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Community planning and the prevention of alcohol involved traffic problems
and Program Planning, Vol. 11, pp. 261-271, Printed in the USA. All rights reserved.
Evaluation
0149-7189/88 $3.00 + .OO Copyright 0 1988 Pergamon Press plc
1988
COMMUNITY PLANNING AND THE PREVENTION OF ALCOHOL INVOLVED TRAFFIC PROBLEMS
An Application
of Computer Simulation
HAROLD D. HOLDER and JAMES 0.
Technology
BLOSE
Prevention Research Center, Berkeley, California
ABSTRACT This paper is an assessment of the application of computer simulation to community-based planning for the prevention of alcohol-involved traffic problems in the County of San Diego, CA. In this project, a computer model was used to assist local planners in (a) documenting the extent of problems, (b) better understanding the complex system which yields alcoholinvolved problems, and (c) projecting the potential ejfects OJ-various prevention strategies. Advantages and limitations of such technology in community planning are discussed.
INTRODUCTION education efforts to reduce drinking and driving recognize that this situation may very well influence the success of their program. There are many parts of the community system contributing to the use of alcohol in conjunction with driving which planners need to recognize. Exogenous factors such as trends in employment or disposable income, largely outside the domain of traffic safety prevention efforts, can impact alcohol use and misuse (see Cook & Tauchen, 1982; Grossman, Coate, & Arluck, 1987; Moore & Gerstein, 1981; and Ornstein & Hanssens, 1981). While planners and community leaders may intuitively appreciate that alcohol-involved traffic problems are impacted by many diverse factors, they do not generally have at their disposal the tools or technology to translate intuitive understandings into concrete relationships. This paper describes one such technology, computer simulation, and its use in a recent community planning effort. See Holder, Blose, and Ryan (1988) for a detailed evaluation of the project and documentation of the computer model utilized.
While the importance of community planning for the prevention of traffic safety problems has been acknowledged, local planning efforts are often handicapped by a lack of resources. Given the limited availability of funds to implement and operate prevention programs, it is not surprising that resources for the technological support of prevention planning are seriously constrained. It is an understatement to observe that traffic safety problems (manifested as fatalities, injuries, and property damage) are part of a complex system. The issue of alcohol involvement further complicates the picture. Not only must the factors of speed, weather, road, and vehicle design, and “normal” driver errors be considered, but so must driver impairment resulting from drinking, as well as the social and economic factors surrounding the community use of alcohol in high-risk behavior. Unfortunately, this complexity is rarely appreciated in practice. For example: in many states chilled beer (the beverage of choice among the young) is readily available at gas stations at a price comparable to soft drinks. Yet seldom do those instituting school-based
Requests for reprints should be sent to Harold D. Holder, PhD, Prevention Research Center, 2532 Durant Avenue, Berkeley, CA 94704.
267
268
HAROLD
D. HOLDER
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0. BLOSE
PROJECT GOALS The purpose of the project described here was to assess the ~ppIication of computer simulation as a means to aid community planning to reduce alcohol-involved traffic problems. Under joint funding from the County of San Diego (California) Alcohol Program and the National Highway Traffic Safety Administration, the project was part of a national-local collaboration to develop and test new approaches and technologies to
FORMATION
aid communities to reduce alcohol-related problems in general and traffic probtems in particular. The development of a San Diego County computer model was an effort to involve the leadership of San Diego County in identifying alcohol-involved traffic problems which the community desired to address and to increase awareness of the larger community system of which drinking and driving is but a part.
