spatial dimension, allowing the identification of loneliness “hotspots” and providing actionable insight for local authorities and health systems interested in place-based interventions. The principal limitation of this study is concerned with its cross-sectional design that precludes any inference of causality, meaning we cannot determine the temporal direction of observed associations between loneliness and sociodemographic characteristics. Longitudinal follow-up will be essential to understand how loneliness evolves over time, especially in the context of life-course transitions, bereavement, chronic illness and social mobility. Second, the study relied exclusively on self-reported data, which may be influenced by social desirability or recall bias. Although loneliness is inherently subjective, participants may underreport feelings of disconnection due to stigma or perceived social expectations, particularly in certain cultural or gender groups. Third, while the sample is large and diverse, sampling bias may persist. Recruitment via NHS and digital platforms may have inadvertently excluded individuals with limited healthcare access (e.g. homeless or undocumented populations), low digital literacy or those who are socially isolated to the point of disengagement from community or institutional networks. Given the intended final sample of INTERACT is 500,000 participants across the UK, for reasons of pragmatism the survey was administered online raising concerns about the underrepresentation of digitally excluded individuals, particularly older adults, those in rural areas and people with disabilities. Future manuscripts will report the findings of subgroup analyses including people experiencing homelessness, undocumented populations and traditionally hard-to-reach groups including care homes and carers. Fourth, although this baseline paper focuses on descriptive analysis, missing data were non-trivial approximating 7%. We addressed this comprehensively in Paper 2 ( El-Osta et al., 2025b ) using Multiple Imputation by Chained Equations (MICE) to ensure more complete and unbiased estimates in subsequent regression analyses. Despite this, we acknowledge that complete case bias cannot be fully ruled out in descriptive reporting. We also acknowledge the potential impact of seasonality on reported loneliness. Data collection for the INTERACT Study already spanned multiple seasons, including winter months when loneliness and social isolation are typically more pronounced due to shorter daylight hours, adverse weather conditions and reduced social activity. While this variability enhances ecological validity, it may also introduce temporal bias as responses could reflect transient mood states influenced by environmental or cultural factors (e.g. holidays, school breaks or lockdown anniversaries). Our future analyses will explore seasonal variation explicitly or control for time-of-response effects in longitudinal extensions of this work. Finally, the COVID-19 pandemic context in which a portion of the data was collected may have influenced responses, with the effect of either inflating or dampening reported loneliness depending on participants’ stage of recovery, adaptation or coping. While this reflects real-world conditions and lived experience, it should be interpreted with care when comparing results to pre-pandemic or international cohorts. Despite these limitations, the INTERACT Study’s methodological strengths of unprecedented scale, validated tools, geospatial design and conceptual breadth, position it as a foundational platform for more detailed inferential (Paper 2) and psychometric (Paper 3) investigations. The study establishes a robust baseline for loneliness surveillance in England and provides a critical springboard for evidence-based public health policy, research and intervention design. Implications for Public Health and Policy The INTERACT Study offers critical insights for policymakers, public health leaders and community planners seeking to address the growing burden of loneliness in the UK. Its scale and spatial precision provide a foundation for more targeted, data-driven and equitable approaches to intervention design and delivery. This study challenges prevailing narratives by demonstrating that loneliness is not confined to older adults. The highest levels of loneliness were observed among young adults, particularly those facing insecure employment, social comparison and digital overexposure. Interventions must therefore adopt a life-course perspective, addressing the distinct needs of younger populations as well as those of older adults. For young people, this could include digital wellbeing initiatives, peer mentoring and access to inclusive community hubs[ 50 , 51 ]. For older adults, it may mean tackling mobility, bereavement and access barriers. Further, given the high burden of loneliness among ethnic minorities, policies should prioritise culturally inclusive initiatives, such as multilingual community programs, minority-focused support networks and interventions addressing structural discrimination. The geospatial findings illustrate the value of place-based interventions. Loneliness “hotspots” were concentrated in urban centres marked by high density but low social capital. Local authorities and Integrated Care Systems can use spatial data to prioritise resources, develop community infrastructure and embed loneliness-reduction strategies into broader health equity agendas. Investments in intergenerational centres, neighbourhood initiatives and public realm improvements can foster greater social connection and reduce isolation in urban areas. This is particularly relevant in the context of post-pandemic urban recovery, where the reimagining of public spaces could promote greater social interactions[ 52 , 53 ]. The pandemic’s amplifying effects on loneliness emphasise the urgency of embedding resilience and social support into public health recovery efforts. Policymakers should consider how future public health emergencies might exacerbate social disconnection and plan proactively to mitigate those impacts. The strong association between low social capital and high loneliness prevalence emphasises the importance of shifting from purely individual-level interventions (e.g. social prescribing) toward community-level investment in trust, cohesion and civic infrastructure. Programmes that build shared identity such as intergenerational community centres, co-housing schemes and neighbourhood-based volunteering, can help to reverse the erosion of social cohesion in densely populated urban areas. These interventions should be co-designed with residents, particularly in the loneliness “hotspots” identified through this study’s geospatial analysis. This is the remit of a future output by the same authors earmarked for publication in 2026. Finally, the inclusion of social capital measures within the INTERACT tool presents a model for future surveillance. By incorporating indicators of trust, cohesion and perceived support, future surveys can move beyond individual-level loneliness to track the health of community networks more holistically. National frameworks such as the UK Government’s Loneliness Strategy and ONS wellbeing monitoring could be enhanced by routinely measuring social capital. Crucially, INTERACT provides a scalable template for global adaptation. As the health and economic costs of loneliness gain recognition internationally from WHO’s Commission on Social Connection to OECD’s wellbeing framework, there is an urgent need for cross-cultural, psychometrically robust tools that can be deployed across settings. The INTERACT platform offers a promising model for global surveillance and intervention design, especially in ageing societies where loneliness and isolation are rapidly becoming endemic. Taken together, the INTERACT findings suggest loneliness should be reframed as a population health issue, one shaped by policy decisions, community design and the quality of our social fabric. Addressing it will require a whole-systems approach that cuts across health, education, housing and urban planning. The INTERACT platform provides a scalable, evidence-based model to guide such strategies and inform both national and international action on loneliness and social wellbeing. Future Research Directions This baseline study lays the groundwork for an ambitious programme of research to better understand, monitor and address loneliness across populations, settings and cultural contexts. Building on the descriptive epidemiology presented here, future analyses will explore longitudinal patterns, causal pathways and intervention impact using advanced statistical methods and psychometric refinements as presented in Papers 2 ( El-Osta et al., 2025b ) & Paper 3 ( Tristan et al., 2025 ). The next phases of the INTERACT programme include longitudinal follow-up to assess how loneliness evolves over time, particularly in response to life transitions, health status changes and social mobility. New data currently being collected among NHS staff (representing the occupational health cohort) will explore loneliness, burnout and team cohesion among healthcare professionals. In-depth interviews are also planned with subgroups most affected by loneliness, including young adults, ethnic minorities, people with disabilities, care home residents, carers and those experiencing homelessness to understand lived experience and inform co-designed interventions. Further psychometric testing and Rasch analysis will continue to strengthen the validity and reliability of the INTERACT scale. These efforts will support its adaptation for use in new populations and geographies, forming the basis for an internationally harmonised loneliness surveillance framework. Conclusion The INTERACT Study represents the most extensive mapping of loneliness and social capital in the UK to date. With over 135,000 participants, the findings highlight high levels of social disconnection and reveal stark disparities by age, ethnicity, geography and health status. This evidence challenges outdated assumptions about who is lonely and why, urging a reframing of loneliness as a structural and systemic issue. The insights offer a critical foundation for equity-focused, place-based public health strategies that prioritise social connection. As the global burden of loneliness rises, INTERACT provides a scalable model for action, combining robust measurement, community engagement and geospatial intelligence to support better health, wellbeing and social cohesion for all. Declarations Consent for publication Not applicable. Funding This research received no funding. Austen El-Osta is grateful for support from the National Institute for Health and 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 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. 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Supplementary Files SupplementaryFile1Surveyexport.docx SupplementaryFile2CHERRIESChecklist.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers invited by journal 18 Jun, 2025 Editor assigned by journal 11 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 10 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-6864072\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":473179364,\"identity\":\"e47f340c-ad2c-4c27-a484-75e8fcc51510\",\"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\":473179366,\"identity\":\"8ee7b5b7-ed27-4af7-b7af-0f9ae27788c9\",\"order_by\":1,\"name\":\"Aos Alaa\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Aos\",\"middleName\":\"\",\"lastName\":\"Alaa\",\"suffix\":\"\"},{\"id\":473179371,\"identity\":\"fd1fb849-adb9-4848-bf27-9a44063b1cff\",\"order_by\":2,\"name\":\"Mahmoud Al-Ammouri\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mahmoud\",\"middleName\":\"\",\"lastName\":\"Al-Ammouri\",\"suffix\":\"\"},{\"id\":473179373,\"identity\":\"687f5122-29e7-41f0-a3d4-d17d656e072b\",\"order_by\":3,\"name\":\"Sami Altalib\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sami\",\"middleName\":\"\",\"lastName\":\"Altalib\",\"suffix\":\"\"},{\"id\":473179374,\"identity\":\"2e32983f-a153-4728-a0b9-b394d8655bff\",\"order_by\":4,\"name\":\"Agustin Tristán-López\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Agustin\",\"middleName\":\"\",\"lastName\":\"Tristán-López\",\"suffix\":\"\"},{\"id\":473179375,\"identity\":\"372bf9ad-0e5b-4067-90ca-7b8d26c460ec\",\"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 14:23:31\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6864072/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6864072/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":85867620,\"identity\":\"2edac8df-d8ba-414e-8afa-88eea6bafb24\",\"added_by\":\"auto\",\"created_at\":\"2025-07-02 13:25:35\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2204220,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eHigh-density urban areas such as London and Birmingham show the greatest concentration of loneliness, reinforcing the spatial clustering of social disconnection. Map of UK is shown on left of image. Map of Lonofn is shown on the right.