The Spread of Obesity in a Large Social Network over 32 Years
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1 The Spread of Obesity in a Large Social Network over 32 Years FCEB Journal Club 28th June 2012 Presented by: Christopher Barton Follow me on
2 Why this paper ~ 18 months ago 1 st look at impact of chronic illness on social relationships and quality of care I had read media articles and commentary regarding social contagion research and comments/blog/twitter discussions about criticisms of the original research About 2 weeks ago a colleague showed me the obesity contagion animation
3 Introduction Background: Prevalence of obesity and overweight has increased in the US in the recent past There is support for a broad set of social and environmental explanations,? Spread within social networks Having obese social contacts might change a persons tolerance for being obese or influence adoption of specific behaviours, or even physiological imitation. Objectives: To examine several aspects of the spread of obesity including: Existence of clusters of obese persons within the network The association between one persons weight gain and weight gain amongst his or her social contacts Dependence on the nature of social ties The influence of sex, smoking and geographic distance of persons in network
4 Study subjects & data collection Participants Framingham study (to 2003): Framingham Heart Study (1948 n=5209) Framingham offspring study (1971 n=5124) Third generation cohort (2002 n=4095) Offspring cohort (mean age 38 years, 13.6yrs education, 53% female) comprised the ego s, and their links with any others in Framingham cohorts were alters. Network Ascertainment Archived handwritten tracking sheets to identify people close to ego s These sheets included first-order relatives (incl. spouses) and at least 1 close friend, many people listed were also Framingham participants Seven examination periods between 1971 and 2003.
5 Glossary
6 Network ties Overall there were 38,611 social and family ties, average 7.5 ties/ego 45% of the 5124 egos were connected through friendship to another person in the network. There were 3604 unique friendships (average 0.7 friendships ties/ego) Friendship ties are directional so studied 3 kinds of friendships Ego perceived friendship Alter perceived friendship Mutual friendship It was hypothesised that a friends social influence on an ego would be affected by the type of friendship, with the strongest effects occurring in mutual friendship.
7 Analysis Graphed the network using the Kamada-Kawai algorithm in Pajek software + generated videos of the network using social network image animator Examined whether data conformed to theoretical network models Results 1 visualising the network and how it changed over time by obese status
8 Considered 3 explanations for this observed clustering (pg. 372: 1. Egos might choose to associate with like alters (homophily) 2. Egos and alters might share attributes, or unobserved factors 3. Alters might exert social influence on ego s (induction) Basic statistical analysis involved specification of longitudinal logistic regression models Ego s obesity status at any time point (t+1) was a function of various attributes such as age, sex, education Ego s obesity status at the previous time point (t) The alters obesity status at times t and t+1 Used GEE to account for multiple observations of the same ego across examinations and across ego-alter pairs (assumed an independent working correlation structure for the clusters)
9 Analysis continued A time lagged dependent variable used to eliminate serial correlation in the errors (Lagrange multiplier test) and substantially control for the egos genetic endowment and any intrinsic, stable predisposition to obesity. The use of a lagged independent variable for an alters weight status controlled for homophily. The key variable of interest was an alters obesity at time t+1. A significant coefficient for this variable would suggest either that an alters weight affected an ego s weight, or that an ego and an alter experienced contemporaneous events affecting both their weights. The later models were estimated with varied ego-alter pair types.
10 Analysis (cont.) Examined how the type or direction of the social relationship affected the association. If unobserved factors drove the association between the ego s obesity and alter s obesity, the directionality of friendship should not be relevant. Evaluated the role of smoking cessation and geographic distance as a contributor by adding variables for smoking status at times t and t+1 to models 95% CI s calculated by simulating the first difference in the alters contemporaneous obesity (change 0 to 1) using 1000 randomly drawn sets of estimates from the coefficient matrix.
11 Results Figure 3: social distance and relative increase in probability of obesity in an ego, if alter becomes obese Compared conditional probability of obesity in the observed network with simulated networks where prevalence of obesity was the same but incidence of obesity randomly distributed. If clustering is occurring then the probability that an alter will be obese, given that an ego is known to be obese should be higher in the observed network than in the simulated network. Reach of the clusters is the point in degrees of separation at which the probability of an alters obesity is no longer related to ego obesity At 1 degree of separation this was 45% higher, at 2 degrees it was 20%, at 3 degrees it was 10% in the observed vs the simulated network
12 Directionality If an ego stated an alter was his or her friend, the ego s chances of becoming obese increase by 57% (6-123) Figure 4: Probability an ego will become obese by type of relationship - Regression analysis to evaluate the extent of interpersonal association in obesity If an ego stated an alter was his or her mutual friend, the ego s chances of becoming obese increase by 171% (59-326)
13 Key findings Findings suggest that obesity may spread in social networks in a quantifiable and disernable pattern that depends on the nature of social ties That is: A process of infection or contagion within the social network, that occurs up to three steps (degrees) in the network. The authors considered three explanations for clustering of obese people 1. Egos might choose to associate with like alters (homophily) 2. Egos and alters might share attributes or jointly experience unobserved contemporaneous events that cause their weight to vary at the same time (confounding) 3. Alters might exert social influence or peer effects on egos (induction) The authors suggest no. 3 as the likely explanation. Models controlled for previous weight status (rules out 1) Weight gain of neighbors and geographic distance not related (rules out 2) The directional nature of effects of friendships is evidence of interpersonal induction of obesity because friends do not simultaneously become obese as a result of contemporaneous exposures to unobserved factors pairs of friends and siblings of same sex have most influence on weight gain (suggests no. 3)
14 Strengths & Limitations Strengths Large sample from well established cohort/study Limitations Limited (and secondary) data on ties between individuals Observational study, analysis relies on modeling, authors then make causal inferences Controversy over statistical methods and interpretation of findings
15 Criticisms of C & F contagion studies Lyons R. The Spread of Evidence Poor Medicine via Flawed Social Network Analysis Claims that social contagion work of C&F contains: Elementary statistical errors and advanced errors Flawed by insufficient attention to assumptions and misinterpretation of results Little can be deduced from the original studies except that we need to improve our statistics education pg. 1.
16 Lyons critique argues Directionality The paper provides speculation, not evidence, for how induction might work Directionality is not statistically significant CIs in Fig 4 overlap (Lyons pg. 5) Differences in net homophily are incorrect use of lagged obesity term means the homophily affects ego and alter in opposite ways (Lyons pg 5 and 6) The differences are consistent with all three possible explanations proposed by C&F Network Models Assumptions underlying regression and model comparison. Data used by C&F is incomplete and thus the network treats some people as not friends when in reality they are friends (pg 7) Language used is of causal inference/prediction, but is merely a numerical comparison of the observed network to a certain random network (pg 8) Increase in risk arises indirectly from the data using statistical models fitted to the data. Model used was mathematically flawed and contradicts the data (pg 9) Assumption of independence for GEE (pg 10)
17 Summary/Discussion Points C & Fs argument that obesity travels through a social network and that an individual s becoming obese is thought to increase the likelihood that his or her friends become obese through social influence is compelling/intuitive??? How to control for the impact/threat of homophily in social network research and social epidemiology Language of directionality and causation (obesity as contagious) when using models C&F reply: We do not claim that this work is definitive, but we do think that it provides some novel sorts of evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data.
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