Estimating Spatiotemporal Effects for
Ecological Alcohol Intervention Models
Yasmin H. Said
Interface 2008, Durham NC
May23, 2008
Joint work with Edward J. Wegman
Outline
• Motivation and Background
• Intervention Model
– Social Network
– Bipartite Graph Model
• Incorporating Temporal Variations
• Including Spatial Effects
Motivation
• Alcohol Use and Abuse Suppresses Cognitive Function
• Judgment is impaired, which can lead to violence
• Assault and Battery, Murder, Suicide, Sexual Assault,
Domestic Violence, Child Abuse
• Alcohol Use and Abuse Suppresses Motor Function
• DWI, Crashes, Fatalities
• Alcohol Use and Abuse Causes Additional Mortality
and Morbidity
Motivation
• Ecological Approach
• Interaction among
• Users
• Alcoholics
• Casual drinkers
• Heavy users/alcohol abusers
• Young drinkers
• Family, peers
• Non-users
• Producers and distributors of alcohol
• Law enforcement
• Judicial
• Treatment center and prevention activities
• Geographic and spatial interactions among diverse communities
Motivation
• Data
• Geographic local
• Aggregate over types to reduce variability
• Use to calibrate models
• Mobility Simulation including time dynamics
• Mobility modeling including
• Synthetic populations with alcohol related behavior
• Activity generation including visits to distributors
• Conditional probabilities of crashes on the road, violence
at outlets, and other acute outcomes
Motivation
• Evaluation of intervention strategies, particularly sensitivity of
intervention strategies
• Short term (day, months)
• Law enforcement checkpoints
• Safe ride programs
• Location of outlets
• Long term (years, tens of years)
• Aging populations
• Adaptation to intervention
• Impact of education, prevention and treatment strategies on
population strata
• Ultimate Goal
• Reduce overall probability of acute outcomes
Approach
• Our concept is that relatively homogeneous clusters of people,
i.e., agents, are identified along with their daily activities.
• These activities are characterized by different states in the
directed graph, and decisions resulting in actions by an agent
move the agent from state to state in the directed graph.
• The leaf nodes in the graph represent a variety of outcomes,
some of which are benign, but a number of which are acute
alcohol-related outcomes.
• The agents have probabilities associated with their transit from
state to state through the directed graph.
• A very important element is to explore the use of interventions
for the simultaneous suppression of acute outcomes.
Social Network of Alcohol Users
Adjacency Matrix of the Alcohol Network
Graph Model for Interventions
Graph Model for Interventions
Graph Model for Interventions
Temporal Effects
350
300
250
Series1
200
Series2
Series3
Series4
150
Series5
100
50
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Alcohol-Related Crashes by Time of Day
Temporal Effects
Alcohol Related Crashes
300
250
200
Series1
Series2
150
Series3
Series4
100
50
0
MON
TUE
WED
THU
Day of Week
FRI
SAT
SUN
Temporal Effects
16000
14000
12000
10000
8000
Series1
6000
4000
2000
0
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Month of Year
Alcohol-related Crashes by Month of Year
Temporal Effects
• Data: Virginia DMV Records of Alcohol
Related Crashes 2000-2005.
– 896,574 incidents summarized into 2192
instances (356 days by 6 years).
– Data are skewed, normalized with square root
transform.
Temporal Effects
Before Transform
After Transform
Temporal Effects
• One-way Random Effects Linear Model
–
–
–
–
–
yijk =  +i +j +k +ijk
ith day of the jth week of the kth year.
Daily variations highly significant
Week of year variations marginally significant
Yearly variations not significant
Temporal Effects
Temporal Effects
Bipartite Network
Two-Mode Computation
More Two-Mode Computation
Example
• There are 25 Alcoholic Beverage Control (ABC)
stores in Fairfax County, VA (n = 25).
• There are 48 Zip Codes in Fairfax County (m = 48).
• A indicates strength of interaction of Zip Codes
(surrogate for people) with ABC Stores.
• C indicates strength of interaction between Zip
Codes with respect to Alcohol.
• P indicates strength of Interactions between ABC
stores with respect to Alcohol.
Two-Mode Alcohol Network
• The Virginia Department of Alcoholic
Beverage Control periodically surveys
customers to determine where the customers
live.
– The goal is to determine where the Department
of ABC might build new stores.
– Interestingly this is not seen as a conflict of
interest in Virginia.
Two-Mode Alcohol Network
ABC Stores by Zip Codes – Our A matrix
Two-Mode Alcohol Network
ABC Stores by ABC Stores – Our P matrix
Two-Mode Alcohol Network
ABC Store Block Model Matrix - Clustered
Two-Mode Alcohol Network
Zip Code Block Model Matrix – Our C Matrix Clustered
Two-Mode Alcohol Network
Two-Mode Alcohol Network
Zip Codes with Most Customers
22041
Falls Church
2192
20171
Herndon
2016
22003
Annandale
1774
22033
Fairfax
1722
22309
Alexandria
1685
22101
McLean
1666
22015
Burke
1372
20170
Herndon
1302
22194
Woodbridge
1258
22191
Woodbridge
1178
Note: Woodbridge is not in Fairfax County.