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6864072/v1/a46f3fb7f5bbd35567e84fb8.png\"},{\"id\":85869221,\"identity\":\"6994cb04-cc4b-492f-98aa-01576b42396e\",\"added_by\":\"auto\",\"created_at\":\"2025-07-02 13:49:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4549144,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6864072/v1/79e06b07-0bf9-4c87-b64b-f9c048d9269d.pdf\"},{\"id\":85867630,\"identity\":\"fc805f5f-d87f-481e-aa01-1958294f0816\",\"added_by\":\"auto\",\"created_at\":\"2025-07-02 13:25:35\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":30532,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFile1Surveyexport.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6864072/v1/b1fc883b4957d67262198fb7.docx\"},{\"id\":85867622,\"identity\":\"295d2216-9f41-4b74-9539-948e7e572009\",\"added_by\":\"auto\",\"created_at\":\"2025-07-02 13:25:35\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":20155,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryFile2CHERRIESChecklist.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6864072/v1/604fc327699c9ac2e0ecbda3.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Measuring Loneliness at an Unprecedented Scale: The INTERACT Study’s Approach and Initial Findings\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eLoneliness is commonly defined as a subjective, distressing experience resulting from a discrepancy between desired and actual social connections [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. It is distinct from social isolation, which refers to an objective lack of social contact [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Loneliness and social isolation are nrecognised as major global public health challenges, with substantial implications for mental, physical and social wellbeing. Extensive research demonstrated strong associations between loneliness and adverse health outcomes, including depression, anxiety, cognitive decline, cardiovascular disease, hypertension and increased mortality risk [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eRecognising the urgency of the issue, the World Health Organisation (WHO) has identified social isolation and loneliness as critical determinants of health, highlighting the need for evidence-based interventions and policy responses [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Beyond individual wellbeing, the economic burden of loneliness is equally profound, contributing to higher healthcare utilisation, lost workplace productivity and increased demand for social care services. In the United Kingdom (UK), loneliness costs employers up to \\u0026pound;3.7\\u0026nbsp;billion annually or at least \\u0026pound;9,976 per person per year [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eContrary to popular belief, loneliness does not solely affect older adults. Recent research suggests that loneliness follows a U-shaped distribution, with peaks in adolescence and late adulthood[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Young adults (16\\u0026ndash;25 years) consistently report high levels of loneliness[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e], possibly due to academic stress, employment insecurity and increased digital socialisation, which may not always translate into meaningful social bonds [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Meanwhile, in older populations, loneliness is frequently associated with bereavement, declining health and reduced social participation [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Geographic disparities in loneliness have also been widely reported, with urban environments exhibiting higher loneliness prevalence despite greater population density [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. The \\\"urban paradox\\\" suggests that city life, while offering proximity to social and economic opportunities, may promote weaker community ties, higher mobility and lower neighbourhood trust, factors contributing to greater loneliness[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. By contrast, rural communities, despite geographic isolation, often benefit from stronger intergenerational networks and social cohesion[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. Ethnicity and cultural background also play a significant role in loneliness. Studies indicate that ethnic minority groups experience higher loneliness levels due to structural inequalities, language barriers, discrimination and weaker social integration[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. The COVID-19 pandemic also intensified loneliness and social isolation globally, with prolonged lockdowns, social distancing measures and economic disruptions contributing to worsening mental health outcomes [\\u003cspan additionalcitationids=\\\"CR23 CR24\\\" citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eLoneliness is a multidimensional construct influenced by individual, relational and structural factors, including personality traits, life transitions, socioeconomic conditions and community-level social capital [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. The measurement of loneliness remains a significant methodological challenge. While self-reported scales such as the UCLA Loneliness Scale [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e] and the Office for National Statistics (ONS) Direct Measure of Loneliness (DMOL) [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]are widely used, they often fail to capture neighbourhood-level social capital, community cohesion and trust[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e], all of which play a crucial role in shaping social connectedness [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn the UK, loneliness was recognised as a national priority with the government launching the Loneliness Strategy in 2018, the first of its kind globally, to address the growing crisis [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. However, despite this early leadership, large-scale, population-based studies of loneliness in the UK remain limited[\\u003cspan additionalcitationids=\\\"CR31\\\" citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Much of the existing literature has focused on older adults or small, localised cohorts, with less attention paid to younger populations or the broader social and structural determinants of loneliness [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe Measuring Loneliness in the UK (INTERACT) Study was developed to address these critical gaps. It represents the largest population-level investigation of loneliness to date, not only within the UK but globally. By incorporating a multidimensional assessment of loneliness alongside measures of social capital, health status, digital connectivity and socioeconomic factors, INTERACT provides one of the most comprehensive post-pandemic assessments of social disconnection in the population. This is particularly important in the context of COVID-19, which exacerbated isolation and disrupted social networks in unprecedented ways.\\u003c/p\\u003e \\u003cp\\u003eThe INTERACT tool is a composite survey instrument developed to assess loneliness, social connection and related determinants at scale. It integrates validated items from established measures such as the UCLA Loneliness Scale and DMOL, alongside questions on digital connectivity, health status, social capital and demographic characteristics. The INTERACT tool was designed for large-scale deployment to help capture the intensity and dimensions of loneliness, allowing for detailed subgroup analyses. Its multidimensional structure enables a comprehensive evaluation of social disconnection and provides a robust platform for geospatial and longitudinal analysis across diverse UK populations. By transforming individual-level data into regional population-level patterns, geospatial heat maps can help visualise geographic disparities in loneliness across participating regions to help identify loneliness \\u0026ldquo;hotspots\\u0026rdquo; where targeted interventions may be most urgently needed.\\u003c/p\\u003e \\u003cp\\u003eThe primary objective of the INTERACT study is to map the prevalence and intensity of Loneliness across diverse population groups in the UK. Specifically, the study aimed to explore how loneliness varies across sociodemographic factors such as age, gender, ethnicity, marital status and employment. This paper is the first in a series of three manuscripts (\\u003cb\\u003eEl-Osta et al., 2025b\\u003c/b\\u003e and \\u003cb\\u003eAgustin et al., 2025)\\u003c/b\\u003e to report the initial findings of the INTERACT study and is intended primarily to provide a clear overview of the sample population.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003ch4\\u003eStudy Design\\u003c/h4\\u003e\\n\\u003cp\\u003eThe INTERACT study is a large-scale, cross-sectional, observational study designed to assess the prevalence, sociodemographic patterns and geographic distribution of loneliness and social isolation across the UK. The study employed a multi-methods quantitative approach, combining quantitative survey data with geospatial analysis to provide a comprehensive evaluation of loneliness and social connectedness at a population level. Only the quantitative study is conciered here.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStudy Setting and Recruitment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was initially launched in England and collected data between 1 October 2021 and 3 June 2024. Participants were recruited via NHS Primary Care Networks (PCNs) where invitations were sent via general practices and NHS Trusts using digital outreach methods, including emails and SMS messages. Voluntary and Community Sector Organisations facilitated recruitment through partnerships with local charities, community groups and national organisations focused on mental health and social wellbeing. The NIHR Be Part of Research (BPoR) Network advertised through research engagement platforms to enhance participation across diverse population subgroups. Targeted recruitment was conducted via Twitter (@LonelinessStudy, @ImperialSCARU), LinkedIn and institutional websites to reach younger adults and digitally engaged populations. To maximise sample representativeness, stratified recruitment strategies were employed, ensuring balanced participation across age groups, ethnic backgrounds and socioeconomic status. Potentially eligible participants who received a link with an invitation to participate could learn more about the study by reading the participant information sheet (PIS) which could be accessed on the survey introduction page.