OF A COMMUNITY
Because an ongoing interagency group concerned with drinking and driving did not exist in San Diego County, a Community Advisory Committee (CAC) was formed for the project. The group was designed to be diverse in membership and representative of the major interests and institutions involved with traffic safety issues. Included were representatives of law enforcement, the district attorney’s office, the judiciary, public schools, local colleges, U.S. Navy (a major Naval facility is located in the San Diego area), and the County Alcohol Program. The CAC provided community Ieaders the opportunity for direct participation in model specification and in designing potential community prevention strategies which could be evaluated through computer simulation. Using driver fatalities and injury crashes as focal points, the CAC meetings documented the historical levels and patterns of alcohol-involved traffic problems in San Diego County, and stimulated discussion and documentation of the community system which involved alcohol use in general and drinking and driving in particular. One of the tasks undertaken by CAC members was to recommend new prevention strategies (i.e., ones which were not a part of existing efforts or current law) which they thought would reduce driver fatalities and injury crashes in the long term. Eighteen such strategies were identified. Before the impact of any particular strategy could be evaluated via computer simulation, it was necessary to understand the relationship between the system changes which the strategy would bring about and traffic outcomes. Thus the existence of adequate research and evaluation data on each strategy was a prerequisite for its inclusion in the simulations. Based on the availability of data on each countermeasure, eight were selected for simulation: I. School-based Education Program: A school-based program focusing on alcohol consumption was postuiated, having the assumed objectives of teaching students about alcohol and its effects and creating appropriate attitudes toward alcohol use and
2.
3.
4.
5
7.
PLANNING
GROUP
drinking and driving (see Mann, Vingilis, Leigh, Anglin, & Blefgen, 1986; McKnight, 1986; and Schaps, Moskowitz, Malvin & Schaeffer, 1984). increased Driving-Under-the-influence (DUI) Enforcement in High Risk Areas: An enforcement program similar to the successful well-documented Stockton, California, effort was proposed. The Stockton program was a special enforcement effort with accompanying publicity which utilized special patrol forces as a supplement to regular patrols on Friday and Saturday nights between 8 p.m. and 4 a.m. The special project involved a series of enforcement waves to sustain its effect on reduced drinking and driving (see Voas & Hause, 1987). Mandatory License Suspension for Convicted DUI Offenders: This involved a six-month mandatory license suspension for all convicted DUI offenders. While judges currently have the authority to issue such sentences, this sanction is seldom used in San Diego (see Peck, Sadler, & Perrine, 1985; Sadler & Perrine, 1984; Tashima & Peck, 1986; and Williams, Hagen, & McConnelI, 1984). Mandatory Jail for Convicted DUI First Offenders: A two-day mandatory jail term for all persons, adult and juveniIe, convicted of a first DUI offense was postulated (see California Department of Drug and Alcohol Programs, 1982; Helander, 1986; National Highway Traffic Safety Administration, 1983, 1986; Ross, 198.5; and Voas, 1986). and 6. Lower the Legal BIood-AIcohol-Concentration (BAC) Limit for conviction of drinking and driving to .08 or to .05. Currently, in California if a person is arrested with a tested BAC of .lO, then they are considered an alcohol-impaired driver. In other words, the current per se level for conviction for Driving-Under-the-Influence (DUI) is .lO. These proposed interventions would require legislation to Iower the per se BAC level for conviction of DUI to .08 or to .05 (see Holder & Blose, 1983; and Waller, 1985). Increased Retail Price of AIcoholic Beverages: This intervention assumed a 25% increase in retaii price but did not specify the particular means to accom-
Community plish this (whether via a tax increase, price setting, etc.) (see Cook & Tauchen, 1982; Grossman et al., 1987; Ornstein & Hanssens, 1981; and Phelps, in press). 8. Increased Treatment for Multiple DUI Offenders: This intervention involved a doubling of the propor-
COMPUTER
MODELS
Computer simulation is a research and policy evaluation technique which has been utilized to investigate traffic problems and changes in traffic indicators as a result of system-level shifts. However, this technique has rarely been applied to reducing alcohol-involved traffic problems. Some researchers (Cook, Holder, Kennedy, & Sawyer, 1973; Holder, 1974; and Schlenger, Haywood, & Hallan, 1976) have applied computer models to various aspects of alcohol misuse and abuse but did not include drinking and driving. Summers and Harris (1978) conducted a computer simulation of the general deterrence of driving while intoxicated but this was not applied to specific communities and did not consider variations in drinking behavior. This project utilized a computer-based model which had been developed and tested initially utilizing
MODEL OVERVIEW While the full documentation of the computer model is beyond the scope of this paper (see Holder & Blose, 1983, 1987; Holder et al., 1988), the major conceptual components of the drinking and driving subsystem of the model are shown in Figure 1. This illustrates in general form the interaction between the community consumption of alcohol (quantity and frequency by gender and age) and the distribution of driving events by Blood Alcohol Concentration (BAC) by age, gender, and drinking practices. Drinking in the model is influenced by previous drinking patterns, cultural values and norms, retail price, disposable income, beverage marketing, and the minimum purchasing age. The drinking and driving distribution is influenced by community drinking patterns and the amount of driving, as well as by public pressure for enforcement and conviction, the specified BAC level for conviction for DUI, actual enforcement and conviction levels, and the perceived risk of sanction. The distribution of driving events interacting with the risk of driver fatalities and injury crashes yields annual model estimates for these outcomes. The model structure is based upon research concerning the various system components as well as on national data. Local data were used to load and run the San Diego County model. A summary of both the model structure and the local data sources is provided in Holder et al. (1988). Before the model was used to project future trends,
Planning
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tion of repeat DUI offenders who are referred to multiple offender rehabilitation programs (see Helander, 1986; Mann, Leigh, Vingilis, & DeGenova, 1983; Peck et al., 1985; Perrine, 1984; and Waller, 1983).
