presentación sandra milena agudelo londoño

Dynamic spread of happiness in a large social network:

Longitudinal analysis over 20 years in the Framingham heart study

Fowler, James H. and Christakis, Nicholas A.

BMJ 2008;337:a2338Sandra Milena Agudelo Londoño- Estudiante Maestría en Epidemiología- [email protected]

To evaluate whether happiness canspread from person to person andwhether niches of happiness formwithin social networks.

Sandra Milena Agudelo Londoño- Estudiante Maestría en Epidemiología- [email protected]


•Happiness is a fundamental object of human existence,determined by a complex set of voluntary and involuntary factors.

•WHO is increasingly emphasising happiness as a component ofhealth.

•Researchers in biological and social sciencies have identified abroad range of stimuli to happiness (or unhappiness); however,have not addressed a possibly key determinant of humanhappiness: the happiness of others.

•People can “catch” emotional states they observe in others overtime frames ranging from seconds to weeks.


Little is known about the role of social networks in happinessor about whether happiness might spread, by a diverse set ofmechanisms, over longer periods or more widely in socialnetworks.

We were particularly interested in whether the spread ofhappiness pertains not just to direct relationships (friends) butalso to indirect relationships (friends of friends) and whetherthere are geographical or temporal constraints on the spreadof happiness through a social network.


This study began in 1948, under the direction of theNational Heart, Lung and Blood Institute (NHLBI). Has beencommitted to identifying the common factors orcharacteristics that contribute to cardiovascular disease.

By recruiting an Original Cohort (5,209). Since that time theStudy has added an Offspring Cohort in 1971 (5,124), theOmni Cohort in 1994, a Third Generation Cohort in 2002(4,095), a New Offspring Spouse Cohort in 2003, and aSecond Generation Omni Cohort in 2003.

http://www.framinghamheartstudy.org/participants/original.html



http://www.framinghamheartstudy.org/participants/offspring.html



http://www.framinghamheartstudy.org/participants/omni.html



http://www.framinghamheartstudy.org/participants/genthree.html





http://www.framinghamheartstudy.org/participants/newoffspring.html















Used the offspring cohort 5124“egos.”Each ego is connected to otherpeople for diverse relationships.Each relationship is a “social tie.”Each person who has arelationship with an ego wascalled an “alter.”There were 12.067 individualswho were connected at somepoint in 1971-2003.

Network ascertainment


• Computerised information about the offspring cohort (since 1971).•The ascertainment of social ties was wide and systematic.

Include:• All first order relatives (alive or dead).• At least one close friend (7 examinations 1971 to 2003).• Home address- geocoded to determine neighbour relationships.• Place of employment- identify ties to coworkers.

•One ego, One alter category.•There were 53.228 observed social ties between the 5124 egos and any other alters. Average 10.4 ties.


Because friendship identifications are directional, 3 differenttypes: ego perceived friend, alter perceived friend and mutualfriend.

Capture the network links among participants longitudinally.

At inception, 53% of the egos were women; the egos’ mean agewas 38 years (21-70); and their mean education was 1.6 years ofcollege (0-≥17 years of education).

We studied 4739 of the 5124 egos who were alive in 1983 (firsttime happiness was measured). All participants were followeduntil 2003 (at exam 7), as were any ties to alters noted during thetime period 1983-2003.

Measures of Happiness

• Instrument: Center for Epidemiological Studies depression scale (CES-D) in1983- 2003 at times corresponding to the 5th, 6th, and 7th examinations of theoffspring cohort.

Defined happy with perfect score:

• 0=rarely or none of the time (<1 day/week),

• 1=some or a little of the time (1-2 days/week),

• 2=occasionally or a moderate amount of the time (3-4 days/week)

• and 3=most or all the time (5-7 days/ week).


Changes in their happiness over time

We used the previous wave as a baseline measure andevaluated the probability of an ego being happy at asucceeding wave.

At follow-up, the prevalence of happiness was 61% in exam 6and 59% in exam 7.

Between exams 6 and 7, 16% of individuals became happy,13% became unhappy, 49% remained happy, and 22%remained unhappy.


Network analysis Glossary

Homophily: the tendency for people to choose relationships with people who havesimilar attributes.

Component: a group of nodes that is a subset of a full network and in which each node isconnected by at least one path to every other node in the same component.

Cluster: a group of nodes of a certain type that is a subset of a full network and in whicheach node is connected by at least one path via nodes of the same type to every othernode in the same .

Degree of separation: the social distance of two individuals as measured by the smallestnumber of intermediary ties between one individual and the other within the network. “Geodesic distance”.

Centrality: is a count of the number of friends of a ego.

Topology: the fundamental pattern of ties in a social network.


Copyright ©2008 BMJ Publishing Group Ltd.

Exam 6.

1181 ind.

Exam 7.

1020 ind.

Node:

circles are female

squares are male.

Ties: black for

siblings,

red for friends

and spouses.

Node colour:

Blue: least happy

Green:

intermediate,

Yellow: mosthappy.

Fig 1. Happiness clusters in the Framingham social network.Kamada-Kawai algorithm . Pajek.

Statistical analysis

The association could be attributed to at least three processes:•Induction, Homophily and Confounding:

To distinguish between these effects requires repeated measures of happiness,longitudinal information about network ties, and information about the natureor direction of the ties.

Regression models (logistic: 1=happy, 0=isn’t happy) of egohappiness as a function of ego’s age, sex, education, and happinessin the previous exam, and of the happiness of an alter in thecurrent and previous exam.

Inclusion of ego happiness in the previous exam helps to eliminate serialcorrelation in the errors and also substantially controls for ego’s geneticendowment and any intrinsic stable predilection to be happy.



Fig 2 Social distance and happiness in the Framingham social network.


