Estimating Spatiotemporal Effects for Ecological Alcohol Intervention Models

1. Estimating Spatiotemporal Effects for Ecological Alcohol Intervention Models Yasmin H. Said Interface 2008, Durham NC May23, 2008 Joint work with Edward J. Wegman

2. Outline • Motivation and Background • Intervention Model • Social Network • Bipartite Graph Model • Incorporating Temporal Variations • Including Spatial Effects

3. 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

4. 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

5. 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

6. 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

7. 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.

8. Social Network of Alcohol Users

9. Adjacency Matrix of the Alcohol Network

10. Graph Model for Interventions

11. Graph Model for Interventions

12. Graph Model for Interventions

13. Temporal Effects Alcohol-Related Crashes by Time of Day

14. Temporal Effects

15. Temporal Effects Alcohol-related Crashes by Month of Year

16. 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.

17. Temporal Effects Before Transform After Transform

18. Temporal Effects • One-way Random Effects Linear Model • yijk =  +i +j +k +ijk • ith dayof the jthweek of the kth year. • Daily variations highly significant • Week of year variations marginally significant • Yearly variations not significant

19. Temporal Effects

20. Temporal Effects

21. Bipartite Network

22. Two-Mode Computation

23. More Two-Mode Computation

24. 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.

25. 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.

26. Two-Mode Alcohol Network ABC Stores by Zip Codes – Our A matrix

27. Two-Mode Alcohol Network ABC Stores by ABC Stores – Our P matrix

28. Two-Mode Alcohol Network ABC Store Block Model Matrix - Clustered

29. Two-Mode Alcohol Network Zip Code Block Model Matrix – Our C Matrix Clustered

30. Two-Mode Alcohol Network

31. 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.

32. 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

33. 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

34. 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?

35. 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.

36. 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.

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

38. HIV and Alcohol Connection

39. HIV and Alcohol Connection

40. HIV and Alcohol Connection High cluster includes Martinsville, Fairfax, Loudon, Prince William, Stafford, King George, Caroline, Hanover and Henrico Counties.

41. HIV and Alcohol Connection High cluster includes Martinsville, Colonial Heights, Petersburg, Richmond, Hampton, Lancaster, Mathews, Norfolk, Northumberland, Poquoson and Portsmouth.

42. HIV and Alcohol Connection

43. HIV and Alcohol Connection

44. 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.

45. 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-04-1-0447. • I gratefully acknowledge the assistance of students and colleagues: • Dr. Rida Moustafa • Mr. Walid Sharabati • Mr. Byeonghwa Park and • Mr. Peter Mburu.

46. Contact Information 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: ysaid99@hotmail.com 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: ewegman@gmail.com