AND SIMULATION national level data and subsequently field-tested in three U.S. counties: Wake County, NC; Washington County, VT; and Alameda County, CA. This prototype model, which simulates the diverse variables comprising the system of drinking patterns and drinking/ driving behavior in a community, had been used to assess the likely impact of various intervention strategies on alcohol-involved traffic problems but was not previously undertaken with the direct participation of any of the three communities in its design or application (Holder & Blose, 1983, 1987). A unique feature of the project described here was the joint participation of CAC members and project staff in loading the model with San Diego County data and in specifying the alcohol countermeasures and prevention strategies to be evaluated.
AND VALIDATION a historical validation was carried out to verify that it could successfully recreate past trends. A 17-year period (1970 to 1986) was used to validate both the alcohol consumption subsystem (the overall use of alcohol in the community) as well as the drinking and driving subsystem. Validation is a test of the understanding of the overall system as expressed by the relationships among variables in the model. The historical data used for benchmark validation were not loaded directly into the model. Figure 2 illustrates that the model estimates of driver fatalities for the 1970-1986 period provided a relatively good fit to the actual data. Similar results were obtained for other measures. For a more detailed discussion of model validation see Holder et al. (1988). As shown in Figure 2, the model projected both driver fatalities and injury crashes into the future (1987-1994) to establish a benchmark for comparison with projected changes from alternative alcohol countermeasures. The benchmark projections were based on the following assumptions: (a) no new traffic safety prevention efforts are instituted during the 1987-1994 period; (b) projections of County population and personal income (1987-1994) developed by the San Diego Association of Governments (SANDAG) were used; (c) changes in the retail price of alcoholic beverages and alcohol marketing trends will be similar to trends during the previous 10 years; and (d) per capita vehicle miles traveled will remain roughly constant.
HAROLD
D.
HOLDER
and JAMES
0. BLOSE
POPlJlAnON CONSUMPnON DlSTRl6lJTlON
DlSTRlSUTlON Of DRIVING E-VENTS BY SAC FOR INDIVIDUALS OF DIFFERING AGE. SW. CONSUMPnON
Figure
1. Drinking
MODEL
and driving
sector: conceptual components.
SIMULATION
For purposes of the simulations it was assumed that each countermeasure could be successfully implemented and that each would result in the system changes that the research literature suggests. The project did not assess if the strategy was politically or economically feasible. For example, in the case of the price increase strategy, we assumed that a price increase of specified magnitude was carried out and predicted declines in consumption occurred as a result. We did not specify the specific operational form of the price increase nor assess its political feasibility. Each prevention effort was operationalized in the model by modifying the key system factors which each
RESULTS
program could be expected to effect based on published research and evaluation studies. The internal dynamics of the model, as a representation of the actual system, then determined the eventual impact of the program on traffic safety outcomes. Figure 3 provides a general illustration of how a program which alters some component of the system may cause a change in community consumption of alcohol and/or drinking and driving behavior, ultimately affecting traffic outcomes. Each strategy was implemented in the model in 1987. The simulated impact of each strategy on driver fatalities and injury crashes was then compared with the “business-as-usual” projections described earlier. Fol-
Community
271
Planning
Q. Actual .I- Computer Projsctionr
0-l
I
; : : : : : : : : : : : : : : : : : : : : : : IYsar
1970 1972
1974
1976
1970
Figure 2. Driver fatalities.
