Road to Heart Health: Paved with Good Intentions?

Road to Heart Health: Paved with Good Intentions? | 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 Road to Heart Health: Paved with Good Intentions? Justin Farmer, Lucía D Macchia, Feifei Bu, Jessica Gong, Andew Steptoe, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8196033/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims: Cardiovascular disease (CVD) is the leading cause of death worldwide. Prosociality, defined as positive other-regarding intentions and behaviours, is associated with positive health outcomes; however, few studies have evaluated its relationship with CVD. This study examines whether prosocial behaviours (formal volunteering and informal helping), intentions, and a combination of these, are associated with reduced CVD risk. Methods Data are from 6,549 adults in the English Longitudinal Study of Ageing, who were free of CVD at baseline. A prosocial intentions scale was derived from items assessing altruism and collectivism. Incident stroke and ischemic heart disease events were identified via medical and mortality record linkages. Cox proportional hazards models assessed risk of developing CVD in relation to prosocial intentions and behaviours, accounting for sociodemographic and health-related covariates. Results 960 incident CVD cases were identified during 11 years of follow-up. Frequent volunteers had an 18% lower CVD risk than those who never volunteer in fully adjusted models (95% confidence interval [CI] = 0.70. 0.98). Informal helping was also associated with lower CVD risk (per additional helping behaviour, hazard ratio (HR) = 0.96 (95% CI: 0.92, 1.01). Having higher prosocial intentions was also weakly associated with lower CVD risk. Individuals who engaged in multiple forms of prosociality had a lower risk of CVD, including a 30% decrease (95% CI: 0.54, 0.89) for those engaging in 3 vs. 0 forms. Conclusions Diverse aspects of prosociality, including formal and informal behaviours and having an overall orientation toward being prosocial may be protective for CVD. Figures Figure 1 Introduction While risk factor identification and treatment has improved in recent decades, cardiovascular diseases (CVD) including ischemic heart disease and stroke remain leading causes of death globally [ 1 ]. By recent estimates, almost 600 million individuals currently have CVD and this number is expected to rise as the global population ages.[ 2 ] This growing burden is not shared equitably, with more socioeconomically disadvantaged individuals disproportionately likely to develop and die from CVD [ 2 , 3 ]. Thus, identifying additional risk and protective factors that influence the development of CVD, and subgroups who may benefit the most from these factors, is a priority for prevention efforts. A growing literature identifies psychosocial factors such as stress and distress, social connectedness, and psychological well-being as important potential targets for interventions to reduce CVD risk [ 4 – 7 ]. Beyond their influence on downstream health behaviours (e.g., smoking, exercise), these factors have been linked to changes in relevant biological processes including increased inflammation and dysregulation of hypothalamic-pituitary-adrenal axis function, implicated in the etiology of CVD [ 8 ]. For instance, recent meta-analyses of prospective studies found individuals with a history of depression have a 30% higher risk of developing coronary heart disease and a 34% higher risk of having a stroke [ 9 , 10 ]. Other work suggests that psychosocial stress may not impact all groups equally, with a recent systematic review finding women are more susceptible than men to psychosocial stress-related health harms, underscoring the need to consider how risk factors and their impacts vary across subgroups [ 11 ]. One psychosocial factor whose health effects are underexplored is prosociality, which broadly refers to intentions and behaviours that prioritize the needs of others [ 12 ]. Prosociality has been linked to lower all-cause mortality [ 13 ], lower pain interference [ 14 ], and lower risk of dementia [ 15 ]. While some studies have found specific prosocial behaviours such as formal volunteering and providing informal help to others are associated with lower risk of CVD and related risk conditions like hypertension [ 16 – 19 ], findings have been inconsistent [ 20 , 21 ]. A limitation of prior work is its reliance on self-reported measures for both prosociality and CVD, leading to concerns about potential bias. This work has also largely examined specific prosocial behaviours in isolation without considering that individuals may engage in multiple forms of prosociality simultaneously, including enacting additional prosocial behaviours and experiencing upstream cognitive and affective motivations such as prosocial intentions and compassion, the latter of which has demonstrated associations with lower levels of inflammation and blood pressure, known risk factors for CVD [ 22 , 23 ], and may modify the health effects of prosocial behaviors [ 24 ]. If certain forms of prosociality are more relevant for health, failing to consider multiple forms simultaneously may underestimate the total health impact of prosociality or misattribute observed effects to the wrong form. Furthermore, whether individuals derive additional health benefits from enacting and experiencing multiple forms of prosociality remains untested. To date, only one study examined different helping behaviours simultaneously in relation to self-reported CVD. Findings pointed to gender differences across associations: volunteering was associated with lower CVD risk in women only, while informal helping was associated with lower CVD risk in men only [ 16 ]. One other limitation of prior work is that few studies have accounted for depression, which precludes understanding if effects of prosociality represent more than just the absence of negative mental states or poor functioning, an important concern given strong linkages between depression and CVD. Thus, a stronger understanding of the role of prosociality in CVD risk will be obtained with research examining multiple forms of prosociality, using objectively measured disease endpoints, and accounting for depression and other confounders. This study begins to address these issues by using a large, nationally representative sample to evaluate several hypotheses. First, we hypothesize prosociality in any form will be associated with reduced CVD risk. We consider two behaviours (formal volunteering and informal helping), prosocial intentions, and the possibility that engaging in more forms of prosociality will increase the magnitude of these associations. We further hypothesize that associations will be especially strong in those with higher prosocial intentions and female participants [ 23 ]. With exploratory analyses, we evaluate the potential modifying effects of age and personal wealth as older adults and individuals with lower resources are disproportionately affected by CVD. Methods Study Participants and Protocol. Data are from the English Longitudinal Study of Ageing (ELSA) core sample, a nationally representative panel study of English adults aged 50 and over living in private households [ 25 ]. ELSA data has been collected biennially since 2002 with participants providing information on health and well-being, social activities, and economic resources through computer-assisted personal interviews. The study sample is periodically replenished to ensure the full age range continues to be represented. We used Wave 4 (2008–2009) as our baseline as it is the only wave during which data on all exposures of interest were collected. Participants were followed through Wave 9 (2018–2019) for a total of 11-years of possible follow up time. Among 9,886 Wave 4 core participants, our analytic sample excludes those who did not consent to medical record data linkage (n = 1,490), those with prevalent CVD at baseline (n = 1,415), those who reported being “Permanently sick or disabled” at baseline (n = 316), and those with incomplete covariate data (n = 330), yielding a sample of 6,549 participants with any of the prosociality measures (see Appendix 1 for more details). Inclusion criteria required individuals have any measure of prosociality, while individuals missing data on some prosociality measures were not excluded; thus, the analytic sample differs slightly for each exposure. All ELSA participants provide written informed consent, and the study was approved by the UK National Research Ethics Service. Researchers may request all ELSA data through the UK Data Service ( https://ukdataservice.ac.uk/use-data.aspx ). The current project met criteria for Institutional Review Board exception. Measures Prosocial Intentions. A 9-item scale using items from validated self-report measures of altruism and collectivism administered in ELSA at Wave 4 only [ 26 , 27 ]. These items were previously selected and modified for the ELSA population, and in prior work predicted better cognitive function.[ 15 ] Individuals missing ≥ 50% of items were excluded; for participants missing < 50% of the items (8.9%), missing values were imputed with the individual’s mean score [ 28 ]. We then derived an overall score summing the 9 items and standardizing the total, with higher scores reflecting higher prosocial intentions. Formal Volunteering. Participants were asked to self-report how often they performed any voluntary work, with response options ranging from “never” to “twice a month or more.” We categorized these responses by considering those who reported volunteering “about once a month” or more as frequently volunteering, those volunteering between “less than once a year” and “every few months” as occasionally volunteering, and those reporting “never” as never volunteering. We also categorized volunteering as a dichotomous exposure (any versus never volunteering). Informal Helping. Participants additionally answered the following question and were invited to endorse any of the 10 listed helping activities in which they had engaged: “In the last 12 months, have you done any of these things, unpaid, for someone who was not a relative?” with activities including items such as “Cooking, cleaning, laundry, gardening or other routine household jobs.” For the full list of items see Table S1 . The number of items endorsed were summed for a possible range of 0–10. Primary analyses use this as a continuous measure while sensitivity analyses dichotomize this variable (“Yes” if endorsed at least one behaviour; “No” otherwise). Cardiovascular Disease. Adjudicated CVD cases were identified through medical and mortality records. Medical records from the Admitted Patient Care data from the National Health Service’s Hospital Episodes Statistics were linked to consenting ELSA participants. Individuals with a record of any of the following ICD-10 codes were considered a CVD case: I21-I25 (ischemic heart diseases) and I60-I69 (cerebrovascular diseases), with the date of the first such incident considered the time of event. Individuals with events that preceded their baseline interview were excluded from analyses. These records were supplemented