Alter’s happiness in the previous exam helps to control for homophily.

We evaluated the possibility of omitted variables or contemporaneous eventsor exposures in explaining the associations by examining how the type ordirection of the social relationship between ego and alter affects theassociation between them.

If unobserved factors drive the association between ego and alter happiness,then directionality of friendship should not be relevant. We also examinedthe possible role of exposure to neighbourhood factors by examining maps.

The main coefficient of interest in these regression models is the one relatedto contemporaneous happiness in alters—that is, the extent to which analter’s present happiness, net of the alter’s previous happiness, is associatedwith an ego’s present happiness, net of the ego’s previous happiness.



Fig 3. Happy alters in Framingham social network. Generalised estimating equation regression models confirm relation is strongly

significant, even with numerous controls.


We used generalised estimating equation procedures to account for multipleobservations of the same ego across waves and across ego-alter pairings.

The generalised estimating equation regression models provide parameterestimates in the form of β coefficients whereas the results reported in the textand in figures 4 and 5 are in the form of risk ratios, which are related to theexponentiated coefficients.

Mean effect sizes and 95% confidence intervals.

We explored the sensitivity of our results by conducting numerous otheranalyses but none of which yielded substantially different results from thosepresented here.



Fig 4. Alter type and happiness in the Framingham social network.

The networks

Networks are only partially observed. Therefore, there will bemeasurement error in individual network attributes.

If there is a correlation between this measurement error and happiness, itcould bias our results.

We evaluated this potential source of bias by measuring the Pearsoncorrelation between the number of social relations named outside theFramingham Heart Study and subject happiness.

The association was both small and not significant (P=0.33), suggestingthat the unobserved parts of the network do not bias the inferences wemake within the observed network.



Fig 5 Physical and temporal separation and spread of happiness in FSN.

• Happy people tend to be connected to one another.

• The clusters of happy and unhappy people seen in thenetwork are significantly larger than expected by chance.

• The association between ego and alter happiness is significantup to three degrees of separation.

• No differences between spouses and friends, and gender ofspouses.

• Relation between happiness and centrality remainedsignificant even when we controlled for age, education, andthe total number of family and non-family alters.

• Happiness itself does not increase a person’s centrality atsubsequent time points.


• The social network effect of happiness is multiplicative andasymmetric.

• Having additional social contacts is helpful to ego’s happinessonly if the extra social contacts are happy themselves .

• The principal determinant of a person’s happiness was theirprevious happiness; individuals who were happy at one wavewere roughly three times more likely than unhappy people tobe happy at the subsequent observation.

• Age, sex, and education had effects consistent, with womenbeing less happy then men and educated people being slightlyhappier.


• If the associations in the social network were merely causedby confounding, these effect sizes for different types offriendships should be more similar.

• That is, if some third factor were explaining both ego and alterhappiness, it should not respect the directionality of the tie.

• All these relations indicate the importance of physicalproximity, and the strong influence of neighbours suggeststhat the spread of happiness might depend more on frequentsocial contact than deep social connections.

• Happiness spreads significantly more through same sexrelationships than opposite sex relationships.


• + Conected alter 15.3% (IC95%12.2-18.8%)

• + Two alters 9.8% (7.0% to 12.9%)

• + Three alters 5.6% (2.4% to 9.0%).

• + Spouse 0.08 (95% CI: 0.002 to0.16)

• + Each happy alter 9% (P=0.001)

• - Each unhappy alter 7% (P=0.004).

• + Nearby friends 25%(1% to 57%).

• Distant friends have no significanteffect.

• + Nearby mutual friends 63% (12%to 148%)

• Nearby alter perceived friends ismuch weaker and not significant

• +Coresident spouses 8% (0.2% to16%)

• Non-coresident spouses have nosignificant effect

• + Nearby siblings 14% (1% to 28%)

• + Next door neighbours 34% (7%to 70%)

• Neighbours on the same blockhave no significant effect.

• No effect of the happiness ofcoworkers .

• + Friend who lives less than half amile 42% (6% to 95%)

• + For friends who live less thantwo miles away 22% (2% to 45%)

• + A friend examined in the pasthalf year 45% (4% to 122%)

• + Friends examined within the pastyear 35% (6% to 77%)


• 1. When the information was collected it was not intended that itwould be used to measure happiness, analyse social networks, orexplore this hypothesis. Consequently, the original data collectionwas not biased by the researchers’ desire to confirm thishypothesis or by the participants’ wishes to give socially desirableanswers.

• 2. Although social network analysis is complex and unfamiliar tomany, this research method is commonly used by sociologists,community psychologists, and others.

• 3. Despite the sometimes large and overlapping confidenceintervals, the results are internally consistent and robust tosensitivity analyses.


We should be cautious, however, for several reasons.1. A single community and a single database that was not designed to tackle

this hypothesis was studied— perhaps Framingham is unique in someway;perhaps the data collection incorporated an unknown systematic bias thatproduced these results.

2. The findings concerning friends must be viewed cautiously because thename generator used seems unlikely to have encouraged respondents withseveral close friends to name more than one.

3. The measure of happiness is well validated as a measure of “positive affect,”but it will be interesting to see if similar results are produced with differentmeasures of happiness. Happiness is not everything; unhappyacquaintances may contribute something other than happiness to our lives.

In summary, Fowler and Christakis have produced valuable, exciting, andreasonably robust results that will stimulate new and productive lines ofenquiry in happiness studies. However, we must not expect all the details oftheir findings to be confirmed in subsequent work.

Don’t drop your unhappy friends yet.Commentary: Understanding social network analysis by Peter Sainsbury BMJ 2008;337:a1957


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25/08/2010

presentación sandra milena agudelo londoño

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