Program B
Program A
I
1980
1982
1984
Actual and computer
Program
C
Specific Changes in System FaCtor
J \
Community Consumption of Alcohol _
Or inking and Driving &bvior
Traffic Problems Figure 3. Illustration termeasures.
of system changes suggested by alcohol coun-
lowing is a brief discussion of the simulation results, grouping the interventions by general approach: (a) strategies targeting convicted DUI offenders, (b) strategies focused on enforcement, and (c) strategies focused on the community as a whole. Plots of computer simulation results, comparing “business-as-usual” and alternative prevention strategies, are shown in Figures
1986
1988
projections,
1990
1992
1994
San Diego County,
CA - 1970 to 1994.
4 through 6 for driver fatalities. The projected in injury crashes, although somewhat smaller, ilar in pattern.
changes are sim-
Enforcement Strategies Of this group of strategies, the high-risk area enforcement campaign had the greatest short-term impact (driver fatalities dropped by 15% during the first year and injury crashes by 6.4%). By the end of the simulation period much of this effect had dissipated. Lowering the legal BAC limit produced a smaller initial reduction which increased slightly over the period of the simulation. A reduction in the legal limit to .05 BAC yielded a short-term projected reduction of 5.4% and a long-term reduction of 7.6% in driver fatalities. DUI Offender Strategies Mandatory license suspension had a substantial immediate impact followed by a decay in effect. While short-term effects (9.3% for driver fatalities and 4.2% for injury crashes) were somewhat lower than for the targeted enforcement campaign, the long-term impact was comparable. The effect of mandatory jail for first offenders followed a similar trend, although the impact was much more modest. Increased treatment of multiple offenders had virtually no impact on either outcome measure. Community Strategies The price increase strategy yielded slight reductions in driver fatalities and injury crashes within one year, but by 1994 the projected impact amounted to a 12.7% reduction in driver fatalities and a 4.6% decline in injury crashes. The school-based educational effort
HAROLD
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D. HOLDER
and JAMES
0. BLOSE
Deaths 350 340
320-310-300 ^_ 290280 --
Usual(Basaline) + Mandated Jail
Figure 4. Driver fatalities.
Business as usual and IXJI offender
strategies,
San Diego County,
CA - 1985 to 1994.
330
310--
290
270--
230 6,: 1965
i Year 1966
1987
Figure 5. Driver fatalities.
1988
1969
1990
1991
Business as usual and enforcement
produced a 2.5% short-term reduction among young driver fatalities and only a slight long-term impact. Because the outcome measures used in these simulutions include all driver fatalities and injury crashes, reductions in alcoh~~l-in~~~ived driver fatalities and injury crashes would be expected to be much greater DOClJMENTATION
1992
strategies,
1994
San Diego County,
CA-
1985 to 1994.
than those just described.
The simulation results presented above-indeed the results of any simulationare only as accurate as the data and research findings used to build the model and the assumptions made about future exogenous factors.
OF CHANGES
One of the central goals of this project MQSto increase awareness among community leaders of the nature and potential impact of various co~lntermeas~lres for rcdui-
1993
IN BELIEFS
ing drinking and driving problems, and especially to increase their awareness of system-level interventions. in order to document these changes we adrt~inistered a
Community
273
Planning
i
.Q School-based Education
( -+Price 1
230 -i
1985
1986
1987
Figure 6. Driver fatalities.