by CVD mortality cases identified by UK NHS mortality records. Covariates. Baseline covariates (Wave 4) for the main analyses were selected based on a review of literature as factors that could be associated with both prosociality and CVD, but which were unlikely to be on the causal pathway between them. Self-reported demographic factors included age, sex, ethnicity (characterized by ELSA as “White” and “Other”), marital status (married, single, divorced, widowed), and socioeconomic factors characterized by educational attainment, work status, and wealth. Educational attainment was categorized according to number of years of formal education: 1) No formal qualifications; 2) School Certificate level; 3) A levels or equivalent; 4) University degree or higher. Work status was categorized as employed, retired, or otherwise out of the work force. Wealth was derived by summing up the value of reported possessions and assets and subtracting reported open mortgages and payments. The sum was then divided into deciles for interpretability. While health factors may confound or mediate the associations of interest, we added baseline health factors to the main models to mitigate concerns about potential confounding. These include probable depression (reporting at least 3 symptoms on the Center for Epidemiological Studies—Depression Scale) [ 29 ], self-reported doctor diagnosed diabetes, hypertension, and hypercholesterolemia; and relevant health behaviours including self-reported current smoking, physical activity level, alcohol use, and healthy eating. Physical activity level was categorized by ELSA as sedentary, low, medium, and high using a validated approach based on self-reported frequency of vigorous (e.g., running), moderate (e.g., gardening), and mild (e.g., laundry) physical activity [ 30 ]. Based on U.K. medical guidelines, moderate alcohol use was dichotomized as drinking 1–14 glasses per week vs. other (0 or > 14 glasses) and healthy eating was classified as having vs. not having at least 5 servings of fruits or vegetables per day [ 31 ]. Statistical Analysis. Descriptive analyses compared covariates across tertiles of prosocial intentions, and by formal volunteering and informal helping status. We conducted separate Cox proportional hazard analyses to compare the hazard of developing CVD for each prosociality measure. Follow up time was defined as the amount of time from Wave 4 survey until CVD diagnosis, death, or the date of the most recently completed ELSA survey. Potential confounders were sequentially added to models in sets related to demographics, socioeconomic characteristics, and baseline health characteristics: model 1 was unadjusted; model 2 adjusted for age, sex, ethnicity, and marital status; model 3 additionally adjusted for educational attainment, work status, and wealth. Model 4 additionally adjusted for baseline health characteristics including depression, diabetes, hypertension, hypercholesteremia, physical activity, smoking, alcohol use, and diet. When evaluating associations with prosocial behaviours, in a final model, we further adjusted for prosocial intentions (Model 5). We additionally examined how being prosocial in multiple forms (range = 0–3), defined as engaging in any volunteering, engaging in any informal helping, and being in the top two tertiles of prosocial intentions, relate to risk of CVD. Furthermore, we explored prosocial intentions modified the association between each prosocial behavior and risk of CVD through use of interaction terms and stratified analyses. We conducted additional analyses to evaluate the robustness of our findings and identify if any subgroups particularly benefit from prosociality. First, we considered these measures of prosociality simultaneously, by treating them as independent exposures in the same model. Next, to reduce concerns about reverse causality (i.e., underlying heart problems constrained individuals in ways that make it harder to engage in prosocial behaviour), we re-ran all models excluding the first two years of follow-up. Third, we explored measurement alternatives for all prosociality measures: treating prosocial intentions as tertiles (low, medium, and high) and treating formal volunteering and informal helping each as dichotomous variables (any volunteering/ helping vs. no volunteering/helping). Finally, to identify whether specific subgroups may benefit from prosociality, we explored effect measure modification by age, sex, or wealth through the inclusion of interaction terms and by conducting stratified analyses. Results A total of 6,549 participants were included in descriptive analyses, while sample sizes for survival analyses ranged from 5,783 to 6,460 depending on availability of data for each prosociality measure. The sample consisted of 56.4% females, 97.7% white participants, and had a mean age of 66.2 years at baseline. Descriptive statistics suggest that individuals with higher vs. lower prosocial intentions were more likely to be female, were younger, more highly educated, more likely to be employed, had higher total wealth at baseline, and lower prevalence of baseline health conditions. Individuals with high prosocial intentions were also more likely to volunteer and to engage in more informal helping behaviours (Table 1 , Table S2). Different aspects of prosociality were weakly positively correlated, with the highest correlation ( \(\:\rho\:\) =0.30) between formal volunteering and informal helping behaviour (Table S3). Table 1 Characteristics of Sample by Prosocial Intentions Prosocial Intentions Tertile Low Medium High Missing n 1886 1824 2073 766 Prosocial Intentions 16.65 (3.39) 22.60 (1.13) 27.73 (2.59) NA (NA) Volunteering Status (%) No Volunteering 1420 (75.3) 1211 (66.4) 1261 (60.8) 533 (69.6) Volunteering 466 (24.7) 613 (33.6) 811 (39.1) 145 (18.9) Missing 0 (0.0) 0 (0.0) 1 (0.0) 88 (11.5) Informal Helping (# of behaviours) 0.87 (1.25) 1.19 (1.50) 1.59 (1.85) 0.92 (1.51) Missing 0 (0.0) 3 (0.2) 0 (0.0) 96 (12.5) Sociodemographics Sex (%) Female 963 (51.1) 1033 (56.6) 1268 (61.2) 429 (56.0) Male 923 (48.9) 791 (43.4) 805 (38.8) 337 (44.0) Age 66.92 (9.47) 65.74 (9.08) 65.01 (8.95) 68.85 (11.86) White (%) 1851 (98.1) 1793 (98.3) 2042 (98.5) 711 (92.8) Marital Status (%) Married / Partnered 1300 (68.9) 1269 (69.6) 1464 (70.6) 357 (46.6) Divorced / Separated 187 (9.9) 201 (11.0) 237 (11.4) 136 (17.8) Never Married 130 (6.9) 102 (5.6) 83 (4.0) 58 (7.6) Widowed 269 (14.3) 252 (13.8) 289 (13.9) 215 (28.1) Educational Attainment (%) No Formal Qualifications 551 (29.2) 418 (22.9) 426 (20.5) 291 (38.0) School Certificate Level 456 (24.2) 447 (24.5) 474 (22.9) 150 (19.6) A Levels or Equivalent 417 (22.1) 458 (25.1) 554 (26.7) 159 (20.8) University Degree or Higher 462 (24.5) 501 (27.5) 619 (29.9) 166 (21.7) Work Status (%) Employed 615 (32.6) 662 (36.3) 805 (38.8) 257 (33.6) Looking after home 107 (5.7) 131 (7.2) 144 (6.9) 56 (7.3) Retired 1135 (60.2) 1010 (55.4) 1108 (53.4) 447 (58.4) Unemployed 29 (1.5) 21 (1.2) 16 (0.8) 6 (0.8) Wealth (£) 304,849.98 (366,100.91) 363,760.86 (498,804.58) 397,819.94 (871,232.24) 288,139.94 (1,455,892.71) Health Factors Diabetes (%) 138 (7.3) 112 (6.1) 97 (4.7) 48 (6.3) Hypercholesterolemia (%) 470 (24.9) 396 (21.7) 419 (20.2) 136 (17.8) Hypertension (%) 577 (30.6) 506 (27.7) 550 (26.5) 215 (28.1) Probable Depression (%) 265 (14.1) 230 (12.6) 265 (12.8) 150 (19.6) Healthy Diet (%) 582 (30.9) 661 (36.2) 780 (37.6) 20 (2.6) Moderate Alcohol (%) 1008 (53.4) 1049 (57.5) 1162 (56.1) 23 (3.0) Physical Activity Level (%) Sedentary 91 (4.8) 51 (2.8) 72 (3.5) 89 (11.6) Low 421 (22.3) 384 (21.1) 403 (19.4) 232 (30.3) Moderate 997 (52.9) 946 (51.9) 1137 (54.8) 334 (43.6) High 377 (20.0) 443 (24.3) 461 (22.2) 111 (14.5) Current Smoker (%) 206 (10.9) 191 (10.5) 284 (13.7) 150 (19.6) A total of 960 incident CVD cases were identified during the 11 years of follow-up (median = 9.8 years). Estimates from separate Cox models for each prosociality exposure are presented in Table 2 . For prosocial intentions, the crude hazard ratio (HR) per 1 SD increase in the exposure is 0.85 (95% CI: 0.79,0.91). This protective association was maintained after adjusting for age, sex, ethnicity, and marital status, but was largely attenuated after adjusting for educational attainment, work status, and wealth. The crude association between frequent vs. never volunteering and risk of CVD was 0.67 (95% CI: 0.58, 0.79). While some attenuation was evident after accounting for confounders, frequent vs. never volunteering remained associated with an 18% decreased CVD risk after adjusting for all covariates including health characteristics and prosocial intentions. Similarly, informal helping behaviour was also associated with lower CVD risk in fully adjusted models, with each additional helping behaviour associated with a HR of 0.96 (95% CI: 0.92, 1.01). Table 2 Independent Associations between Forms of Prosociality and Time to Cardiovascular Event Prosociality Measure Cases / person-years Model 1 Model 2 Model 3 Model 4 Model 5 Intentions 833/46,243 0.85 (0.79, 0.91) 0.92 (0.86, 0.99) 0.94 (0.88, 1.01) 0.95 (0.89, 1.02) NA Volunteering Frequency Never 727 / 33,717 Ref Ref Ref Ref Ref Occasionally 44 / 4,248 0.48 (0.35, 0.65) 0.63 (0.47, 0.86) 0.69 (0.51, 0.94) 0.71 (0.52, 0.97) 0.73 (0.53, 1.01) Frequently 189 / 12,936 0.67 (0.58, 0.79) 0.71 (0.60, 0.83) 0.77 (0.65, 0.90) 0.82 (0.70, 0.98) 0.82 (0.68, 0.98) Informal Helping 960/50,869 0.88 (0.84, 0.92) 0.93 (0.88, 0.97) 0.94 (0.90, 0.98) 0.96 (0.92, 1.01) 0.96 (0.91, 1.01) Note. Estimates are Hazards Ratios (95% CI) with each exposure treated separately except when noted otherwise. For Prosocial Intentions, the HR is per 1sd change. For Informal Helping, the HR is per each additional helping behaviour. Model 1 is a crude model. Model 2 adjusts for age, sex, ethnicity, and marital status. Model 3 is Model 2 + educational attainment, current work status, and wealth. Model 4 is Model 3 + depression and self-reported diagnoses of diabetes, hypertension, and hypercholesterolemia, as well as smoking status, physical activity, alcohol consumption, and healthy diet. Model 5 is Model 4 + prosocial intentions. Engaging in and experiencing more forms of prosociality was associated with substantially lower CVD risk (Fig. 1 , Table S4), with participants endorsing all three (n = 1,037; 17.9% of sample) vs. no forms (n = 899; 15.6% of sample) exhibiting a HR of 0.70 (95% CI: 0.54, 0.89) in fully adjusted models. Additionally, associations between formal volunteering and CVD were significantly modified by level of prosocial intentions (HR for intentions*frequent formal volunteering = 1.22; p = 0.03). Stratified analyses found frequent volunteering was associated with 31% (95% CI: 0.50, 0.94) reduced risk of CVD in participants with low prosocial intentions but only a 19% (95% CI: 0.66, 1.01) reduced risk in participants with medium or high intentions. The association between informal helping behaviors was somewhat stronger in those with medium or high intentions (HR = 0.93, 95% CI: 0.88, 0.99) compared to those with low intentions (HR = 0.97, 95% CI: 0.88, 1.07), although the formal interaction term was not statistically significant (HR for intentions*informal