Sample Feasibility Study of a Dormitory

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					       GRADUATE SCHOOL OF PUBLIC HEALTH
            PILOT GRANT INITIATIVE
    COMPUTATION AND SYSTEM MODELS IN PUBLIC
                   HEALTH



  Social Networks and Tobacco Use in First Year
                College Students

PI: Stephanie R. Land, Ph.D.
   Department of Biostatistics
Research Team

   Brian Primack, MD, MEd, MS, co-I
   Deborah Moss, MD, Co-I
   Ju-Sung Lee, PhD, Consultant
   Melissa Jones, MPH, CHES, Study
    Coordinator
Tobacco use during the
first year of college
   21.2% of Pitt undergraduates smoke
    cigarettes
   Overall, cigarette smoking declines during
    college, although there is some initiation
    (11% of non-smokers in one study)

   Hookah (waterpipe) smoking
    uptake seems to double (~15%
    to ~30% ever use) during the
    first year of college
How does tobacco use relate
to social networks among
first year college students?
Are smoking students in the
center of social life or
marginalized?
How is a student’s tobacco
use predicted by friends’
attitudes?
Are the answers different
for cigarettes versus
hookah?
Smokers in a
cluster of the
Framingham
social
network,
2001-2002
(Christakis,
NEJM, 2008)

Smokers
became
socially
marginalized
from 1971 to
2003
Specific Aims: To
examine…
1.   Feasibility of dormitory social network study
2.   Longitudinal trends in social network and tobacco
     outcomes over the course of the year
3.   Associations between quantitative features of the
     network and tobacco outcomes in college
     students. Additionally, we will estimate the extent
     to which students select friends with similar
     behavior, rather than influencing one another to
     adopt similar behavior.
Research Plan
   Questionnaire survey to be completed in
    residence hall common room.
   Participants: students of 10 selected first-
    year floors (n=280)
   Resident assistant helps to recruit and
    administer survey
   Surveys in March 2009 (pilot), August 2009,
    January 2010.
   Incentives: toys, pizza, raffle iPod Shuffle
Questionnaire

   Items regarding participant (“ego”):
    –   Demographics
    –   Smoking status
    –   Cigarette attitudes, beliefs
    –   Hookah attitudes, beliefs
   Items regarding friends (“alters”):
    – Strength of all pairwise associations (including
      those between alters)
    – Smoking behavior, approval
Mapping the
social network
Software: Statnet Ver. 2.1 (R)
Measures:
 Mean ego-network density = ratio of the
  number of ties among that ego’s contacts,
  divided by the number of possible ties.
 Centrality = number of ties reported by an
  ego
 Cluster size = number of smokers
  connected
Analyses will assume undirected associations.
Aim 1 (Feasibility)
Analysis
   Participation rates (expect n=28/floor)
   Compare demographics of sample to
    University first year students (Χ2 tests)
   Mean ego-network density (0.4 to 0.8
    is typical)
   Adequate variability in centrality
Aim 2 Analysis

   Ever-use of cigarettes at baseline
    versus hookah (McNemar’s test)
   Initiation of cigarettes versus hookah
    (McNemar’s test)
   Tabulate conversion between smoking
    behaviors
   Density and centrality baseline & mid-
    year
Aim 3 Analysis
Repeated measures logistic regression of participant’s
  mid-year cigarette smoking status, with explanatory
  variables:
   – participant’s centrality at baseline & mid-year,
   – participant’s smoking behavior at baseline,
   – smoking behavior at baseline and mid-year of the
     student’s social contacts,
   – perceived smoking approval of the contacts at baseline &
     mid-year,
   – interactions (e.g., between the student’s & contact’s
     baseline smoking)
   – sex, race, educational achievement and socioeconomic
     status of the student.
   To include alters whose relationship either arises or dissolves
     between fall and winter, we will classify alters as: not a
     contact, non-smoking, or smoking at each time point.
Aim 3 Analysis (cont’d)

Selection versus influence?
We will use the exponential random
  graph approach, which models the
  probability of tie creation/deletion
  from baseline to mid-year as a
  function of the behavioral
  characteristics of the alters.
Aim 3 Analysis (cont’d)

   Logistic regression analysis will be
    repeated for hookah, and for smoking
    attitudes and intentions
   Graphically compare smoker cluster
    sizes between cigarette and
    waterpipe, and between time points
Continuation study
   Contingent on additional funding
   Survey same students in April, 2010
   Additional analyses:
    – How associations between network features and tobacco
      variables differ based on demographics and friend
      directionality
    – Permutation test for the existence of clusters of smokers or
      students with pro-tobacco attitudes, beliefs and intentions.
      Clustering in the observed network is compared to that in
      simulated networks.
    – Similarly estimate the influence of one student’s tobacco
      outcomes on another student’s, as a function of their social
      distance (degrees of separation).
Future directions
   Larger college study
   Socially-based interventions for tobacco use
    prevention
   Facebook to obtain network and tobacco
    use information, and transmit smoking
    prevention messages.
   Adult populations, e.g. occupational setting,
   Other health behaviors, including obesity
    and fitness.
Limitations/Strategies
   Not including all alters as participants. E.g. can’t measure
    eigenvector centrality, which counts student’s contacts,
    weighting each contact by the number of other contacts he or
    she has.
   Low participation at baseline? Consider hosting a second
    event or performing assessments via an email survey
    conducted by each RA.
   Participation at winter survey low? Biased self-selection at
    the second time point? Targeted survey of the missing
    baseline participants.
   Sparse social network of isolated individuals? Conduct a
    secondary recruitment of alters.
   Can construct network based on student major and dormitory
    floor residence, although past research regarding tobacco use
    suggests that neighbors are not as influential as friends and
    coworkers.
xij =1 if there is an edge between node i and node j
c(θ) is a normalizing constant. θt is transpose of θ.
David Hunter, http://www.stat.psu.edu/~dhunter/talks/ergm.pdf
 Timeline
                                                              Winter
                                    Spring   Summer    Fall   2009-
                                     2009      2009   2009       10
RA recruitment/ training

Initial pilot survey

Questionnaire design

IRB submission

Initial advertisement of study

First survey

Initial evaluation of feasibility

Second survey

Analysis

				
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posted:8/2/2011
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