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInclusion and Exclusion Criteria\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants were eligible to take part if they were 16 years or older, resided in England and were either registered as NHS patients or affiliated with NHS Trusts. The survey was open and participation was voluntary to participants. Inclusion also required participants to have access to an internet-enabled device (such as a smartphone, tablet or computer) to complete the online survey and to provide informed electronic consent before participation. Given the study\\u0026apos;s focus on general population-based loneliness assessments, recruitment targeted a diverse range of individuals, including those from different age groups, ethnic backgrounds and socioeconomic statuses.\\u003c/p\\u003e\\n\\u003cp\\u003eExclusion criteria included individuals with severe cognitive impairments (e.g., dementia, schizophrenia or psychosis) that could compromise their ability to provide informed consent. Additionally, individuals receiving end-of-life care or experiencing severe mental health conditions requiring institutionalisation were excluded from the study. Participants who refused or were unable to provide informed consent were also ineligible. Given that the survey was conducted entirely online, individuals without internet access or digital literacy skills may have been inadvertently excluded, a limitation acknowledged in the study\\u0026rsquo;s discussion.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSurvey Development and Measures\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe INTERACT Scale was developed as a composite of various existing items, including validated loneliness measures, social capital indicators and community trust scales (\\u003cstrong\\u003eTable 1\\u003c/strong\\u003e). The final survey instrument included 27 items over two pages, comprising 14 socio-demographic questions and 13 validated scale items to assess loneliness and social connectedness. To promote inclusivity, the survey was translated into 11 languages (the most commonly used across the UK). Respondents were able to review their responses before submission. The survey is included in \\u003cstrong\\u003eSupplementary File\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e and can be accessed by following this link: https://imperial.eu.qualtrics.com/jfe/form/SV_2uIyxcCD7JYbKyq. The survey underwent pilot testing with 38 participants, leading to minor revisions for clarity and accessibility. The survey structure is shown in \\u003cstrong\\u003eTable 1\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 1: Survey Structure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSurvey block\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDescription\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStudy introduction, link to PIS \\u0026amp; Informed Consent\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eParticipants provided digital consent before proceeding.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge, Gender \\u0026amp; Postcode\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eAge, gender, full postcode\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLoneliness Measures\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eUCLA Loneliness Scale, ONS Direct Measure of Loneliness (DMOL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSocial Connection\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eFrequency of contact with family and friends.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSocial Capital Indicators\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eNeighbourhood trust, community cohesion and perceived social support.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCOVID\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eCovid specific questions\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDemographics\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eEthnicity, marital status, number of children, pet ownership, employment, disability and household size.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 226px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eConsent to contact\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 415px;\\\"\\u003e\\n \\u003cp\\u003eRespondents could leave their contact details should they wish to volunteer to partake in a personal interview.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLoneliness Assessment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMeasurement development and evaluation procedures were grounded in internationally recognised frameworks to ensure objectivity, validity and reliability, including the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) criteria for health-related instruments [35] and the psychometric guidelines outlined in the American Educational Research Associations, American Psychological Association and National Council on Measurement in Education (AERA-APA-NCME) Standards for Educational and Psychological Testing [36].\\u003c/p\\u003e\\n\\u003cp\\u003eLoneliness was measured using two validated instruments. The UCLA Loneliness Scale (3-item version) [27] with the following three questions: \\u0026ldquo;How often do you feel that you lack companionship?\\u0026rdquo;, \\u0026ldquo;How often do you feel left out?\\u0026rdquo; and, \\u0026ldquo;How often do you feel isolated from others?\\u0026rdquo;. Response options for this scale were: 1 (Hardly ever), 2 (Some of the time), and 3 (Often). Total scores range from 3 to 9, with higher scores indicating greater loneliness. The ONS Direct Measure of Loneliness (DMOL) [28]\\u0026ldquo;How often do you feel lonely?\\u0026rdquo; question allowed response options: 1 (Never), 2 (Hardly ever), 3 (Occasionally), 4 (Some of the time) or 5 (Often or always) where higher scores indicate greater loneliness frequency.\\u003c/p\\u003e\\n\\u003cp\\u003eThe survey also incorporated social capital and community trust indicators adapted from validated scales: (i) the Neighbourhood Cohesion Scale [37, 38] and Social Trust Indicators [37]. Participants rated agreement with statements regarding neighbour relations, perceived social support and willingness to assist others\\u003cstrong\\u003e.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Collection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe survey was hosted on the Qualtrics online platform, accessible via secure, encrypted links distributed through NHS, community networks and social media channels. No personally identifiable information (e.g., IP addresses) was collected. Survey responses were pseudonymised at entry, stored on Imperial College London\\u0026rsquo;s secure servers and handled in compliance with GDPR and the Data Protection Act 2018.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSeparate analyses were conducted for the UCLA Loneliness Scale, the DMOL and social capital score, following ONS guidelines. Only completed surveys were analyzed. Participant characteristics were summarised using frequencies and percentages to provide a clear overview of the sample population. In a second phase, Rasch analysis was applied to the complete questionnaire, revealing two item clusters - UCLA/DMOL and social capital - forming a unidimensional scale (see \\u003cstrong\\u003eTristan et al., 2025\\u003c/strong\\u003e). The Rasch model, in line with best practices outlined by COSMIN and other international frameworks, provides psychometric support for the integrated use of the full instrument in assessing loneliness. This supports the identification of loneliness traits for screening and the development of targeted, objective interventions. All statistical analyses were performed using R software version 4.2.2.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGeospatial Analysis and Heat Mapping\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo visualise the spatial distribution of loneliness across the UK, we employed kernel density estimation (KDE) using postcode-level data provided by participants. Responses were geocoded at the Lower Super Output Area (LSOA) level, allowing for aggregation while preserving anonymity. KDE was used to generate smoothed heat maps of loneliness prevalence for both the UCLA Loneliness Scale and DMOL. Spatial analyses were conducted using QGIS (version 3.36.1 and R (version 4.2.2) with relevant geospatial packages. Areas with higher concentrations of participants reporting high loneliness scores were rendered as intensity \\u0026ldquo;hotspots\\u0026rdquo;. Geographic disparities were further examined in relation to rural-urban classifications and regional deprivation indices.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical Considerations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe INTERACT study was registered on the NIHR Portfolio (CPMS#52230). The study received a favourable opinion from NHS Research Ethics Committee (#21IC6950) and Imperial College London Research Ethics Committee (ICREC #305483).\\u003c/p\\u003e\\n\\u003cp\\u003eSurvey respondents provided consent electronically prior to participation. Data confidentiality and anonymity were maintained throughout the study process. To protect participants\\u0026apos; privacy, all responses were pseudonymised at the point of entry. No personal identifiers, such as IP addresses, were collected and all data were stored on secure, encrypted servers at Imperial College London, in compliance with GDPR and the Data Protection Act 2018. Data will be securely archived for a minimum of 10 years following the completion of the study.\\u003c/p\\u003e\\n\\u003cp\\u003eThe Checklist for Reporting Results of Internet E-Surveys (CHERRIES) was used to improve the quality of reporting [39]; \\u003cstrong\\u003eSupplementaty File 2\\u003c/strong\\u003e.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipant Characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 135,725 individuals participated in the INTERACT study and included in the analysis. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe \\u0026nbsp;majority of respondents was female (61.6%) and identified as White (82.6%). The largest proportion of participants were over 65 years old (32.4%), followed by those aged 56-65 (24.5%) and 46-55 (16.2%). Respondent characteristics are presented in \\u003cstrong\\u003eTable 2.\\u003c/strong\\u003e Individuals aged 46-65 represented a significant portion (40.7%) of the sample, with participants aged 65+ comprising the largest group. The majority were female (61.6%), 37.4% were male, and 0.6% identified as other or chose not to disclose their gender. The study captured a predominantly White population (82.6%), with Asian/Asian British participants comprising 3.8%, Black/African/Caribbean 1.7% and mixed or multiple ethnic groups 1.5%. Approximately 8.0% of participants did not disclose their ethnicity. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNearly half (44.7%) of participants were married or in a civil partnership, 19.9% were single and 9.4% were divorced. A small proportion (7.1%) were widowed. Nearly half (42.7%) of participants held a university degree or higher, with 27.0% having attained A-levels/college education and 22.5% having secondary school as their highest educational level. A third (33.1%) of participants were retired, 28.3% were employed full-time and 10.76% were employed part-time. Additionally, 5.6% were self-employed, 2.8% were students and 1.2% were unpaid carers. A significant 18.4% reported having a disability and 45.71% had a long-term health condition (\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e). \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePet ownership was relatively common among participants, with 40.8% reporting that they had one or more pets (\\u003cstrong\\u003eTable 2\\u003c/strong\\u003e). While the survey did not collect species-specific data, the presence of a pet may represent an important, albeit underexplored, source of emotional support and companionship. Given the established association between pet ownership and reduced feelings of loneliness in some subgroups, further analysis may help elucidate whether the protective effects of companion animals vary by age, living arrangement or intensity of social isolation. Inferential findings of the INTERACT cohort are presented in \\u003cstrong\\u003eEl-Osta et al., 2025\\u003c/strong\\u003e\\u003cstrong\\u003eb.