helping = 1.01; p = 0.73). For all results, see Table S5. Additional Analyses: Estimates for each form of prosociality obtained from models that simultaneously adjusted for multiple forms were largely the same as those from models that considered them independently (Table S6). Additionally, excluding the first two-years of follow up did not substantially alter associations between each prosocial exposure and CVD risk (n = 203 cases dropped; Table S7). In fact, estimates for both formal volunteering and informal helping were slightly stronger in these lagged models, with a particularly notable change for volunteering frequency (HR for frequent vs. never = 0.78; 95% CI: 0.65, 0.95). Using alternative exposure definitions hinted at potential threshold effects for prosocial intentions (Table S8), with individuals with both medium and high (vs. low) intentions demonstrating a 15% lower CVD risk. Additionally, using different characterizations of volunteering or informal helping yielded highly similar findings to those reported above. Finally, we found no evidence for effect modification by age, sex, or wealth for any measure of prosociality with risk of CVD. Discussion This study examined associations of three forms of prosociality including prosocial intentions, formal volunteering, and informal helping with risk of developing CVD over 11 years of follow-up. Results from survival analyses show higher prosociality in any form was associated with reductions in incident CVD risk, even after accounting for demographics, socioeconomic factors, and baseline health factors. For instance, frequently vs. never engaging in formal volunteering was associated with an 18% lower risk of developing CVD across 11 years, and these associations were maintained even after adjusting for multiple major CVD risk factors including hypertension and depression. Such findings suggest prosociality may have salutogenic benefits that do not simply signal the absence of depression. While associations for prosocial intentions and informal helping were more modest relative to formal volunteering, the consistency of findings and robustness to adjustment for a broad range of covariates highlight that there may be multiple forms of prosociality from which individuals can derive health benefits, i.e., there is no single road to health. Contrary to our hypotheses regarding differential benefit across demographic groups, we found no evidence for effect modification by sex, age or socioeconomic factors, suggesting these benefits could operate across different populations. Considering Multiple Forms of Prosociality. Taken together, our results further suggest that engaging in more forms of prosociality may provide greater benefit for cardiovascular health, regardless of which combination of forms were enacted or experienced. This is consistent with findings from a recent study that considered multiple prosocial behaviours simultaneously in relation to formal volunteering and informal helping [ 16 , 17 , 20 , 21 ]. It further aligns with findings from an experimental study demonstrating that adolescents randomized to volunteer had significantly lower cholesterol, body mass index, and inflammatory markers two weeks later compared to a wait-list control group, with the strongest effects for those with the largest increases in prosocial intentions and behaviours as a result of the treatment [ 32 ]. Our results also suggest that interactions between different forms of prosociality may further influence risk of CVD, but there may also be a potential ceiling with regard to beneficial effects possible by engaging in multiple forms. Intriguingly these interactions appear to be somewhat form-specific with more suggestive evidence for level of prosocial intentions modifying the relationship between formal volunteering and CVD. More work should be done considering multiple forms of prosociality to explore these interactions. Our results were notably strong for formal volunteering, the most studied prosocial behaviour in the epidemiologic literature. For instance, engaging in any formal volunteering was associated with a 20% reduction in CVD risk while engaging in any informal helping was associated with only a 5% reduction. This difference could be due to volunteering expanding an individual’s social network more than informal helping, which is more often enacted for someone the individual knows well. Importantly though, engaging in informal helping behaviours maintained an association with CVD risk, even after accounting for formal volunteering. Models focusing on prosocial intentions suggest that individuals who report a higher willingness to help others have some reduction in CVD risk, though these estimates were weaker after accounting for socioeconomic and health-related factors. The weaker estimates could be due in part due to the behaviour-intention gap [ 33 ], whereby having intentions to behave in certain ways do not necessarily result in enacted behaviour. However, the associations for having medium vs. low levels of prosocial intentions remain after accounting for co-occurring formal volunteering and informal helping behaviours, suggesting that having more willingness to help others could be health beneficial in itself without directly engaging in prosocial behaviours. This is important as not all older adults have the physical or mental capacity to consistently engage in formal volunteering or other types of helping. Taken together our findings suggest independent effects of prosocial behaviours and intentions. Perhaps most strikingly, we found an association between engaging in more forms of prosociality and a lower risk of CVD. Given these forms were only weakly correlated, our results suggest there may be cumulative effects from engaging in multiple forms of prosociality, though there may be a ceiling of maximum benefit. Furthermore, there are multiple ways for individuals to derive health benefits from prosociality, thereby underscoring the need to consider how prosociality may improve health more broadly [ 12 ]. Also notable, while this is the first study to examine a measure of prosocial intentions in relation to CVD, some studies have shown that having more compassion, a feeling that motivates individuals to help others, is associated with lower blood pressure and inflammation [ 22 , 23 ]. Other work has shown higher prosocial intentions are related to lower dementia risk [ 15 ]. Together, these studies suggest that prosocial intentions may be an underexplored health asset, in addition to more commonly studied behaviours like formal volunteering. Mechanisms and Pathways. Several frameworks have been proposed to explain how prosociality may promote health, emphasizing the importance of considering multiple levels of influence [ 12 , 34 ]. On the microscale, engaging in prosocial behaviours and/or having more prosocial intentions may help individuals find meaning in their life, which has been linked with reduced risk of cardiovascular events and CVD mortality [ 35 ]. More prosocial individuals may also derive mental health benefits from their propensity to help others, with several studies showing that volunteers have a lower risk of depression and higher emotional well-being [ 23 ], key factors robustly associated with better cardiovascular health [ 5 , 9 ]. Furthermore, prosocial individuals generally engage in more health promoting behaviours like physical activity, which may also buffer against detrimental effects of stress [ 18 , 36 ]. At the macro-level, societies with a more prosocial orientation may impart more trust in their communities and institutions, and provide stronger social safety nets, both of which may promote cardiovascular health at the population level [ 37 ]. For instance, increased social trust may facilitate healthcare utilization [ 38 ], improving primary prevention for individuals at high risk of CVD. Decades of research suggest prosociality is modifiable and can be influenced on multiple levels. For example, at the individual level, engaging in meditation practice can increase prosociality with prior research demonstrating it can increase not only an individual’s engagement in prosocial behaviours but also the underlying emotional components of prosociality examined in this study [ 39 ]. Other work suggests prosociality is influenced by a population’s social norms, one’s socioeconomic resources, and one’s social network size [ 34 , 40 ]. Study Strengths and Limitations. Our study has a number of strengths. First, we used data from a large and nationally representative study of older adults who are richly characterized facilitating inclusion of many potential confounders. Second, we used an objective measure of CVD, which came from medical and mortality records. Finally, our study examined several components of prosociality simultaneously, revealing that that formal volunteering may have the largest health benefit in terms of reduction in CVD, forms of prosociality beyond formal volunteering and engaging in more types of prosociality are also beneficial for cardiovascular health. Our work also has some limitations. First, our prosociality measures are derived from self-report and may not capture the full range of behaviours and intentions in the population. For instance, our measure of informal helping did not include an exhaustive list of all helping behaviours and may underestimate of the full range of helping behaviours carried out by participants. Such measurement imprecision however would likely bias associations toward the null unless there were sizable differences based on future CVD risk status. Second, all individuals in ELSA self-selected to enroll and remain in a long-term health study, a process that likely excludes less prosocial individuals. Third, as with all observational studies, there may be unmeasured confounding. Concerns about the lack of specificity of our exposure and confounding are mitigated by our rigorous analytic approach including adjusting for a broad range of confounders and conducting multiple sensitivity analyses. Additionally, our results align with some experimental work, which is less prone to concerns of confounding [ 32 ]. Conclusions Reducing the burden of CVD is a high priority for improving the health and well-being of individuals and our societies. Our study found that increasing prosociality may be a promising target for doing just this, particularly given prior work demonstrating its modifiability [ 32 , 41 ]. In our study, individuals who engaged in formal volunteering, those who provide informal help, and those with higher prosocial intentions all had lower CVD risk compared to less prosocial study participants. We also found that engaging in more forms of prosociality seemed to confer even greater benefit for cardiovascular health, suggesting that while doing any of these forms of prosociality is good, doing more may be better up to a certain point. To date, much research and clinical attention has been given to the benefits of receiving support to improve CVD health outcomes, but our study suggests that giving this support may be as, if not more, beneficial. Declarations Funding. This work was supported by the National Institute on Aging (R01AG017644); and the National Institute for Health and Care Research (198/1074-02). Competing Interests. