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Examining the Fit Between Environment and Prevention Intervention: A GIS Study

Presented by:

Stephen Gilson, Ph.D., Prevention Center of Excellence, Center for Community Inclusion and Disability Studies, University of Maine, stephen_gilson@maine.edu
Elizabeth DePoy, Ph.D., Prevention Center of Excellence, Center for Community Inclusion and Disability Studies, University of Maine, edepoy@maine.edu

Introduction

In this poster, we present a study that sought to examine the association among substance consumption, population density, and prevention infrastructure.

Literature Review

We summarized the literature on causes of substance abuse that are relevant to prevention, into nine categories:

1. Public health causes
2. Communities as causal
3. Provider scarcity and limited preparation
4. Individual and community level of readiness for prevention
5. Individual behavior causes
6. Human intrinsic factors (intrapsychic, biological)
7. Subpopulation factors
8. Social (family and peer group) causal factors
9. Media-virtual causal factors

Given the multiple causal theories of substance abuse, the following principles for sound prevention were amalgamated from the literature:

1. Prevention should be informed by theory and empirically generated data.
2. Data-based knowledge should form the basis for determining the direct and mediating causes and consequences of substance abuse that prevention efforts should seek to change.
3. Both direct and mediating causal variables should be addressed in prevention.
4. Prevention has a range of targets and scopes—person (P), situation (S) and environment (E).
5. Multi-level approaches are more productive in achieving positive outcomes than singular approaches.

Methods

In order to examine the complex interplay of multiple individual, situational, and environmental factors we undertook a study, relying on geo-coding, to answer the following questions:

1. What is the current prevention infrastructure in place?
2. What is the population density in diverse geographic locations throughout the state?
3. What are the patterns and severity of consumption?
4. What are the relationships among prevention infrastructure, population density, and consumption patterns?

Consistent with state-of-the art methods of inquiry that take into account the multiple theories and prevention principles, we selected Geographic Information Systems (GIS) mapping as our data analytic approach. GIS provides the opportunity to visually locate substance use-specific information within an environmental context and to depict relationships among behavior and important contextual variables such as population density, prevention service presence, highways, and other aspects of the built and natural environment.

We obtained data from interviews, point locations, geographic coordinates, and existing census and consumption databases.

Each variable (infrastructure, population density, consumption patterns, and severity) was mapped separately and then layered to produce the visuals presented in this poster.

GIS Maps (05/17/2006)

GIS Maps provide a visual picture of binary relationships within geographic locations in Maine among the following variables:

1. Population density
2. Prevention infrastructure coverage
3. Consumption of key substances

Maps that identify the coverage areas by township for prevention program are generated from geo-coded data, in this case on town-specific coverage as reported by each core program. Thus, individuals who are covered by these programs outside of the towns are not shown in the maps.

d

Data used to create this map are available by clicking here.

Map #2 provides a visual image of the relationship between core program prevention infrastructure coverage and population density. This map depicts programs and population density by minor civil divisions and thus, does not present data at the smallest level of census measurement. The key depicts the meaning of shapes, color scheme, and lines. Towns and cities are included for geographic orientation only.
 

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d

Data used to create this map are available by clicking here.

Map #5 presents the relationship between binge drinking by county (as measured by valid percentage of reported binge drinking from the Maine Youth Drug and Alcohol Use Survey [MYDAUS] data, 2004) and prevention infrastructure, measured as number of core programs by minor civil division. The key depicts the meaning of shapes, color scheme, and lines. Towns and cities are included for geographic orientation only.
 

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d

Data used to create this map are available by clicking here.

Map #8 presents the relationship between reported marijuana use during the last 30 days by county (as measured by valid percentage of reported marijuana use from the MYDAUS data, 2004) and population density by block group. As noted in the definitions below, census blocks are the smallest level of measurement, and thus, population density is depicted in this map differently than in Map #2. The key depicts the meaning of shapes, color scheme, and lines. Towns and cities are included for geographic orientation only.
 

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d

Data used to create this map are available by clicking here.

Map #13 presents the relationship between illicit prescription use during the past 30 days by county (as measured by valid percentage of reported illicit prescription use from the MYDAUS data, 2004) and prevention infrastructure, measured as number of total programs by minor civil division. The key depicts the meaning of shapes, color scheme, and lines. Towns and cities are included for geographic orientation only.
 

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d

Data used to create this map are available by clicking here.

Map #16 depicts the coverage area by township of the One Maine Partnerships. Population is based on township level of measurement.
 

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Findings

No consistent associations were revealed among the variables.

Implications

In order to identify and tailor prevention programs to specific community need, further research is essential. On the basis of the mapped data presentation, we have selected four communities with equivalent population density and demographic characteristics for comparative investigation (low consumption-low infrastructure coverage; low consumption-high infrastructure coverage; high consumption-low infrastructure coverage; high consumption-high infrastructure coverage).

Selected References

Arizona Prevention Resource Center. (n.d.). Program Inventory/Social Indicators. Retrieved May 19, 2006, from http://www.azprevention.org/Research_And_Reports/
Research_Results_And_Reports/Research_Results_And_Reports.htm

Baranowski, T., Perry, C.L., & Parcel, G.S. (2002). How individuals, environments, and health behavior interact. In K. Glanz, B.K. Rimer, & F.M. Lewis (Eds.), Social Cognitive Theory (pp. 165-184). San Francisco, CA: Jossey-Bass.

DePoy, E., & Gitlin, L. (2005). Introduction to research. St. Louis: Mosby.

DePoy, E., & Gilson, S.F. (2003). Evaluation practice. Belmont, CA: Thomson.

Elder, J.P., Ayala, G.X., & Harris, S. (1999). Theories and intervention approaches to health-behavior change in primary care. American Journal of Preventive Medicine, 17(4), 275-284.

Glanz, K., Rimer, B.K., & Lewis, F.M. (2002). Health Behavior and Health Education, Theory, Research, and Practice. San Francisco, CA: Jossey-Bass.

Hogan, J.A., Gabrielsen, K.R., Luna, N., & Grothaus, D. (2003). Substance Abuse Prevention: The Intersection of Science and Practice. Boston, MA: Allyn & Bacon.

Nigg, C.R., Allegrante, J.P., & Ory, M. (2002). Theory-comparison and multiple-behavior research: common themes advancing health behavior research. Health Education Research, Theory & Practice, 17(5), 670-679.

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