\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eTable 2: Respondent characteristics\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable (N = 135,725)\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e16-25\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e8,352\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(6.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e26-35\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e13,057\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(9.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e36-45\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e15,006\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(11.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e46-55\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e22,031\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(16.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e56-65\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e33,235\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(24.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026gt;65\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e43,927\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(32.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e117\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGender\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eFemale\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e83,661\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(61.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMale\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e50,729\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(37.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eWould rather not say\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e498\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOther\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e804\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEthnicity\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eAsian/Asian British\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e5,162\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(3.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eBritish Black/African/Caribbean\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e2,310\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(1.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMixed/Multiple ethnic groups\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e2,025\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(1.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eWhite\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e112,164\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(82.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eWhite and Black Caribbean\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e462\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOther ethnic group\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e2,722\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(2.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,880\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(8.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital status\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eDivorced\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e12,761\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(9.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eIn a relationship\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e12,404\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(9.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMarried / Civil partnership\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e60,612\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(44.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSingle\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e26,983\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(19.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eWidowed\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e9,625\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e2,992\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(2.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,348\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHighest level of education\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eA levels/College\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e36,700\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(27.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSecondary School\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e30,481\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(22.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eUniversity Degree or higher\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e58,008\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(42.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,536\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEmployment status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eEmployed full-time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e38,388\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(28.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eEmployed part-time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e14,604\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(10.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eFurloughed\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e54\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eRetired\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e45,038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(33.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSelf-employed\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e7,608\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(5.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eStudent (full or part-time)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3,733\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(2.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eUnemployed\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e7,629\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(5.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eUnpaid carer\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1,560\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(1.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eVolunteer (full or part-time)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1,459\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(1.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOther\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e4,083\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(3.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e11,569\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(8.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDo you have any pets?\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e55,413\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(40.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e69,895\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(51.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,417\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eNumber of people live in your household other than you\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e0\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e33,489\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(24.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e1\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e52,760\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(38.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e2-3\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e30,407\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(22.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e4-5\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e7,278\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(5.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMore than 5\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e1,494\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(1.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,297\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDo you have any children\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e84,561\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(62.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e40,684\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(30.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,480\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHaving children aged 16 years or under\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e19,287\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(22.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e64,991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(76.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e283\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(0.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDisability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eYes\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e24,915\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(18.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNo\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e96,817\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(71.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003ePrefer not to say\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3,584\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(2.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,409\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLong-term conditions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eYes\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e62,038\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(45.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNo\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e60,260\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(44.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003ePrefer not to say\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3,085\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(2.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,342\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(7.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMain survey findings\\u0026nbsp;\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe main survey findings are presented in \\u003cstrong\\u003eTable 3\\u003c/strong\\u003e.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eUCLA Loneliness Scale and Direct Measure of Loneliness (DMOL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFindings from the UCLA 3-item Loneliness Scale showed substantial levels of social disconnection across the sample (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). When asked how often they felt a lack of companionship, 40.5% responded \\u0026quot;hardly ever or never,\\u0026quot; 37.5% said \\u0026quot;some of the time,\\u0026quot; and 21.9% reported feeling this way \\u0026quot;often\\u0026rdquo;. Similar trends emerged in response to feelings of being left out, with 37.6% indicating they hardly ever or never felt excluded, 41.4% reporting this occurred some of the time and 21.0% stating it happened often. Regarding feelings of isolation, 40.7% reported hardly ever or never feeling isolated, 36.8% felt this way some of the time and 22.5% said they often experienced isolation.