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contributions. Study conception and design: JF, LM, LK; Data Curation: JF, FB, JG; Statistical analyses: JF; Supervision of the Project: LK; Draft of the manuscript: JF, LM, LK; Funding Acquisition: AS; All authors participated in the interpretation of the results and critical revision of the manuscript. All authors agreed to the published version of the manuscript. Ethics Approval. All ELSA participants provide written informed consent, and the study was approved by the UK National Research Ethics Service. The current project met criteria for Institutional Review Board exception from the Harvard University Area Institutional Review Board. 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Does Compassion Predict Blood Pressure and Hypertension? The Modifying Role of Familial Risk for Hypertension. Int J Behav Med. 2020;27:527–38. https://doi.org/10.1007/s12529-020-09886-5 . Hui BPH, Ng JCK, Berzaghi E, Cunningham-Amos LA, Kogan A. Rewards of kindness? A meta-analysis of the link between prosociality and well-being. Psychol Bull. 2020;146:1084–116. https://doi.org/10.1037/bul0000298 . Konrath S, Fuhrel-Forbis A, Lou A, Brown S. Motives for volunteering are associated with mortality risk in older adults. Volume 31. Health Psychol. American Psychological Association; 2012. p. 87. Steptoe A, Breeze E, Banks J, Nazroo J. Cohort profile: the English longitudinal study of ageing. Int J Epidemiol. 2013;42:1640–8. https://doi.org/10.1093/ije/dys168 . Singelis TM. The Measurement of Independent and Interdependent Self-Construals. Pers Soc Psychol Bull. SAGE Publications Inc. 1994;20:580–91. https://doi.org/10.1177/0146167294205014 . Johnson RC, Danko GP, Darvill TJ, Bochner S, Bowers JK, Huang Y-H, et al. Cross-cultural assessment of altruism and its correlates. Personal Individ Differ Elsevier. 1989;10:855–68. Bell ML, Fairclough DL, Fiero MH, Butow PN. Handling missing items in the Hospital Anxiety and Depression Scale (HADS): a simulation study. BMC Res Notes. 2016;9:479. https://doi.org/10.1186/s13104-016-2284-z . Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging Am Psychol Association. 1997;12:277. Demakakos P, Hamer M, Stamatakis E, Steptoe A. Low-intensity physical activity is associated with reduced risk of incident type 2 diabetes in older adults: evidence from the English Longitudinal Study of Ageing. Diabetologia. 2010;53:1877–85. https://doi.org/10.1007/s00125-010-1785-x . Deckers K, van Boxtel MP, Schiepers OJ, de Vugt M, Muñoz Sánchez JL, Anstey KJ, et al. Target risk factors for dementia prevention: a systematic review and Delphi consensus study on the evidence from observational studies. Int J Geriatr Psychiatry Wiley Online Libr. 2015;30:234–46. Schreier HM, Schonert-Reichl KA, Chen E. Effect of volunteering on risk factors for cardiovascular disease in adolescents: A randomized controlled trial. JAMA Pediatr Am Med Association. 2013;167:327–32. Conner M, Norman P. Understanding the intention-behavior gap: The role of intention strength. Front Psychol Front Media SA. 2022;13:923464. Bailey PE, Ebner NC, Stine-Morrow EAL. Introduction to the Special Issue on Prosociality in Adult Development and Aging: Advancing Theory Within a Multilevel Framework. Volume 36. Psychol Aging. WASHINGTON: American Psychological Association; 2021. pp. 1–9. https://doi.org/10.1037/pag0000598 . Kim ES, Delaney SW, Kubzansky LD. Sense of Purpose in Life and Cardiovascular Disease: Underlying Mechanisms and Future Directions. Curr Cardiol Rep. 2019;21:135. https://doi.org/10.1007/s11886-019-1222-9 . Poulin MJ, Brown SL, Dillard AJ, Smith DM. Giving to Others and the Association Between Stress and Mortality. Am J Public Health. 2013;103:1649–55. https://doi.org/10.2105/AJPH.2012.300876 . Sachs JD, Karim SSA, Aknin L, Allen J, Brosbøl K, Colombo F, et al. The Lancet Commission on lessons for the future from the COVID-19 pandemic. The Lancet. Elsevier. 2022;400:1224–80. Emmering SA, Astroth KS, Woith WM, Dyck MJ, Kim M. Social capital, health, health behavior, and utilization of healthcare services among older adults: A conceptual framework. Nurs Forum (Auckl). 2018;53:416–24. https://doi.org/10.1111/nuf.12268 . Luberto CM, Shinday N, Song R, Philpotts LL, Park ER, Fricchione GL, et al. A Systematic Review and Meta-analysis of the Effects of Meditation on Empathy, Compassion, and Prosocial Behaviors. Mindfulness. 2018;9:708–24. https://doi.org/10.1007/s12671-017-0841-8 . Eisenberg N, Hofer C, Sulik MJ, Liew J. The development of prosocial moral reasoning and a prosocial orientation in young adulthood: concurrent and longitudinal correlates. Dev Psychol Am Psychol Association. 2014;50:58. Carlson MC, Kuo JH, Chuang Y-F, Varma VR, Harris G, Albert MS, et al. Impact of the Baltimore Experience Corps Trial on cortical and hippocampal volumes. Alzheimers Dement Elsevier. 2015;11:1340–8. https://doi.org/10.1016/j.jalz.2014.12.005 . Statements & Declarations. Supplementary Files RoadtoHeartHealthSupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 03 Dec, 2025 Editor invited by journal 29 Nov, 2025 Editor assigned by journal 25 Nov, 2025 First submitted to journal 24 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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10:27:10","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136478,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8196033/v1/519afaf8494d8d017921f41f.html"},{"id":97687593,"identity":"3a3aa8f3-dc8b-4734-81a5-12593786514c","added_by":"auto","created_at":"2025-12-08 10:27:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProbability of Remaining CVD Event Free by Prosociality Level\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Curves are adjusted for age, sex, ethnicity, marital status, educational attainment, work status, and wealth. There were 169 cases / 6,687 person-years for individuals not engaging in any forms of prosociality, 321 cases /15,134 person-years for those engaged in one form, \u0026nbsp;224 cases/ 15,591 person-years for those who engaged in two forms, and 117 cases / 8,885 person-years for those who engaged in three forms of prosociality.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8196033/v1/025beffcc9a5a8298704a6dd.png"},{"id":97902405,"identity":"850db84c-f872-436d-a5e2-e5fb167229d0","added_by":"auto","created_at":"2025-12-10 15:52:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":930792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8196033/v1/9e7ff40d-3aa8-4483-8ee7-1fd3dcba9625.pdf"},{"id":97893550,"identity":"58015abe-a8e1-4ca1-88c8-d488dda0d8c0","added_by":"auto","created_at":"2025-12-10 15:30:42","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":182399,"visible":true,"origin":"","legend":"","description":"","filename":"RoadtoHeartHealthSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8196033/v1/1b2b6e99cf8e95cb0e8a6cc7.docx"}],"financialInterests":"","formattedTitle":"Road to Heart Health: Paved with Good Intentions?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhile risk factor identification and treatment has improved in recent decades, cardiovascular diseases (CVD) including ischemic heart disease and stroke remain leading causes of death globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. By recent estimates, almost 600\u0026nbsp;million individuals currently have CVD and this number is expected to rise as the global population ages.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] This growing burden is not shared equitably, with more socioeconomically disadvantaged individuals disproportionately likely to develop and die from CVD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, identifying additional risk and protective factors that influence the development of CVD, and subgroups who may benefit the most from these factors, is a priority for prevention efforts.\u003c/p\u003e\u003cp\u003eA growing literature identifies psychosocial factors such as stress and distress, social connectedness, and psychological well-being as important potential targets for interventions to reduce CVD risk [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond their influence on downstream health behaviours (e.g., smoking, exercise), these factors have been linked to changes in relevant biological processes including increased inflammation and dysregulation of hypothalamic-pituitary-adrenal axis function, implicated in the etiology of CVD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For instance, recent meta-analyses of prospective studies found individuals with a history of depression have a 30% higher risk of developing coronary heart disease and a 34% higher risk of having a stroke [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Other work suggests that psychosocial stress may not impact all groups equally, with a recent systematic review finding women are more susceptible than men to psychosocial stress-related health harms, underscoring the need to consider how risk factors and their impacts vary across subgroups [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne psychosocial factor whose health effects are underexplored is prosociality, which broadly refers to intentions and behaviours that prioritize the needs of others [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Prosociality has been linked to lower all-cause mortality [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], lower pain interference [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and lower risk of dementia [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. While some studies have found specific prosocial behaviours such as formal volunteering and providing informal help to others are associated with lower risk of CVD and related risk conditions like hypertension [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], findings have been inconsistent [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA limitation of prior work is its reliance on self-reported measures for both prosociality and CVD, leading to concerns about potential bias. This work has also largely examined specific prosocial behaviours in isolation without considering that individuals may engage in multiple forms of prosociality simultaneously, including enacting additional prosocial behaviours and experiencing upstream cognitive and affective motivations such as prosocial intentions and compassion, the latter of which has demonstrated associations with lower levels of inflammation and blood pressure, known risk factors for CVD [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and may modify the health effects of prosocial behaviors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. If certain forms of prosociality are more relevant for health, failing to consider multiple forms simultaneously may underestimate the total health impact of prosociality or misattribute observed effects to the wrong form. Furthermore, whether individuals derive additional health benefits from enacting and experiencing multiple forms of prosociality remains untested. To date, only one study examined different helping behaviours simultaneously in relation to self-reported CVD. Findings pointed to gender differences across associations: volunteering was associated with lower CVD risk in women only, while informal helping was associated with lower CVD risk in men only [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. One other limitation of prior work is that few studies have accounted for depression, which precludes understanding if effects of prosociality represent more than just the absence of negative mental states or poor functioning, an important concern given strong linkages between depression and CVD.