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Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study.

ABSTRACT: OBJECTIVES:To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. DESIGN:Longitudinal social network analysis. SETTING:Framingham Heart Study social network. PARTICIPANTS:4739 individuals followed from 1983 to 2003. MAIN OUTCOME MEASURES:Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. RESULTS:Clusters of happy and unhappy people are visible in the network, and the relationship between people's happiness extends up to three degrees of separation (for example, to the friends of one's friends' friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. CONCLUSIONS:People's happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

SUBMITTER: Fowler JH

PROVIDER: S-EPMC2600606 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study.

Fowler James H JH Christakis Nicholas A NA

BMJ (Clinical research ed.) 20081204

<h4>Objectives</h4>To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks.<h4>Design</h4>Longitudinal social network analysis.<h4>Setting</h4>Framingham Heart Study social network.<h4>Participants</h4>4739 individuals followed from 1983 to 2003.<h4>Main outcome measures</h4>Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties.<h4>Results</h4>Clusters of happy and ...[more]

PMID: 19056788

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Project description:ImportanceMany studies of the association between obesity and mortality rely on weight status at a single point in time, making it difficult to adequately address bias associated with reverse causality.ObjectiveTo investigate the association between maximum body mass index (BMI) and all-cause mortality without the consequences of reverse causality.Design, setting, and participantsProspective cohort studies for the original and offspring cohorts of the Framingham Heart Study. The follow-up period started from baseline examination 13 for the original cohort and from baseline examination 6 for the offspring cohort and ended December 31, 2014. The analyses were conducted in 2017. Participants were 6197 individuals with 3478 deaths during a mean of 17 years of follow-up.Main outcomes and measuresMaximum BMI over 24 years of weight history before the beginning of follow-up for all-cause mortality and cause-specific mortality. All-cause mortality and cause-specific mortality (deaths due to cardiovascular disease, cancer, or other causes).ResultsAmong 6197 participants (mean [SD] age at baseline, 62.79 [8.98] years; 55.5% female), 3478 (56.1%) died during the follow-up. A monotonic association was observed between maximum BMI and mortality, with increasing risks observed across obese I (BMI of 30 to <35; hazard ratio [HR], 1.27; 95% CI, 1.14-1.41) and obese II (BMI of 35 to <40; HR, 1.93; 95% CI, 1.68-2.20) categories. A significant association was not observed for the overweight category (BMI of 25 to <30; HR, 1.08; 95% CI, 0.99-1.18). Among never smokers, the risks increased, with a significant association emerging for individuals with maximum BMI in the overweight range (HR, 1.31; 95% CI, 1.13-1.51). The mortality rates of normal-weight individuals who were formerly overweight or obese were 47.48 and 66.67 per 1000 person-years, respectively, while individuals who never exceeded normal weight had a mortality rate of 27.93 per 1000 person-years.Conclusions and relevanceA monotonic association was found between maximum BMI over 24 years of weight history and subsequent all-cause mortality. Maximum BMI in the normal-weight range was associated with the lowest risk of mortality in this cohort, highlighting the importance of obesity prevention.

Project description:OBJECTIVE:To assess temporal trends in the association between newly diagnosed atrial fibrillation and death. DESIGN:Community based cohort study. SETTING:Framingham Heart Study cohort, in 1972-85, 1986-2000, and 2001-15 (periods 1-3, respectively), in Framingham, MA, USA. PARTICIPANTS:Participants with no atrial fibrillation, aged 45-95 in each time period, and identified with newly diagnosed atrial fibrillation (or atrial flutter) during each time period. MAIN OUTCOME MEASURES:The main outcome was all cause mortality. Hazard ratios for the association between time varying atrial fibrillation and all cause mortality were calculated with adjustment for time varying confounding factors. The difference in restricted mean survival times, adjusted for confounders, between participants with atrial fibrillation and matched referents at 10 years after a diagnosis of atrial fibrillation was estimated. Meta-regression was used to test for linear trends in hazard ratios and restricted mean survival times over the different time periods. RESULTS:5671 participants were selected in time period 1, 6177 in period 2, and 6174 in period 3. Adjusted hazard ratios for all cause mortality between participants with and without atrial fibrillation were 1.9 (95% confidence interval 1.7 to 2.2) in time period 1, 1.4 (1.3 to 1.6) in period 2, and 1.7 (1.5 to 2.0) in period 3 (Ptrend=0.70). Ten years after diagnosis of atrial fibrillation, the adjusted difference in restricted mean survival times between participants with atrial fibrillation and matched referents decreased by 31%, from -2.9 years (95% confidence interval -3.2 to -2.5) in period 1, to -2.1 years (-2.4 to -1.8) in period 2, to -2.0 years (-2.3 to -1.7) in period 3 (Ptrend=0.03). CONCLUSIONS:No evidence of a temporal trend in hazard ratios for the association between atrial fibrillation and all cause mortality was found. The mean number of life years lost to atrial fibrillation at 10 years had improved significantly, but a two year gap compared with individuals without atrial fibrillation still remained.