I
Increase j
I Year 1988
1989
1990
1991
Business as usual and community
set of surveys of perceptions, values, and beliefs regarding drinking and driving and the use of alcohol more generally. The instrument has been used in other efforts in California to document community leader opinions about alcohol prevention policy. Results are shown in Table 1. The preliminary wave of surveys was given at the beginning of the first CAC meeting (January 1987) prior to the initiation of any discussion or training. The second wave was administered at the end of the final meeting after the CAC had reviewed and discussed the results of the computer simulations (September 1987). Respondents were asked to indicate intensity of
1992
strategies,
1994
San Diego County,
CA-
1985 to 1994.
agreement or disagreement on a 1 to 5 scale-the lower the score the more agreement with the statement. The results suggest that after working with the computer model, CAC members had both an increased understanding of systemic factors impacting drinking and driving, as well as a greater appreciation of the potential of certain countermeasures. For example, Table f shows that respondents were more likely to support increased alcohol prices and limits on alcohol availability. Respondents were also more likely to favor use of license suspension and less likely to favor mandatory jail terms or school-based education than they had been earlier.
OBSERVATIONS ON SIMULATION THE COMMUNITY PLANNING The Community Advisory Committee was constituted specifically for this project. GAC members did not have any previous forum for sitting together around a common concern for alcohol-involved traffic issues and for identifying the various approaches and contributions which each could make to community-oriented prevention. The project provided a process in which the participants could learn from each other and could integrate ideas about the community as a system. The computer model-its operationalization with local data, its validation, and its use to simulate a series of potential countermeasures-provided a focal point for this learning. Participation in the CRC process allowed the diverse concerns and interests of the participants to be discussed and a concrete product, a series of computer evaluations of potential countermeasures, resulted.
1993
MODELING PROCESS
AND
While the CAC functioned as a mechanism for information sharing and dissemination, a critical first step in community planning, an ongoing community planning process would involve much more than this. It would be unrealistic to expect the CAC process to result in concrete planning steps or programmatic decisions during the nine month time frame of the project -and indeed the CAC had no such mandate. One basic question to be addressed at the outset was whether a computer simulation would be accepted as a tool in a community context. In the present case the answer is clearly yes. In evaluating the implications of this conclusion we recognize that San Diego County officials appeared well-informed about alcohol problems. Members of the CAC recognized that their organizations and agencies currently deal with only a small part of the picture and that there were factors
274
HAROLD
D. HOLDER
and JAMES
0. BLOSE
TABLE 1 COMMUNITY Some
of the following
strategies
the level of your agreement
a)
Require
labels
LEADER
have been
suggested
or disagreement
on all alcoholic
SURVEY:
for reducing
containers
OF RESULTS
the amount
position statement
with each
beverage
SUMMARY
(PRE AND POST]
and severity
of alcohol-related
problems.
Please
indicate
below.
to warn the user of potenital
long and short
term health
Pre
Post
Mean * --
Mean *
1.83
1.76
1.67
7 -41
1.67
1.65
1.94
1 35
1.89
2 06
nsks
bl
Increase
police
cl
Restrtct
alcohol
sales
In locations
d)
Increase
excise
taxes
on all alcoholic
e)
Impose
mandatory
f)
Increase
funding
9)
Permit
h)
Increase
funding
includrng
sales
i)
Requtre
k)
and/or
to state
Require
broadcasters
the health
(e.g. sports
stadrum,
outdoor
concerts)
stops
Beverage
for all persons
programs
to test for cinving
Control
convicted
of driving
under
the influence
tn the schools
agencies
under
to improve
the influence enforcement
of regulations
1.61
1 88
2 28
1 88
2.1 1
2 18
2.33
2 00
2.17
1.94
2.33
2.00
2.33
1 88
2.39
1 65
laws
who then
the advertising
to follow
beverages
random
Alcoholtc
and driving
is likely
of at least 46 hours
that bars and restaurants
Prohibit
of drinking driving
and drug education
acceptable
to minors
patrols where
jail sentences for alcohol
constitutionally
age customer
ji
surveillance
share
harms another of alcoholic
to provtde
financial
responsibility
if they
serve an obviously
intoxrcated
or under
person
beverages
time equal
on radio and television
to that devoted
to alcohol
advertising
for messages
regarding
risks of alcohol
1)
Requtre
Immediate
ml
Prohibit
the sale of alcoholic
adminrstratrve
revocation
beverages
of drivers
license
for drivrng
at gas stations
under
the influence
N= *Based
on scale
of “1 ” (Strongly
Agree)
to “5”
(Strongly
Strengths
introducing
N=
17
Disagree)
outside their direct control which affect the extent of drinking and driving. A systems approach to alcoholinvolved traffic problems thus did not appear to pose great difficulty for many Committee members. We should also point out that San Diego is the home base of the America’s Cup and the winning sail boat skippered last year by Dennis Conners. This event received extensive publicity and the community was quite aware that computer models of hull design were utilized during the developmental phase of the sail boat project. As a result, simulation technology was already viewed as capable of producing viabte results. It is possible that efforts to utilize and gain acceptance of computer modeling by community leaders in other locations may have more difficulty. Our experience in this project indicated a number of specific strengths and limitations of applying computer modeling to commLlnity ptanning for the prevention of alcohol-involved traffic problems.