\u003c/p\u003e\u003cp\u003eThus, a stronger understanding of the role of prosociality in CVD risk will be obtained with research examining multiple forms of prosociality, using objectively measured disease endpoints, and accounting for depression and other confounders. This study begins to address these issues by using a large, nationally representative sample to evaluate several hypotheses. First, we hypothesize prosociality in any form will be associated with reduced CVD risk. We consider two behaviours (formal volunteering and informal helping), prosocial intentions, and the possibility that engaging in more forms of prosociality will increase the magnitude of these associations. We further hypothesize that associations will be especially strong in those with higher prosocial intentions and female participants [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. With exploratory analyses, we evaluate the potential modifying effects of age and personal wealth as older adults and individuals with lower resources are disproportionately affected by CVD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Participants and Protocol.\u003c/b\u003e Data are from the English Longitudinal Study of Ageing (ELSA) core sample, a nationally representative panel study of English adults aged 50 and over living in private households [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. ELSA data has been collected biennially since 2002 with participants providing information on health and well-being, social activities, and economic resources through computer-assisted personal interviews. The study sample is periodically replenished to ensure the full age range continues to be represented. We used Wave 4 (2008\u0026ndash;2009) as our baseline as it is the only wave during which data on all exposures of interest were collected. Participants were followed through Wave 9 (2018\u0026ndash;2019) for a total of 11-years of possible follow up time. Among 9,886 Wave 4 core participants, our analytic sample excludes those who did not consent to medical record data linkage (n\u0026thinsp;=\u0026thinsp;1,490), those with prevalent CVD at baseline (n\u0026thinsp;=\u0026thinsp;1,415), those who reported being \u0026ldquo;Permanently sick or disabled\u0026rdquo; at baseline (n\u0026thinsp;=\u0026thinsp;316), and those with incomplete covariate data (n\u0026thinsp;=\u0026thinsp;330), yielding a sample of 6,549 participants with any of the prosociality measures (see Appendix 1 for more details). Inclusion criteria required individuals have any measure of prosociality, while individuals missing data on some prosociality measures were not excluded; thus, the analytic sample differs slightly for each exposure. All ELSA participants provide written informed consent, and the study was approved by the UK National Research Ethics Service. Researchers may request all ELSA data through the UK Data Service (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ukdataservice.ac.uk/use-data.aspx\u003c/span\u003e\u003cspan address=\"https://ukdataservice.ac.uk/use-data.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The current project met criteria for Institutional Review Board exception.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003e\u003cb\u003eProsocial Intentions.\u003c/b\u003e A 9-item scale using items from validated self-report measures of altruism and collectivism administered in ELSA at Wave 4 only [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These items were previously selected and modified for the ELSA population, and in prior work predicted better cognitive function.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Individuals missing\u0026thinsp;\u0026ge;\u0026thinsp;50% of items were excluded; for participants missing\u0026thinsp;\u0026lt;\u0026thinsp;50% of the items (8.9%), missing values were imputed with the individual\u0026rsquo;s mean score [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. We then derived an overall score summing the 9 items and standardizing the total, with higher scores reflecting higher prosocial intentions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFormal Volunteering.\u003c/b\u003e Participants were asked to self-report how often they performed any voluntary work, with response options ranging from \u0026ldquo;never\u0026rdquo; to \u0026ldquo;twice a month or more.\u0026rdquo; We categorized these responses by considering those who reported volunteering \u0026ldquo;about once a month\u0026rdquo; or more as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003efrequently\u003c/span\u003e volunteering, those volunteering between \u0026ldquo;less than once a year\u0026rdquo; and \u0026ldquo;every few months\u0026rdquo; as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eoccasionally\u003c/span\u003e volunteering, and those reporting \u0026ldquo;never\u0026rdquo; as \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003enever\u003c/span\u003e volunteering. We also categorized volunteering as a dichotomous exposure (any versus never volunteering).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInformal Helping.\u003c/b\u003e Participants additionally answered the following question and were invited to endorse any of the 10 listed helping activities in which they had engaged: \u0026ldquo;In the last 12 months, have you done any of these things, unpaid, for someone who was not a relative?\u0026rdquo; with activities including items such as \u0026ldquo;Cooking, cleaning, laundry, gardening or other routine household jobs.\u0026rdquo; For the full list of items see Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. The number of items endorsed were summed for a possible range of 0\u0026ndash;10. Primary analyses use this as a continuous measure while sensitivity analyses dichotomize this variable (\u0026ldquo;Yes\u0026rdquo; if endorsed at least one behaviour; \u0026ldquo;No\u0026rdquo; otherwise).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCardiovascular Disease.\u003c/b\u003e Adjudicated CVD cases were identified through medical and mortality records. Medical records from the Admitted Patient Care data from the National Health Service\u0026rsquo;s Hospital Episodes Statistics were linked to consenting ELSA participants. Individuals with a record of any of the following ICD-10 codes were considered a CVD case: I21-I25 (ischemic heart diseases) and I60-I69 (cerebrovascular diseases), with the date of the first such incident considered the time of event. Individuals with events that preceded their baseline interview were excluded from analyses. These records were supplemented by CVD mortality cases identified by UK NHS mortality records.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates.\u003c/b\u003e Baseline covariates (Wave 4) for the main analyses were selected based on a review of literature as factors that could be associated with both prosociality and CVD, but which were unlikely to be on the causal pathway between them. Self-reported demographic factors included age, sex, ethnicity (characterized by ELSA as \u0026ldquo;White\u0026rdquo; and \u0026ldquo;Other\u0026rdquo;), marital status (married, single, divorced, widowed), and socioeconomic factors characterized by educational attainment, work status, and wealth. Educational attainment was categorized according to number of years of formal education: 1) No formal qualifications; 2) School Certificate level; 3) A levels or equivalent; 4) University degree or higher. Work status was categorized as employed, retired, or otherwise out of the work force. Wealth was derived by summing up the value of reported possessions and assets and subtracting reported open mortgages and payments. The sum was then divided into deciles for interpretability.\u003c/p\u003e\u003cp\u003eWhile health factors may confound or mediate the associations of interest, we added baseline health factors to the main models to mitigate concerns about potential confounding. These include probable depression (reporting at least 3 symptoms on the Center for Epidemiological Studies\u0026mdash;Depression Scale) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], self-reported doctor diagnosed diabetes, hypertension, and hypercholesterolemia; and relevant health behaviours including self-reported current smoking, physical activity level, alcohol use, and healthy eating. Physical activity level was categorized by ELSA as sedentary, low, medium, and high using a validated approach based on self-reported frequency of vigorous (e.g., running), moderate (e.g., gardening), and mild (e.g., laundry) physical activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Based on U.K. medical guidelines, moderate alcohol use was dichotomized as drinking 1\u0026ndash;14 glasses per week vs. other (0 or \u0026gt;\u0026thinsp;14 glasses) and healthy eating was classified as having vs. not having at least 5 servings of fruits or vegetables per day [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analysis.\u003c/b\u003e Descriptive analyses compared covariates across tertiles of prosocial intentions, and by formal volunteering and informal helping status. We conducted separate Cox proportional hazard analyses to compare the hazard of developing CVD for each prosociality measure. Follow up time was defined as the amount of time from Wave 4 survey until CVD diagnosis, death, or the date of the most recently completed ELSA survey. Potential confounders were sequentially added to models in sets related to demographics, socioeconomic characteristics, and baseline health characteristics: model 1 was unadjusted; model 2 adjusted for age, sex, ethnicity, and marital status; model 3 additionally adjusted for educational attainment, work status, and wealth. Model 4 additionally adjusted for baseline health characteristics including depression, diabetes, hypertension, hypercholesteremia, physical activity, smoking, alcohol use, and diet. When evaluating associations with prosocial behaviours, in a final model, we further adjusted for prosocial intentions (Model 5). We additionally examined how being prosocial in multiple forms (range\u0026thinsp;=\u0026thinsp;0\u0026ndash;3), defined as engaging in any volunteering, engaging in any informal helping, and being in the top two tertiles of prosocial intentions, relate to risk of CVD. Furthermore, we explored prosocial intentions modified the association between each prosocial behavior and risk of CVD through use of interaction terms and stratified analyses.