Two-Mode Alcohol Network
Zip Codes with Most Distant Customers
24201
Bristol, VA
357 miles
24210
Abington, VA
346 miles
24112
Martinsville, VA 242 miles
24095
Goodview, VA
228 miles
24175
Troutville, VA
213 miles
24502
Lynchburg, VA
169 miles
24593
Appomattox, VA 169 miles
23882
Stony Creek, VA 151 miles
24421
Churchville, VA
138 miles
23860
Hopewell, VA
128 miles
Two-Mode Alcohol Network
ABC Stores with Most Customers
2832
267
McLean
Yes
2532
294
Annandale
Yes
2513
268
Springfield
Yes
2498
357
Reston
Yes
2330
231
Vienna
No
2221
236
Annandale
Yes
2116
235
Alexandria
Yes
2114
228
Alexandria
Yes
1938
120
Alexandria
Yes
1898
82
Sterling
Yes
HIV and Alcohol Connection
• Conjectures
– People at risk for or with HIV tend to be heavy
drinkers (Meyerhoff, 2001)
• HIV => EtOH Use
– People with Alcohol Use Disorder (AUD) are
more likely to contract HIV (NIAAA, 2002)
• EtOH Use => HIV
– What is connection between HIV and AUD?
HIV and Alcohol Connection
• Conjecture
– HIV => EtOH Use – HIV contracted by drug use,
homosexual males, contact with infected blood.
• Alcohol/drugs used as self-medication.
• More likely to be older people, especially males.
– EtOH Use => HIV – Alcohol experimentation and use
frequent among college age and underage drinkers.
• More likely to result in promiscuous, unprotected sexual
encounters.
• More likely to see a higher percentage of younger females.
HIV and Alcohol Connection
• Data Source: Virginia Center for Health Statistics
– Automated Classification of Medical Entities (ACME)
– Death Records:
• Included some traits of the deceased, location of death, and
ICD codes for cause of death.
• 135 Unique locations in Virginia.
• 284,029 deaths recorded in 2000-2004.
• 936 alcohol related deaths.
• 1331 HIV related deaths.
• 7 deaths with both HIV and Alcohol related ICD codes.
• All 7 were males over age of 37.
HIV and Alcohol Connection
• Method:
– Clustering is done by assuming a Poisson
distribution for the 135 units based on overall
population in the 135 units.
– Used a scan statistic method to form clusters
HIV and Alcohol Connection
HIV and Alcohol Connection
HIV and Alcohol Connection
High cluster includes Martinsville, Fairfax, Loudon,
Prince William, Stafford, King George, Caroline,
Hanover and Henrico Counties.
HIV and Alcohol Connection
High cluster includes Martinsville, Colonial Heights, Petersburg,
Richmond, Hampton, Lancaster, Mathews, Norfolk,
Northumberland, Poquoson and Portsmouth.
HIV and Alcohol Connection
HIV and Alcohol Connection
Conclusions
• The connection in the death data is at best inconclusive.
• Alcohol deaths are especially evident in military oriented areas.
• HIV deaths are evident in many areas with African-American
populations.
• Martinsville shows up as a substantial anomaly in alcohol deaths and
HIV deaths.
• The directed graph model allows us to incorporate multiple causative
factors, geospatial information, and multiple acute outcomes into an
agent-based simulation.
• The two-mode social network model allows us to examine the
interaction of individuals and institutions.
– In our example, zip codes are proxies for individuals and ABC stores are
proxies for institutions.
• The interactive agent-based directed graph model allows us to examine
alternative intervention scenarios.
Acknowledgements
• The work of Dr. Said is supported in part by National
Institutes of Alcohol Abuse and Alcoholism under
grant 1 F32 AA015876-01A1.
• The work of Dr. Wegman is supported in part by the
Army Research Office under contract W911NF-04-10447.
• I gratefully acknowledge the assistance of students
and colleagues:
–
–
–
–
Dr. Rida Moustafa
Mr. Walid Sharabati
Mr. Byeonghwa Park and
Mr. Peter Mburu.
Contact Information
Yasmin H. Said
Edward J. Wegman
Department of Computational
and Data Sciences, MS 6A2
George Mason University
Fairfax, VA 22030-4444 USA
Department of Computational
and Data Sciences, MS 6A2
George Mason University
Fairfax, VA 22030-4444 USA
Phone (703) 993-1680
Phone: (703) 993-1691
Cell: (301) 538-7478
Cell: (703) 945-9648
Email: ysaid99@hotmail.com
Email: ewegman@gmail.com