Project description:BackgroundThe prevalence of dementia is expected to soar as the average life expectancy increases, but recent estimates suggest that the age-specific incidence of dementia is declining in high-income countries. Temporal trends are best derived through continuous monitoring of a population over a long period with the use of consistent diagnostic criteria. We describe temporal trends in the incidence of dementia over three decades among participants in the Framingham Heart Study.MethodsParticipants in the Framingham Heart Study have been under surveillance for incident dementia since 1975. In this analysis, which included 5205 persons 60 years of age or older, we used Cox proportional-hazards models adjusted for age and sex to determine the 5-year incidence of dementia during each of four epochs. We also explored the interactions between epoch and age, sex, apolipoprotein E ε4 status, and educational level, and we examined the effects of these interactions, as well as the effects of vascular risk factors and cardiovascular disease, on temporal trends.ResultsThe 5-year age- and sex-adjusted cumulative hazard rates for dementia were 3.6 per 100 persons during the first epoch (late 1970s and early 1980s), 2.8 per 100 persons during the second epoch (late 1980s and early 1990s), 2.2 per 100 persons during the third epoch (late 1990s and early 2000s), and 2.0 per 100 persons during the fourth epoch (late 2000s and early 2010s). Relative to the incidence during the first epoch, the incidence declined by 22%, 38%, and 44% during the second, third, and fourth epochs, respectively. This risk reduction was observed only among persons who had at least a high school diploma (hazard ratio, 0.77; 95% confidence interval, 0.67 to 0.88). The prevalence of most vascular risk factors (except obesity and diabetes) and the risk of dementia associated with stroke, atrial fibrillation, or heart failure have decreased over time, but none of these trends completely explain the decrease in the incidence of dementia.ConclusionsAmong participants in the Framingham Heart Study, the incidence of dementia has declined over the course of three decades. The factors contributing to this decline have not been completely identified. (Funded by the National Institutes of Health.).

Project description:BackgroundStatistical methods for modeling longitudinal and time-to-event data has received much attention in medical research and is becoming increasingly useful. In clinical studies, such as cancer and AIDS, longitudinal biomarkers are used to monitor disease progression and to predict survival. These longitudinal measures are often missing at failure times and may be prone to measurement errors. More importantly, time-dependent survival models that include the raw longitudinal measurements may lead to biased results. In previous studies these two types of data are frequently analyzed separately where a mixed effects model is used for the longitudinal data and a survival model is applied to the event outcome.MethodsIn this paper we compare joint maximum likelihood methods, a two-step approach and a time dependent covariate method that link longitudinal data to survival data with emphasis on using longitudinal measures to predict survival. We apply a Bayesian semi-parametric joint method and maximum likelihood joint method that maximizes the joint likelihood of the time-to-event and longitudinal measures. We also implement the Two-Step approach, which estimates random effects separately, and a classic Time Dependent Covariate Model. We use simulation studies to assess bias, accuracy, and coverage probabilities for the estimates of the link parameter that connects the longitudinal measures to survival times.ResultsSimulation results demonstrate that the Two-Step approach performed best at estimating the link parameter when variability in the longitudinal measure is low but is somewhat biased downwards when the variability is high. Bayesian semi-parametric and maximum likelihood joint methods yield higher link parameter estimates with low and high variability in the longitudinal measure. The Time Dependent Covariate method resulted in consistent underestimation of the link parameter. We illustrate these methods using data from the Framingham Heart Study in which lipid measurements and Myocardial Infarction data were collected over a period of 26 years.ConclusionsTraditional methods for modeling longitudinal and survival data, such as the time dependent covariate method, that use the observed longitudinal data, tend to provide downwardly biased estimates. The two-step approach and joint models provide better estimates, although a comparison of these methods may depend on the underlying residual variance.

Dataset Information

Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study.

Publications

Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study.

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