\u003c/p\u003e\u003cp\u003eWe conducted additional analyses to evaluate the robustness of our findings and identify if any subgroups particularly benefit from prosociality. First, we considered these measures of prosociality simultaneously, by treating them as independent exposures in the same model. Next, to reduce concerns about reverse causality (i.e., underlying heart problems constrained individuals in ways that make it harder to engage in prosocial behaviour), we re-ran all models excluding the first two years of follow-up. Third, we explored measurement alternatives for all prosociality measures: treating prosocial intentions as tertiles (low, medium, and high) and treating formal volunteering and informal helping each as dichotomous variables (any volunteering/ helping vs. no volunteering/helping). Finally, to identify whether specific subgroups may benefit from prosociality, we explored effect measure modification by age, sex, or wealth through the inclusion of interaction terms and by conducting stratified analyses.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 6,549 participants were included in descriptive analyses, while sample sizes for survival analyses ranged from 5,783 to 6,460 depending on availability of data for each prosociality measure. The sample consisted of 56.4% females, 97.7% white participants, and had a mean age of 66.2 years at baseline. Descriptive statistics suggest that individuals with higher vs. lower prosocial intentions were more likely to be female, were younger, more highly educated, more likely to be employed, had higher total wealth at baseline, and lower prevalence of baseline health conditions. Individuals with high prosocial intentions were also more likely to volunteer and to engage in more informal helping behaviours (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S2). Different aspects of prosociality were weakly positively correlated, with the highest correlation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\)\u003c/span\u003e\u003c/span\u003e=0.30) between formal volunteering and informal helping behaviour (Table S3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Sample by Prosocial Intentions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eProsocial Intentions Tertile\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003en\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProsocial Intentions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16.65 (3.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.60 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.73 (2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA (NA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolunteering Status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Volunteering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1420 (75.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1211 (66.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1261 (60.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e533 (69.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVolunteering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e466 (24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e613 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e811 (39.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e145 (18.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e88 (11.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformal Helping (# of behaviours)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87 (1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.19 (1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59 (1.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92 (1.51)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMissing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (0.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e96 (12.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSociodemographics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e963 (51.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1033 (56.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1268 (61.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e429 (56.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e923 (48.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e791 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e805 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e337 (44.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66.92 (9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.74 (9.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.01 (8.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68.85 (11.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1851 (98.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1793 (98.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2042 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e711 (92.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried / Partnered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1300 (68.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1269 (69.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1464 (70.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e357 (46.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced / Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e187 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201 (11.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e237 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136 (17.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58 (7.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e252 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289 (13.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e215 (28.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational Attainment (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Formal Qualifications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e551 (29.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e418 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e426 (20.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e291 (38.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool Certificate Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e456 (24.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e447 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e474 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (19.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA Levels or Equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e417 (22.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e458 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e554 (26.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e159 (20.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity Degree or Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e462 (24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e501 (27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e619 (29.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (21.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork Status (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e615 (32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e662 (36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e805 (38.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e257 (33.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLooking after home\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e131 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e144 (6.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetired\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1135 (60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1010 (55.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1108 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e447 (58.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6 (0.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWealth (\u0026pound;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304,849.98 (366,100.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e363,760.86 (498,804.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e397,819.94 (871,232.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e288,139.94 (1,455,892.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Factors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138 (7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48 (6.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypercholesterolemia (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e470 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e396 (21.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e419 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e136 (17.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e577 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e506 (27.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e550 (26.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e215 (28.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProbable Depression (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e265 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e230 (12.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e265 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (19.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthy Diet (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e582 (30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e661 (36.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e780 (37.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20 (2.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Alcohol (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1008 (53.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1049 (57.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1162 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23 (3.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical Activity Level (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSedentary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (4.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89 (11.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e421 (22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e384 (21.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e403 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e232 (30.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e997 (52.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e946 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1137 (54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e334 (43.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e377 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e443 (24.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e461 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e111 (14.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent Smoker (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e206 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e191 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e284 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150 (19.