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eResults from the DMOL mirrored these findings (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). A total of 16.5% of respondents reported feeling lonely \\u0026quot;often or always,\\u0026quot; while 26.4% experienced loneliness \\u0026quot;some of the time\\u0026quot; and 22.9% \\u0026quot;occasionally\\u0026rdquo;. A smaller proportion, 22.2%, felt lonely \\u0026quot;hardly ever,\\u0026quot; and only 12.0% said they \\u0026quot;never\\u0026quot; felt lonely.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOverall, the data suggest that while loneliness manifests in varying degrees, it is a prevalent experience for the majority of participants (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). More than two-thirds reported feeling lonely at least occasionally and nearly one in six experienced chronic, high-intensity loneliness. These results highlight the importance of recognising loneliness as a widespread and persistent public health concern.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSocial connections among participants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants reported varying frequencies of contact with relatives and friends (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). For relatives, 5.3% reported no contact, while others reported contact with 1 (8.9%), 2 (15.5%), 3-4 (29.2%), 5-8 (21.9%) and 9 or more relatives (11.7%) in the last month. Similarly, for friends 8.8% reported no contact, with others reporting contact with 1 (10.2%), 2 (14.1%), 3-4 (23.3%), 5-8 (18.6%) and 9 or more friends (17.3%).\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003cstrong\\u003eTable 3: Main findings\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eVariable (N = 135,725)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eN\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e(%)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eUCLA Loneliness Scale\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHow often do you feel that you lack companionship (UCLA 1)?\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eHardly ever or never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e55,034\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(40.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSome of the time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e50,897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(37.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOften\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e29,791\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(21.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(\\u0026lt;0.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHow often do you feel left out (UCLA 2)?\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eHardly ever or never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e51,044\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(37.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSome of the time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e56,243\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(41.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOften\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e28,435\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(21.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(\\u0026lt;0.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHow often do you feel isolated from others (UCLA 3)?\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eHardly ever or never\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e55,180\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(40.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSome of the time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e50,009\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(36.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOften\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e30,533\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(22.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(\\u0026lt;0.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDirect Measure of Loneliness (DMOL)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOften or always\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e22,366\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(16.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eSome of the time\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e35,868\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(26.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eOccasionally\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e31,133\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(22.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eHardly ever\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e30,122\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(22.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eNever\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e16,233\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e(\\u0026lt;0.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHow many RELATIVES did you see or hear from in the last month?\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e7,195\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(5.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e1\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e12,101\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(8.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e2\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e20,994\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(15.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e3 or 4\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e39,644\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(29.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e5-8\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e29,656\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(21.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e9 or more\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e15,825\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(11.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,310\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(7.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHow many FRIENDS did you see or hear from in the last month?\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\u003cbr\\u003e\\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e11,936\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(8.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e1\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e13,850\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(10.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e2\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e19,188\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(14.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e3 or 4\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e31,679\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(23.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e5-8\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e25,299\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(18.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e9 or more\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e23,465\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(17.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e10,308\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(7.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\" style=\\\"width: 100px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eThe COVID-19 pandemic and lockdowns made me feel more LONELY\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eStrongly disagree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e18,295\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(13.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eDisagree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e45,953\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(33.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eAgree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e38,662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(28.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eStrongly agree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e21,040\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(15.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e11,775\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(8.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eThe COVID-19 pandemic and lockdown made me feel more ISOLATED\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eStrongly disagree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e17,033\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(12.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eDisagree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e40,937\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(30.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eAgree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e43,513\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(32.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eStrongly agree\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e21,915\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(16.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 78px;\\\"\\u003e\\n \\u003cp\\u003eMissing\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e12,327\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"bottom\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;(9.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u0026apos; perceptions regarding their neighbourhood dynamics, cohesion and trust (Social Capital Scale)\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants\\u0026rsquo; perceptions of their local neighbourhoods revealed a complex and often fragmented picture of social capital (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). When asked whether people in their area were willing to help one another, just over half (51.7%) agreed and only 12.3% strongly agreed, suggesting that while mutual aid exists it may not be deeply embedded. Conversely, more than one in four respondents either disagreed (21.0%) or strongly disagreed (7.3%), indicating significant deficits in perceived neighbourly support.\\u003c/p\\u003e\\n\\u003cp\\u003eViews on community cohesion were similarly mixed (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). Only 6.7% strongly agreed that their neighbourhood was close-knit, with a further 38.1% agreeing. In contrast, 35.7% disagreed and 11.3% strongly disagreed-pointing to a widespread sense of social fragmentation, particularly in urban areas. The ability to turn to neighbours in times of need was also uncertain: while 36.2% believed they could borrow \\u0026pound;30 from a neighbour in an emergency, an almost equal proportion (55.3%) disagreed or strongly disagreed.\\u003c/p\\u003e\\n\\u003cp\\u003ePerceptions of interpersonal trust reflected this ambivalence (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). A total of 58.9% of participants agreed that people in their neighbourhood could be trusted, with only 9.5% strongly agreeing. Meanwhile, 22.7% expressed mistrust, reporting either disagreement (18.1%) or strong disagreement (4.6%) with the statement. When asked whether neighbours would help with basic tasks such as shopping for groceries during illness, just over half (52.5%) felt confident they could rely on local support, but over 38% were doubtful.\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, when asked about shared values, nearly one-third (29.5%) agreed that people in their neighbourhood did not share the same values as them, while 53.1% disagreed (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). This divergence reflects the broader social heterogeneity of contemporary communities and its potential impact on social cohesion.