An article in the UK Guardian newspaper focuses on a recently published meta-analysis that shows that social relationships are associated with a longer life (yes, this is not new news). I thought that the structure of the Guardian article was was good and made the presentation very accessible. Another positive was that they provided a reference to meta-analysis article at the end.
The Guardian article can be read here: http://www.guardian.co.uk/lifeandstyle/besttreatments/2010/jul/27/can-your-friendships-save-your-life The meta-analysis was published in the Public Library of Science (PLoS) Medicine journal and can be accessed here: http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1000316 The reference for the article is: Holt-Lunstad J, Smith TB, Layton JB, 2010 Social Relationships and Mortality Risk: A Meta-analytic Review. PLoS Med 7(7): e1000316. doi:10.1371/journal.pmed.1000316 The results section from the abstact of the article is quoted here: |Results | |Across 148 studies (308,849 participants), the random effects |weighted average effect size was OR = 1.50 (95% CI 1.42 to 1.59), |indicating a 50% increased likelihood of survival for participants with |stronger social relationships. This finding remained consistent across |age, sex, initial health status, cause of death, and follow-up period. |Significant differences were found across the type of social measurement |evaluated (p<0.001); the association was strongest for complex measures |of social integration (OR = 1.91; 95% CI 1.63 to 2.23) and lowest |for binary indicators of residential status (living alone versus with others) |(OR = 1.19; 95% CI 0.99 to 1.44). A couple of notes: (1) Chen, Cohen, & Chen (2010) provide a method for converting an odds ratio into Cohen's d effect size measures (where d=0.20 is small, d=0.50 is medium, and d=0.80 is large). Converting the average OR=1.50 produces an approximate d of 0.20, that is, the effect size would be considered small. (NOTE: there are additional considerations in making the conversion and additional info is needed to make a valid conversion). See: Chen, H., Cohen, P. & Chen, S. (2010). How Big is a Big Odds Ratio? Interpreting the Magnitudes of Odds Ratios in Epidemiological Studies. Communications in Statistics - Simulation and Computation, 39(4), 860-864. doi:10.1080/03610911003650383 (2) The last result above, the Odds Ratio when a simple question is asked (i.e., do you live alone vs live with others) is 1.19 but the 95% Confidence Interval contains the value of 1.00, that is, the interval contains the null hypothesized value for no association. This implies that one cannot use such a simple question as an indicator of the degree/type of a person's social relationships. (3) Although the overall odds ratio is 1.50, there was significant heterogeneity in effect sizes across studies. Quoting the original article: |Odds ratios ranged from 0.77 to 6.50, with substantial heterogeneity |across studies (I2= 81% [95% CI = 78% to 84%]; Q(147)= 790, |p,0.001; t2 =0.07), suggesting that systematic effect size variability |was unaccounted for. Thus factors associated with the studies |themselves (e.g., publication status), participant characteristics |(e.g., age, health status), and the type of evaluation of social |relationships (e.g., structural social networks versus perceptions |of functional social support) may have moderated the overall results. (page 4) The article goes on to evaluate these different sources with most attention on evaluating the role of types of social relationships. (4) The authors appears to be reasonably circumspect in their conclusions. Quoting from the article: |This meta-analysis also provides evidence to support the |directional influence of social relationships on mortality. ... |However, causality is not easily established. One cannot randomly |assign human participants to be socially isolated, married, or in a |poor-quality relationship. A similar dilemma characterizes virtually |all lifestyle risk factors for mortality: for instance, one cannot |randomly assign individuals to be smokers or nonsmokers. Despite |such challenges, ‘‘smoking represents the most extensively |documented cause of disease ever investigated in the history of |biomedical research’’ [183]. The link between social relationships |and mortality is currently much less understood than other risk factors; |nonetheless there is substantial experimental, cross-sectional, and |prospective evidence linking social relationships with multiple pathways |associated with mortality (see [182] for review). Existing models |for reducing risk of mortality may be substantially strengthened by |including social relationship factors. So, will you get by with a little help from your friends? -Mike Palij New York University [email protected] --- You are currently subscribed to tips as: [email protected]. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13090.68da6e6e5325aa33287ff385b70df5d5&n=T&l=tips&o=3845 or send a blank email to leave-3845-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