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA total of 960 incident CVD cases were identified during the 11 years of follow-up (median\u0026thinsp;=\u0026thinsp;9.8 years). Estimates from separate Cox models for each prosociality exposure are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For prosocial intentions, the crude hazard ratio (HR) per 1 SD increase in the exposure is 0.85 (95% CI: 0.79,0.91). This protective association was maintained after adjusting for age, sex, ethnicity, and marital status, but was largely attenuated after adjusting for educational attainment, work status, and wealth. The crude association between frequent vs. never volunteering and risk of CVD was 0.67 (95% CI: 0.58, 0.79). While some attenuation was evident after accounting for confounders, frequent vs. never volunteering remained associated with an 18% decreased CVD risk after adjusting for all covariates including health characteristics and prosocial intentions. Similarly, informal helping behaviour was also associated with lower CVD risk in fully adjusted models, with each additional helping behaviour associated with a HR of 0.96 (95% CI: 0.92, 1.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIndependent Associations between Forms of Prosociality and Time to Cardiovascular Event\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProsociality Measure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCases / person-years\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntentions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e833/46,243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.85 (0.79, 0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.92 (0.86, 0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.88, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.95 (0.89, 1.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVolunteering Frequency\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e727 / 33,717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccasionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 / 4,248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.48 (0.35, 0.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63 (0.47, 0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.69 (0.51, 0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71 (0.52, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.73 (0.53, 1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFrequently\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 / 12,936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.67 (0.58, 0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71 (0.60, 0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.77 (0.65, 0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.82 (0.70, 0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.82 (0.68, 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInformal Helping\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e960/50,869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.88 (0.84, 0.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.93 (0.88, 0.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.94 (0.90, 0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.96 (0.92, 1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003e0.96 (0.91, 1.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote.\u003c/em\u003e Estimates are Hazards Ratios (95% CI) with each exposure treated separately except when noted otherwise. For Prosocial Intentions, the HR is per 1sd change. For Informal Helping, the HR is per each additional helping behaviour. Model 1 is a crude model. Model 2 adjusts for age, sex, ethnicity, and marital status. Model 3 is Model 2\u0026thinsp;+\u0026thinsp;educational attainment, current work status, and wealth. Model 4 is Model 3\u0026thinsp;+\u0026thinsp;depression and self-reported diagnoses of diabetes, hypertension, and hypercholesterolemia, as well as smoking status, physical activity, alcohol consumption, and healthy diet. Model 5 is Model 4\u0026thinsp;+\u0026thinsp;prosocial intentions.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEngaging in and experiencing more forms of prosociality was associated with substantially lower CVD risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table S4), with participants endorsing all three (n\u0026thinsp;=\u0026thinsp;1,037; 17.9% of sample) vs. no forms (n\u0026thinsp;=\u0026thinsp;899; 15.6% of sample) exhibiting a HR of 0.70 (95% CI: 0.54, 0.89) in fully adjusted models. Additionally, associations between formal volunteering and CVD were significantly modified by level of prosocial intentions (HR for intentions*frequent formal volunteering\u0026thinsp;=\u0026thinsp;1.22; p\u0026thinsp;=\u0026thinsp;0.03). Stratified analyses found frequent volunteering was associated with 31% (95% CI: 0.50, 0.94) reduced risk of CVD in participants with low prosocial intentions but only a 19% (95% CI: 0.66, 1.01) reduced risk in participants with medium or high intentions. The association between informal helping behaviors was somewhat stronger in those with medium or high intentions (HR\u0026thinsp;=\u0026thinsp;0.93, 95% CI: 0.88, 0.99) compared to those with low intentions (HR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.88, 1.07), although the formal interaction term was not statistically significant (HR for intentions*informal helping\u0026thinsp;=\u0026thinsp;1.01; p\u0026thinsp;=\u0026thinsp;0.73). For all results, see Table S5.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eAdditional Analyses:\u003c/h3\u003e\n\u003cp\u003eEstimates for each form of prosociality obtained from models that simultaneously adjusted for multiple forms were largely the same as those from models that considered them independently (Table S6). Additionally, excluding the first two-years of follow up did not substantially alter associations between each prosocial exposure and CVD risk (n\u0026thinsp;=\u0026thinsp;203 cases dropped; Table S7). In fact, estimates for both formal volunteering and informal helping were slightly stronger in these lagged models, with a particularly notable change for volunteering frequency (HR for frequent vs. never\u0026thinsp;=\u0026thinsp;0.78; 95% CI: 0.65, 0.95). Using alternative exposure definitions hinted at potential threshold effects for prosocial intentions (Table S8), with individuals with both medium and high (vs. low) intentions demonstrating a 15% lower CVD risk. Additionally, using different characterizations of volunteering or informal helping yielded highly similar findings to those reported above. Finally, we found no evidence for effect modification by age, sex, or wealth for any measure of prosociality with risk of CVD.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined associations of three forms of prosociality including prosocial intentions, formal volunteering, and informal helping with risk of developing CVD over 11 years of follow-up. Results from survival analyses show higher prosociality in any form was associated with reductions in incident CVD risk, even after accounting for demographics, socioeconomic factors, and baseline health factors. For instance, frequently vs. never engaging in formal volunteering was associated with an 18% lower risk of developing CVD across 11 years, and these associations were maintained even after adjusting for multiple major CVD risk factors including hypertension and depression. Such findings suggest prosociality may have salutogenic benefits that do not simply signal the absence of depression. While associations for prosocial intentions and informal helping were more modest relative to formal volunteering, the consistency of findings and robustness to adjustment for a broad range of covariates highlight that there may be multiple forms of prosociality from which individuals can derive health benefits, i.e., there is no single road to health. Contrary to our hypotheses regarding differential benefit across demographic groups, we found no evidence for effect modification by sex, age or socioeconomic factors, suggesting these benefits could operate across different populations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConsidering Multiple Forms of Prosociality.\u003c/b\u003e Taken together, our results further suggest that engaging in more forms of prosociality may provide greater benefit for cardiovascular health, regardless of which combination of forms were enacted or experienced. This is consistent with findings from a recent study that considered multiple prosocial behaviours simultaneously in relation to formal volunteering and informal helping [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. It further aligns with findings from an experimental study demonstrating that adolescents randomized to volunteer had significantly lower cholesterol, body mass index, and inflammatory markers two weeks later compared to a wait-list control group, with the strongest effects for those with the largest increases in prosocial intentions and behaviours as a result of the treatment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our results also suggest that interactions between different forms of prosociality may further influence risk of CVD, but there may also be a potential ceiling with regard to beneficial effects possible by engaging in multiple forms. Intriguingly these interactions appear to be somewhat form-specific with more suggestive evidence for level of prosocial intentions modifying the relationship between formal volunteering and CVD. More work should be done considering multiple forms of prosociality to explore these interactions.\u003c/p\u003e\u003cp\u003eOur results were notably strong for formal volunteering, the most studied prosocial behaviour in the epidemiologic literature. For instance, engaging in any formal volunteering was associated with a 20% reduction in CVD risk while engaging in any informal helping was associated with only a 5% reduction. This difference could be due to volunteering expanding an individual\u0026rsquo;s social network more than informal helping, which is more often enacted for someone the individual knows well. Importantly though, engaging in informal helping behaviours maintained an association with CVD risk, even after accounting for formal volunteering. Models focusing on prosocial intentions suggest that individuals who report a higher willingness to help others have some reduction in CVD risk, though these estimates were weaker after accounting for socioeconomic and health-related factors. The weaker estimates could be due in part due to the behaviour-intention gap [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], whereby having intentions to behave in certain ways do not necessarily result in enacted behaviour. However, the associations for having medium vs. low levels of prosocial intentions remain after accounting for co-occurring formal volunteering and informal helping behaviours, suggesting that having more willingness to help others could be health beneficial in itself without directly engaging in prosocial behaviours. This is important as not all older adults have the physical or mental capacity to consistently engage in formal volunteering or other types of helping.