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eCollectively, these findings highlight inconsistent and, in many cases, weak perceptions of local solidarity, trust and mutual support which are important dimensions of social capital that are likely to shape and, in some cases, amplify individuals\\u0026rsquo; experiences of loneliness and isolation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImpact of COVID-19 Pandemic\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDuring the COVID-19 pandemic, participants reported experiencing heightened feelings of loneliness and isolation (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). In terms of loneliness, 15.5% of respondents strongly agreed that the pandemic exacerbated their sense of loneliness, while 28.5% agreed. On the other hand, 13.5% strongly disagreed and 33.9% disagreed with this sentiment. Similarly, regarding isolation, 16.1% strongly agreed that the pandemic intensified their feelings of isolation and 32.1% agreed. In contrast, 12.5% strongly disagreed and 30.2% disagreed with this statement. These findings highlight the varied psychological impacts of the pandemic on individuals\\u0026apos; perceptions of loneliness and isolation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePrevalence of Loneliness and Social Isolation\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSummary statistics for the UCLA Loneliness Scale, DMOLand Social Capital Score are presented in\\u003cstrong\\u003e\\u0026nbsp;Table 4.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 4: Summary statistics for UCLA Loneliness Scale, Direct Measure of Loneliness and Social Capital Score\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMedian\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eIQR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMin.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMax.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 47px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eUCLA Loneliness Scale\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 17px;\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e3 - 7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 10px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 305px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDirect Measure of Loneliness (DMOL)*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e2 - 4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 305px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSocial Capital Scale\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 110px;\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 76px;\\\"\\u003e\\n \\u003cp\\u003e2- 6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 85px;\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\" valign=\\\"top\\\" style=\\\"width: 642px;\\\"\\u003e\\n \\u003cp\\u003eNever=1; Hardly ever=2; Occasionally=3; Some of the time=4; Often or always=5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eUCLA Loneliness Scale\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe prevalence of loneliness in the INTERACT cohort was widespread and heterogeneous (\\u003cstrong\\u003eTables 3 \\u0026amp; 4\\u003c/strong\\u003e). Responses to the UCLA 3-item Loneliness Scale resulted in a median score of 6 (IQR: 3-7), suggesting that most participants experienced at least moderate levels of loneliness; \\u003cstrong\\u003eTable 4\\u003c/strong\\u003e. The scale\\u0026rsquo;s full range (3-9) was observed, reflecting a broad distribution of subjective experiences, from minimal to chronic loneliness. Across the three items, approximately 1 in 5 participants consistently reported feeling \\u0026lsquo;often\\u0026rsquo; lonely, left out or isolated, while the largest proportion felt this way \\u0026ldquo;some of the time\\u0026rdquo; (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eDirect Measure of Loneliness (DMOL)\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe ONS DMOL further provided additional insights into participants\\u0026rsquo; experiences of loneliness and corroborated these trends (\\u003cstrong\\u003eTables 3 \\u0026amp; 4\\u003c/strong\\u003e). The median DMOL score was 3 (IQR: 2-4), corresponding to loneliness felt \\u0026ldquo;occasionally\\u0026rdquo; to \\u0026ldquo;some of the time\\u0026rdquo;. However, 16.5% of respondents reported feeling lonely \\u0026lsquo;often or always\\u0026rsquo;, indicating a substantial burden of persistent loneliness. By contrast, only 12% of participants reported never feeling lonely, highlighting the rarity of complete social connectedness even in this large, diverse sample.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eSocial Connections and Frequency of Contact\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn addition to individual-level risk factors, participants\\u0026rsquo; experiences of loneliness were shaped by neighbourhood context and perceived social support. Perceived social capital, which was measured via a composite index of neighbourhood trust, cohesion and perceived support, also varied widely. The median score was 4 (IQR: 2-6) on a 0-7 scale (\\u003cstrong\\u003eTable 4\\u003c/strong\\u003e). \\u0026nbsp;A small percentage (5.3%) reported no contact with relatives in the last month, while 8.9% had contact with one relative, 15.5% with two relatives and 29.2% with 3-4 relatives (\\u003cstrong\\u003eTable 3\\u003c/strong\\u003e). A similar trend was observed with friends, where 8.8% reported no contact, 10.2% had contact with one friend, 14.1% with two friends and 23.3% with 3-4 friends (\\u003cstrong\\u003eTable 3).\\u0026nbsp;\\u003c/strong\\u003eThus, while some participants perceived their communities as supportive, a considerable proportion indicated weak or fragmented social ties. These patterns were particularly pronounced in urban settings, as elaborated in the geospatial analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eImpact of the COVID-19 Pandemic on Loneliness and Isolation\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants\\u0026rsquo; responses suggest that the COVID-19 pandemic played a significant role in amplifying feelings of loneliness and social isolation. Nearly 44% agreed or strongly agreed that their sense of loneliness increased during the pandemic, while a similar proportion (48.2%) reported heightened feelings of social isolation (\\u003cstrong\\u003eTable 3)\\u003c/strong\\u003e. These effects were not uniform; approximately one-third of participants did not perceive any negative impact, highlighting a differential experience of pandemic-related social disruption.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSubgroup Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDescriptive analysis showed that loneliness was not evenly distributed across the sample. It was most prevalent among young adults (16-25 years), ethnic minority groups and those who were single, divorced or unemployed. Women reported slightly higher levels of loneliness than men and participants from Asian/Asian British backgrounds reported greater loneliness than White participants. These patterns suggest the importance of age, relationship status, ethnicity and socioeconomic context in shaping experiences of loneliness. Detailed multivariable and interaction analyses are presented in \\u003cstrong\\u003eEl-Osta, et al., 2025. b\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGeospatial Patterns of Loneliness\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eGeospatial analysis highlighted striking regional variation in reported loneliness across England. Kernel density heat maps generated from participants\\u0026rsquo; postcode-level data highlighted distinct urban \\u0026ldquo;hotspots\\u0026rdquo; with elevated loneliness scores. As shown in \\u003cstrong\\u003eFigure 1\\u003c/strong\\u003e, major metropolitan areas, including London, Birmingham and Manchester exhibited the highest concentrations of individuals reporting frequent loneliness as measured by both the UCLA 3-item scale and DMOL. In contrast, rural and semi-rural areas in the South West and East of England tended to report lower levels of loneliness, reflecting greater community cohesion and higher social capital scores in these regions. These spatial differences persisted even when stratified by age and social capital indicators, reinforcing the notion of an \\u0026ldquo;urban paradox\\u0026rdquo;: densely populated areas characterised by weak interpersonal ties, social anonymity and reduced trust.\\u003c/p\\u003e\\n\\u003cp\\u003eThe visualisation of loneliness at the LSOA level provides a powerful tool for identifying areas with concentrated social disconnection. This spatial lens offers actionable insight for local authorities and health systems aiming to prioritise place-based interventions. While the present map serves as a proof-of-concept, a dedicated geospatial analysis incorporating indices of deprivation, digital exclusion and service accessibility is planned in a follow-up manuscript.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study presents the largest and most comprehensive descriptive epidemiological investigation of loneliness and social disconnection undertaken in the United Kingdom to date. Drawing on data from over 135,000 community-dwelling adults, the INTERACT study reveals that loneliness is a widespread and deeply patterned phenomenon, affecting individuals across the life course and social spectrum.\\u003c/p\\u003e \\u003cp\\u003eRemarkably, we found that 16.5% of participants reported feeling lonely \\u0026ldquo;often or always,\\u0026rdquo; while more than two-thirds indicated experiencing loneliness at least occasionally. These rates are substantially higher than previously estimated in UK national statistics and suggest a critical public health burden[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Our results also confirm that loneliness is not confined to older adults, a widely held misconception in public discourse. Instead, younger adults (16\\u0026ndash;25 years) reported the highest levels of loneliness, aligning with emerging evidence that loneliness peaks in adolescence and early adulthood[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWhile our data show that loneliness is most prevalent among younger adults, we also identified a substantial burden of loneliness among older adults aged 65 and above, who comprised the largest age group in our cohort (32%). Approximately 17% of participants in this age group reported severe loneliness on the UCLA scale and over one-third experienced loneliness at least occasionally according to the DMOL. These findings are especially concerning given the established association between loneliness and adverse outcomes in later life, including increased risk of cognitive decline, cardiovascular disease, functional impairment and premature mortality[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eInterestingly, despite high absolute numbers, older adults reported lower loneliness scores on average compared to younger cohorts, suggesting a potential resilience effect, possibly linked to stronger existing social ties, more stable relationships or better coping strategies. However, this apparent resilience may mask important subgroup variation. For example, widowed individuals, those living alone and those with long-term health conditions or disabilities were significantly more likely to report chronic loneliness.\\u003c/p\\u003e \\u003cp\\u003ePrevious studies have highlighted how cultural and structural factors, including language barriers, social discrimination and weaker community networks, may contribute to higher loneliness among ethnic minorities[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Ethnic minority groups, particularly Asian/Asian British participants in our study reported significantly higher loneliness scores compared to White participants. This highlights the need for culturally sensitive interventions, particularly within urban areas where minority populations are more concentrated.