Estimating Spatiotemporal Effects for Ecological Alcohol Intervention Models Yasmin H. Said...

Estimating Spatiotemporal Effects for Estimating Spatiotemporal Effects for Ecological Alcohol Intervention Models Ecological Alcohol Intervention Models

Yasmin H. Said

Interface 2008, Durham NCMay23, 2008

Joint work with Edward J. Wegman

OutlineOutline

• Motivation and Background• Intervention Model

– Social Network

– Bipartite Graph Model

• Incorporating Temporal Variations• Including Spatial Effects

MotivationMotivation

• Alcohol Use and Abuse Suppresses Cognitive FunctionAlcohol Use and Abuse Suppresses Cognitive Function

• Judgment is impaired, which can lead to violenceJudgment is impaired, which can lead to violence

• Assault and Battery, Murder, Suicide, Sexual Assault, Assault and Battery, Murder, Suicide, Sexual Assault, Domestic Violence, Child AbuseDomestic Violence, Child Abuse

• Alcohol Use and Abuse Suppresses Motor FunctionAlcohol Use and Abuse Suppresses Motor Function

• DWI, Crashes, FatalitiesDWI, Crashes, Fatalities

• Alcohol Use and Abuse Causes Additional MortalityAlcohol Use and Abuse Causes Additional Mortality and Morbidity and Morbidity

• 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


• 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


• 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


ApproachApproach• 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 UsersSocial Network of Alcohol Users

Adjacency Matrix of the Alcohol Network Adjacency Matrix of the Alcohol Network

Graph Model for InterventionsGraph Model for Interventions

Temporal Effects

0

50

100

150

200

250

300

350

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Series1

Series2

Series3

Series4

Series5

Alcohol-Related Crashes by Time of Day

Temporal EffectsAlcohol Related Crashes

0

50

100

150

200

250

300

MON TUE WED THU FRI SAT SUN

Day of Week

Series1

Series2

Series3

Series4

Temporal Effects

0

2000

4000

6000

8000

10000

12000

14000

16000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month of Year

Series1

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

Bipartite NetworkBipartite Network

Two-Mode ComputationTwo-Mode Computation

More Two-Mode ComputationMore Two-Mode Computation

ExampleExample

• 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 NetworkTwo-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.


ABC Stores by Zip Codes – Our A matrix


ABC Stores by ABC Stores – Our P matrix


ABC Store Block Model Matrix - Clustered


Zip Code Block Model Matrix – Our C Matrix Clustered

Two-Mode Alcohol NetworkTwo-Mode Alcohol NetworkZip 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 NetworkTwo-Mode Alcohol NetworkZip 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 NetworkTwo-Mode Alcohol NetworkABC 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



1898 82 Sterling Yes

HIV and Alcohol ConnectionHIV 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?


• 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.


• 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.


• 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


High cluster includes Martinsville, Fairfax, Loudon, Prince William, Stafford, King George, Caroline,

Hanover and Henrico Counties.


High cluster includes Martinsville, Colonial Heights, Petersburg, Richmond, Hampton, Lancaster, Mathews, Norfolk,

Northumberland, Poquoson and Portsmouth.

ConclusionsConclusions• 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.

AcknowledgementsAcknowledgements

• 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-04-1-0447.

• I gratefully acknowledge the assistance of students and colleagues:– Dr. Rida Moustafa– Mr. Walid Sharabati– Mr. Byeonghwa Park and – Mr. Peter Mburu.

Contact InformationContact Information

Edward J. Wegman

Department of Computational and Data Sciences, MS 6A2 George Mason University Fairfax, VA 22030-4444 USA

Phone: (703) 993-1691

Cell: (703) 945-9648

Email: [email protected]

Yasmin H. Said

Department of Computational and Data Sciences, MS 6A2 George Mason University Fairfax, VA 22030-4444 USA

Phone (703) 993-1680

Cell: (301) 538-7478

Email: [email protected]

Estimating Spatiotemporal Effects for Ecological Alcohol Intervention Models Yasmin H. Said...

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