\u003c/p\u003e\u003cp\u003eTaken together our findings suggest independent effects of prosocial behaviours and intentions. Perhaps most strikingly, we found an association between engaging in more forms of prosociality and a lower risk of CVD. Given these forms were only weakly correlated, our results suggest there may be cumulative effects from engaging in multiple forms of prosociality, though there may be a ceiling of maximum benefit. Furthermore, there are multiple ways for individuals to derive health benefits from prosociality, thereby underscoring the need to consider how prosociality may improve health more broadly [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlso notable, while this is the first study to examine a measure of prosocial intentions in relation to CVD, some studies have shown that having more compassion, a feeling that motivates individuals to help others, is associated with lower blood pressure and inflammation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Other work has shown higher prosocial intentions are related to lower dementia risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Together, these studies suggest that prosocial intentions may be an underexplored health asset, in addition to more commonly studied behaviours like formal volunteering.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMechanisms and Pathways.\u003c/b\u003e Several frameworks have been proposed to explain how prosociality may promote health, emphasizing the importance of considering multiple levels of influence [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. On the microscale, engaging in prosocial behaviours and/or having more prosocial intentions may help individuals find meaning in their life, which has been linked with reduced risk of cardiovascular events and CVD mortality [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. More prosocial individuals may also derive mental health benefits from their propensity to help others, with several studies showing that volunteers have a lower risk of depression and higher emotional well-being [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], key factors robustly associated with better cardiovascular health [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, prosocial individuals generally engage in more health promoting behaviours like physical activity, which may also buffer against detrimental effects of stress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. At the macro-level, societies with a more prosocial orientation may impart more trust in their communities and institutions, and provide stronger social safety nets, both of which may promote cardiovascular health at the population level [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For instance, increased social trust may facilitate healthcare utilization [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], improving primary prevention for individuals at high risk of CVD.\u003c/p\u003e\u003cp\u003eDecades of research suggest prosociality is modifiable and can be influenced on multiple levels. For example, at the individual level, engaging in meditation practice can increase prosociality with prior research demonstrating it can increase not only an individual\u0026rsquo;s engagement in prosocial behaviours but also the underlying emotional components of prosociality examined in this study [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Other work suggests prosociality is influenced by a population\u0026rsquo;s social norms, one\u0026rsquo;s socioeconomic resources, and one\u0026rsquo;s social network size [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Strengths and Limitations.\u003c/b\u003e Our study has a number of strengths. First, we used data from a large and nationally representative study of older adults who are richly characterized facilitating inclusion of many potential confounders. Second, we used an objective measure of CVD, which came from medical and mortality records. Finally, our study examined several components of prosociality simultaneously, revealing that that formal volunteering may have the largest health benefit in terms of reduction in CVD, forms of prosociality beyond formal volunteering and engaging in more types of prosociality are also beneficial for cardiovascular health. Our work also has some limitations. First, our prosociality measures are derived from self-report and may not capture the full range of behaviours and intentions in the population. For instance, our measure of informal helping did not include an exhaustive list of all helping behaviours and may underestimate of the full range of helping behaviours carried out by participants. Such measurement imprecision however would likely bias associations toward the null unless there were sizable differences based on future CVD risk status. Second, all individuals in ELSA self-selected to enroll and remain in a long-term health study, a process that likely excludes less prosocial individuals. Third, as with all observational studies, there may be unmeasured confounding. Concerns about the lack of specificity of our exposure and confounding are mitigated by our rigorous analytic approach including adjusting for a broad range of confounders and conducting multiple sensitivity analyses. Additionally, our results align with some experimental work, which is less prone to concerns of confounding [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eReducing the burden of CVD is a high priority for improving the health and well-being of individuals and our societies. Our study found that increasing prosociality may be a promising target for doing just this, particularly given prior work demonstrating its modifiability [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In our study, individuals who engaged in formal volunteering, those who provide informal help, and those with higher prosocial intentions all had lower CVD risk compared to less prosocial study participants. We also found that engaging in more forms of prosociality seemed to confer even greater benefit for cardiovascular health, suggesting that while doing any of these forms of prosociality is good, doing more may be better up to a certain point. To date, much research and clinical attention has been given to the benefits of receiving support to improve CVD health outcomes, but our study suggests that giving this support may be as, if not more, beneficial.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eThis work was supported by the National Institute on Aging (R01AG017644); and the National Institute for Health and Care Research (198/1074-02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests.\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions.\u0026nbsp;\u003c/strong\u003eStudy conception and design: JF, LM, LK; Data Curation: JF, FB, JG; Statistical analyses: JF; Supervision of the Project: LK; Draft of the manuscript: JF, LM, LK; Funding Acquisition: AS; All authors participated in the interpretation of the results and critical revision of the manuscript. All authors agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval.\u0026nbsp;\u003c/strong\u003eAll ELSA participants provide written informed consent, and the study was approved by the UK National Research Ethics Service. The current project met criteria for Institutional Review Board exception from the Harvard University Area Institutional Review Board.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaghavi M, Ong KL, Aali A, Ababneh HS, Abate YH, Abbafati C, et al. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Volume 403. Elsevier; 2024. pp. 2100\u0026ndash;32. 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Alzheimers Dement Elsevier. 2015;11:1340\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jalz.2014.12.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jalz.2014.12.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStatements \u0026amp; Declarations.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8196033/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8196033/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground and Aims:\u003c/h2\u003e\u003cp\u003eCardiovascular disease (CVD) is the leading cause of death worldwide. Prosociality, defined as positive other-regarding intentions and behaviours, is associated with positive health outcomes; however, few studies have evaluated its relationship with CVD. This study examines whether prosocial behaviours (formal volunteering and informal helping), intentions, and a combination of these, are associated with reduced CVD risk.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData are from 6,549 adults in the English Longitudinal Study of Ageing, who were free of CVD at baseline. A prosocial intentions scale was derived from items assessing altruism and collectivism. Incident stroke and ischemic heart disease events were identified via medical and mortality record linkages. Cox proportional hazards models assessed risk of developing CVD in relation to prosocial intentions and behaviours, accounting for sociodemographic and health-related covariates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003e960 incident CVD cases were identified during 11 years of follow-up. Frequent volunteers had an 18% lower CVD risk than those who never volunteer in fully adjusted models (95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;0.70. 0.98). Informal helping was also associated with lower CVD risk (per additional helping behaviour, hazard ratio (HR)\u0026thinsp;=\u0026thinsp;0.96 (95% CI: 0.92, 1.01). Having higher prosocial intentions was also weakly associated with lower CVD risk. Individuals who engaged in multiple forms of prosociality had a lower risk of CVD, including a 30% decrease (95% CI: 0.54, 0.89) for those engaging in 3 vs. 0 forms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eDiverse aspects of prosociality, including formal and informal behaviours and having an overall orientation toward being prosocial may be protective for CVD.\u003c/p\u003e","manuscriptTitle":"Road to Heart Health: Paved with Good Intentions?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:27:05","doi":"10.21203/rs.3.rs-8196033/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-08T08:30:52+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-03T14:30:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Epidemiology","date":"2025-11-29T15:37:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-25T09:07:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Epidemiology","date":"2025-11-24T12:37:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejep","sideBox":"Learn more about [European Journal of Epidemiology](https://www.springer.com/journal/10654)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ejep/default.aspx","title":"European Journal of Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"87bbd5fb-b95b-4aef-8f0b-111d3df28559","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T10:27:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:27:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8196033","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8196033","identity":"rs-8196033","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}