\\u003c/p\\u003e \\u003cp\\u003eOur geospatial analysis revealed striking geographic disparities in the distribution of loneliness across the UK with pronounced \\u0026ldquo;hotspots\\u0026rdquo; concentrated in major urban centres such as London, Birmingham and Manchester. These findings reinforce the growing body of literature describing the \\u0026lsquo;urban paradox\\u0026rsquo; - the counterintuitive phenomenon in which high population density does not equate to greater social connectedness. Despite physical proximity, urban environments may foster fragmentation, social anonymity and reduced trust, especially in areas with lower social capital[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Conversely, rural and semi-rural areas appeared to exhibit greater social cohesion and lower levels of loneliness, potentially due to stronger intergenerational ties and community integration[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. These results highlight the need for place-based interventions that account for spatial context, particularly in post-pandemic urban recovery efforts. Local authorities and public health teams can leverage heat mapping tools, such as those demonstrated in this study, to prioritise and tailor interventions in high-burden areas. The heat maps presented here offer only a preliminary view to demonstrate a proof-of-concept. A dedicated follow-up manuscript is planned to expand this analysis in detail, integrating additional geodemographic, deprivation and service accessibility variables to further understand the spatial epidemiology of loneliness and guide targeted action.\\u003c/p\\u003e \\u003cp\\u003eWhile loneliness in older adults has been extensively studied, younger populations may face unique socio-emotional challenges, including academic pressure, employment instability and social comparison via digital platforms[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. Collectively, these findings highlight the dual reality that while younger adults may be at greatest risk, older adults remain a critical group for intervention, particularly in the context of multimorbidity, bereavement and structural disconnection.\\u003c/p\\u003e \\u003cp\\u003eOur study expands on existing research by combining validated loneliness scales with a large, demographically diverse sample, allowing for more granular insights. Previous large-scale studies including the English Longitudinal Study of Ageing (ELSA), have focused predominantly on older adults[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e], whereas INTERACT provides a broader, population-wide perspective. Moreover, our findings align with the UK Government's Loneliness Strategy (2018), which identified single and divorced individuals as high-risk groups. In our study, single and divorced individuals exhibited significantly higher loneliness scores compared to married participants, reaffirming the role of relationship status as a protective factor against loneliness[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThat the COVID-19 pandemic further amplified loneliness levels, with 44% of our study respondents reporting that pandemic-related restrictions increased their feelings of loneliness, aligns with findings from Bu et al. in 2020[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e], who demonstrated that social distancing measures disproportionately affected individuals with pre-existing loneliness. Clearly, the long-term mental health consequences of pandemic-related social isolation remain an urgent research priority, particularly as societies transition into post-pandemic recovery phases[\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec27\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and limitations\\u003c/h2\\u003e \\u003cp\\u003eThe INTERACT Study represents the largest population-based investigation of loneliness, social isolation and social capital ever conducted in the United Kingdom, with over 135,000 community-dwelling adults participating. This unprecedented sample size is a major strength, enabling subgroup analysis across age, gender, ethnicity, socioeconomic status and health-related variables with sufficient statistical power to detect meaningful differences. Furthermore, the study\\u0026rsquo;s diverse, multi-channel recruitment strategy, including NHS primary care networks, voluntary sector partners, social media and research platforms such as the NIHR Be Part of Research Network enhanced the inclusivity and demographic reach of the sample, improving its national relevance.\\u003c/p\\u003e \\u003cp\\u003eMethodologically, the study is grounded in the use of validated instruments, including the 3-item UCLA Loneliness Scale and the ONS DMOL, both of which ensure alignment with existing literature and facilitate benchmarking against national and international datasets. Importantly, the integration of social capital indicators, assessing trust, cohesion and perceived neighbourhood support, enables a multidimensional understanding of loneliness, moving beyond individual-level explanations to consider the influence of structural and contextual factors. The geospatial kernel density mapping conducted at the LSOA level adds a novel spatial dimension, allowing the identification of loneliness \\u0026ldquo;hotspots\\u0026rdquo; and providing actionable insight for local authorities and health systems interested in place-based interventions.\\u003c/p\\u003e \\u003cp\\u003eThe principal limitation of this study is concerned with its cross-sectional design that precludes any inference of causality, meaning we cannot determine the temporal direction of observed associations between loneliness and sociodemographic characteristics. Longitudinal follow-up will be essential to understand how loneliness evolves over time, especially in the context of life-course transitions, bereavement, chronic illness and social mobility.\\u003c/p\\u003e \\u003cp\\u003eSecond, the study relied exclusively on self-reported data, which may be influenced by social desirability or recall bias. Although loneliness is inherently subjective, participants may underreport feelings of disconnection due to stigma or perceived social expectations, particularly in certain cultural or gender groups.\\u003c/p\\u003e \\u003cp\\u003eThird, while the sample is large and diverse, sampling bias may persist. Recruitment via NHS and digital platforms may have inadvertently excluded individuals with limited healthcare access (e.g. homeless or undocumented populations), low digital literacy or those who are socially isolated to the point of disengagement from community or institutional networks. Given the intended final sample of INTERACT is 500,000 participants across the UK, for reasons of pragmatism the survey was administered online raising concerns about the underrepresentation of digitally excluded individuals, particularly older adults, those in rural areas and people with disabilities. Future manuscripts will report the findings of subgroup analyses including people experiencing homelessness, undocumented populations and traditionally hard-to-reach groups including care homes and carers.\\u003c/p\\u003e \\u003cp\\u003eFourth, although this baseline paper focuses on descriptive analysis, missing data were non-trivial approximating 7%. We addressed this comprehensively in Paper 2 (\\u003cb\\u003eEl-Osta et al., 2025b\\u003c/b\\u003e) using Multiple Imputation by Chained Equations (MICE) to ensure more complete and unbiased estimates in subsequent regression analyses. Despite this, we acknowledge that complete case bias cannot be fully ruled out in descriptive reporting.\\u003c/p\\u003e \\u003cp\\u003eWe also acknowledge the potential impact of seasonality on reported loneliness. Data collection for the INTERACT Study already spanned multiple seasons, including winter months when loneliness and social isolation are typically more pronounced due to shorter daylight hours, adverse weather conditions and reduced social activity. While this variability enhances ecological validity, it may also introduce temporal bias as responses could reflect transient mood states influenced by environmental or cultural factors (e.g. holidays, school breaks or lockdown anniversaries). Our future analyses will explore seasonal variation explicitly or control for time-of-response effects in longitudinal extensions of this work.\\u003c/p\\u003e \\u003cp\\u003eFinally, the COVID-19 pandemic context in which a portion of the data was collected may have influenced responses, with the effect of either inflating or dampening reported loneliness depending on participants\\u0026rsquo; stage of recovery, adaptation or coping. While this reflects real-world conditions and lived experience, it should be interpreted with care when comparing results to pre-pandemic or international cohorts.\\u003c/p\\u003e \\u003cp\\u003eDespite these limitations, the INTERACT Study\\u0026rsquo;s methodological strengths of unprecedented scale, validated tools, geospatial design and conceptual breadth, position it as a foundational platform for more detailed inferential (Paper 2) and psychometric (Paper 3) investigations. The study establishes a robust baseline for loneliness surveillance in England and provides a critical springboard for evidence-based public health policy, research and intervention design.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec28\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplications for Public Health and Policy\\u003c/h2\\u003e \\u003cp\\u003eThe INTERACT Study offers critical insights for policymakers, public health leaders and community planners seeking to address the growing burden of loneliness in the UK. Its scale and spatial precision provide a foundation for more targeted, data-driven and equitable approaches to intervention design and delivery.\\u003c/p\\u003e \\u003cp\\u003eThis study challenges prevailing narratives by demonstrating that loneliness is not confined to older adults. The highest levels of loneliness were observed among young adults, particularly those facing insecure employment, social comparison and digital overexposure. Interventions must therefore adopt a life-course perspective, addressing the distinct needs of younger populations as well as those of older adults. For young people, this could include digital wellbeing initiatives, peer mentoring and access to inclusive community hubs[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. For older adults, it may mean tackling mobility, bereavement and access barriers. Further, given the high burden of loneliness among ethnic minorities, policies should prioritise culturally inclusive initiatives, such as multilingual community programs, minority-focused support networks and interventions addressing structural discrimination.\\u003c/p\\u003e \\u003cp\\u003eThe geospatial findings illustrate the value of place-based interventions. Loneliness \\u0026ldquo;hotspots\\u0026rdquo; were concentrated in urban centres marked by high density but low social capital. Local authorities and Integrated Care Systems can use spatial data to prioritise resources, develop community infrastructure and embed loneliness-reduction strategies into broader health equity agendas. Investments in intergenerational centres, neighbourhood initiatives and public realm improvements can foster greater social connection and reduce isolation in urban areas. This is particularly relevant in the context of post-pandemic urban recovery, where the reimagining of public spaces could promote greater social interactions[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe pandemic\\u0026rsquo;s amplifying effects on loneliness emphasise the urgency of embedding resilience and social support into public health recovery efforts. Policymakers should consider how future public health emergencies might exacerbate social disconnection and plan proactively to mitigate those impacts.\\u003c/p\\u003e \\u003cp\\u003eThe strong association between low social capital and high loneliness prevalence emphasises the importance of shifting from purely individual-level interventions (e.g. social prescribing) toward community-level investment in trust, cohesion and civic infrastructure. Programmes that build shared identity such as intergenerational community centres, co-housing schemes and neighbourhood-based volunteering, can help to reverse the erosion of social cohesion in densely populated urban areas. These interventions should be co-designed with residents, particularly in the loneliness \\u0026ldquo;hotspots\\u0026rdquo; identified through this study\\u0026rsquo;s geospatial analysis. This is the remit of a future output by the same authors earmarked for publication in 2026.\\u003c/p\\u003e \\u003cp\\u003eFinally, the inclusion of social capital measures within the INTERACT tool presents a model for future surveillance. By incorporating indicators of trust, cohesion and perceived support, future surveys can move beyond individual-level loneliness to track the health of community networks more holistically. National frameworks such as the UK Government\\u0026rsquo;s Loneliness Strategy and ONS wellbeing monitoring could be enhanced by routinely measuring social capital.\\u003c/p\\u003e \\u003cp\\u003eCrucially, INTERACT provides a scalable template for global adaptation. As the health and economic costs of loneliness gain recognition internationally from WHO\\u0026rsquo;s Commission on Social Connection to OECD\\u0026rsquo;s wellbeing framework, there is an urgent need for cross-cultural, psychometrically robust tools that can be deployed across settings. The INTERACT platform offers a promising model for global surveillance and intervention design, especially in ageing societies where loneliness and isolation are rapidly becoming endemic.\\u003c/p\\u003e \\u003cp\\u003eTaken together, the INTERACT findings suggest loneliness should be reframed as a population health issue, one shaped by policy decisions, community design and the quality of our social fabric. Addressing it will require a whole-systems approach that cuts across health, education, housing and urban planning. The INTERACT platform provides a scalable, evidence-based model to guide such strategies and inform both national and international action on loneliness and social wellbeing.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec29\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eFuture Research Directions\\u003c/h2\\u003e \\u003cp\\u003eThis baseline study lays the groundwork for an ambitious programme of research to better understand, monitor and address loneliness across populations, settings and cultural contexts. Building on the descriptive epidemiology presented here, future analyses will explore longitudinal patterns, causal pathways and intervention impact using advanced statistical methods and psychometric refinements as presented in Papers 2 (\\u003cb\\u003eEl-Osta et al., 2025b\\u003c/b\\u003e) \\u0026amp; Paper 3 (\\u003cb\\u003eTristan et al., 2025\\u003c/b\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe next phases of the INTERACT programme include longitudinal follow-up to assess how loneliness evolves over time, particularly in response to life transitions, health status changes and social mobility. New data currently being collected among NHS staff (representing the occupational health cohort) will explore loneliness, burnout and team cohesion among healthcare professionals. In-depth interviews are also planned with subgroups most affected by loneliness, including young adults, ethnic minorities, people with disabilities, care home residents, carers and those experiencing homelessness to understand lived experience and inform co-designed interventions. Further psychometric testing and Rasch analysis will continue to strengthen the validity and reliability of the INTERACT scale. These efforts will support its adaptation for use in new populations and geographies, forming the basis for an internationally harmonised loneliness surveillance framework.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe INTERACT Study represents the most extensive mapping of loneliness and social capital in the UK to date. With over 135,000 participants, the findings highlight high levels of social disconnection and reveal stark disparities by age, ethnicity, geography and health status. This evidence challenges outdated assumptions about who is lonely and why, urging a reframing of loneliness as a structural and systemic issue. The insights offer a critical foundation for equity-focused, place-based public health strategies that prioritise social connection. As the global burden of loneliness rises, INTERACT provides a scalable model for action, combining robust measurement, community engagement and geospatial intelligence to support better health, wellbeing and social cohesion for all.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eConsent for publication\\u003c/h2\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\n\\u003cp\\u003eThis research received no funding. Austen El-Osta is grateful for support from the National Institute for Health and Care Research (NIHR) Applied Research Collaboration NorthWest London. The views expressed in this article are those of the authors and not necessarily those of the NIHR or Department of Health and Social Care.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\n\\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\\n\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\n\\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 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\\n\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\n\\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\\n\\u003cli\\u003ePerlman, D. and L. 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Cross-cohort analyses of predictors of loneliness before and during the COVID-19 pandemic.\\u003c/em\\u003e Public Health, 2020. \\u003cstrong\\u003e186\\u003c/strong\\u003e: p. 31-34.\\u003c/li\\u003e\\n\\u003cli\\u003eKeles, B., M. Niall, and A. and Grealish, \\u003cem\\u003eA systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents.\\u003c/em\\u003e International Journal of Adolescence and Youth, 2020. \\u003cstrong\\u003e25\\u003c/strong\\u003e(1): p. 79-93.\\u003c/li\\u003e\\n\\u003cli\\u003eMagid, K., et al., \\u003cem\\u003eThe Impact of Digital Mental Health Services on Loneliness and Mental Health: Results from a Prospective, Observational Study.\\u003c/em\\u003e Int J Behav Med, 2024. \\u003cstrong\\u003e31\\u003c/strong\\u003e(3): p. 468-478.\\u003c/li\\u003e\\n\\u003cli\\u003eQi, J., S. Mazumdar, and A.C. Vasconcelos, \\u003cem\\u003eUnderstanding the Relationship between Urban Public Space and Social Cohesion: A Systematic Review.\\u003c/em\\u003e International Journal of Community Well-Being, 2024. \\u003cstrong\\u003e7\\u003c/strong\\u003e(2): p. 155-212.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Y., et al., \\u003cem\\u003eSocial Interaction in Public Spaces and Well-Being among Elderly Women: Towards Age-Friendly Urban Environments.\\u003c/em\\u003e Int J Environ Res Public Health, 2022. \\u003cstrong\\u003e19\\u003c/strong\\u003e(2).\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":true,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-global-and-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)\",\"snPcode\":\"44263\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44263/3\",\"title\":\"BMC Global and Public Health\",\"twitterHandle\":\"@BMC_GPH\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Loneliness, Social isolation, Social connection, Public health, Mental health, Social capital, Community health, COVID-19\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6864072/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6864072/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e\\u003cbr\\u003e\\nLoneliness is increasingly recognised as a major public health challenge with significant implications for mental, physical and social wellbeing. Despite growing interest, population-level data remains limited, particularly at the intersection of individual, community and geographic determinants. The Measuring Loneliness in the UK (INTERACT) Study was designed to map the prevalence, intensity and sociodemographic determinants of loneliness across diverse population groups in the UK. The aim of this first paper in a series is to describe the development, implementation and early findings of the INTERACT Survey, which to date is the largest population-based study of loneliness, social isolation and social capital conducted in the UK.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e\\u003cbr\\u003e\\nBetween March and July 2023, 135,725 adults completed the online INTERACT Survey. The instrument included validated measures of loneliness (UCLA-3 \\u0026amp; ONS Direct Measure), social capital indicators and demographic variables. Descriptive statistics were stratified by key subgroups. A novel geospatial analysis at Lower Super Output Area (LSOA) level was used to visualise clustering of loneliness across the UK.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e\\u003cbr\\u003e\\nLoneliness was widespread, with 16.5% of participants reporting they often or always felt lonely. Younger adults, individuals from minority ethnic backgrounds, those who were single or unemployed and people with disabilities were more likely to report frequent loneliness. Social capital varied widely, with lower scores in urban areas and among groups with greater reported loneliness. The COVID-19 pandemic was reported as an amplifying factor, with 44% of respondents indicating increased loneliness during the pandemic. Geospatial mapping revealed distinct loneliness “hotspots” in densely populated urban regions, particularly London, Birmingham and Manchester.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e\\u003cbr\\u003e\\nThe INTERACT Study provides a comprehensive national dataset on loneliness and social disconnection in the UK. Its scale, methodological rigour and spatial granularity offer valuable insight for designing targeted, place-based interventions. Future papers will present inferential analyses, explore lived experience and propose policy-relevant solutions to mitigate loneliness and promote social connection across communities.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Measuring Loneliness at an Unprecedented Scale: The INTERACT Study’s Approach and Initial Findings\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-07-02 13:25:30\",\"doi\":\"10.21203/rs.3.rs-6864072/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2025-09-03T07:45:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-31T21:11:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-08-04T15:53:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"60858965845866142746006982219925414135\",\"date\":\"2025-07-04T12:12:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"197280555797044816036205148719662125353\",\"date\":\"2025-06-18T15:17:22+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-06-18T11:38:06+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-06-11T06:16:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-06-11T06:08:04+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Global and Public Health\",\"date\":\"2025-06-10T14:19:05+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-global-and-public-health\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [BMC Global and Public Health](https://bmcglobalpublichealth.biomedcentral.com/)\",\"snPcode\":\"44263\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/44263/3\",\"title\":\"BMC Global and Public Health\",\"twitterHandle\":\"@BMC_GPH\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c21953ee-b873-41ab-99d8-e037407d5342\",\"owner\":[],\"postedDate\":\"July 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-13T05:53:45+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-07-02 13:25:30\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6864072\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6864072\",\"identity\":\"rs-6864072\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}