1. A computer
18
model can be an effective means of local data-as well as knowledge from
research and evaluation studies-into a planning process. While there are certainly other methods of accomplishing this, the model is a data-based tool that does not require users to absorb and analyze the data themselves. Rather they evaluate the results of simulation projections which use data and research findings as input, awareness of the 2. The model increases community system surrounding drinking and driving behavior. The model can assist community leaders and planners to recognize and understand relationships between diverse issues and factors which operate in the real world. Many of these factors and relationships are largely unrelated to their specific professional responsibilities but can affect the results of their efforts to reduce problems. The total system is composed of many parts; each part is often the concern of separate go~ernnleIlt agencies which may interact only minimally. Thus no agency can see the whole system. 3. The computer model facilitates the comparison of alternative programs, stimulating planning group discussions about the relative long-term merits of diverse strategies and providing a more en~piricalIy
Community grounded basis for deliberations than might otherwise occur. 4. A computer model is a versatile, multi-purpose tool. In addition to simulating prevention strategies, it can be used to assess differing assumptions about the likely course of future events (e.g., trends in population growth or the economy) and can be modified to reflect differing assumptions about the nature of the drinking and driving system itself. Limitations 1. Some data collection is required-and thus some level of community commitment and resources. Most communities are unlikely to have all the necessary data readily available. Few longitudinal studies of some key system variables existed in San Diego. For example, perceived risk of detection could only be inferred from a newspaper content analysis. This situation is quite likely to exist in other localities as well.
Planning
275
2. At least in its present form, the model is not a tool that a community can use on its own. Assistance from technical experts is required to use it effectively. 3. Although the model incorporates the best available knowledge about countermeasures, that knowledge is incomplete and the accuracy of model predictions of the potential impact of strategies is variable. Estimates for some interventions are likely to be more on the mark than others because we know more about some strategies. 4. It is important that participants in the planning process understand the limitations of the model (or any other planning aid or technology) if it is to.be appropriately used. Assuring such understanding is not an easy task. Many people may be likely to place undue confidence in anything involving computers. If participants do not understand the limitations and end up making decisions expecting all model predictions to come true, they may be soured on future uses of modeling or planning in general.
IMPLICATIONS Over the past two decades, computer modeling has been widely applied to governmental planning and operations, especially at the Federal level (see Fromm, Hamilton, & Hamilton, 1974; Gass & Sisson, 1974; Pugh, 1977; and U.S. GAO, 1982). The usefulness of many of these models has been questioned by some (see Ascher, 1978; Brewer, 1973, 1983; and Fromm et al., 1974). Among the issues raised are the concern that the utility of models may sometimes be oversold by their designers; that due to inappropriate design, delays in development or other reasons many models are in fact never used in practice; that modelers are too often insensitive to the political and organizational context within which their work will be used and insufficiently aware of differences in perspectives between researchers and model designers on the one hand and policymakers and program designers on the other. These concerns are strikingly similar to those raised by evaluators and policy analysts regarding their own work (for example, Behn, 1981; Bryk, 1983; Etzioni, 1985; Leman & Nelson, 1981; Patton, 1978; Weiss, 1973; and Wildavsky, 1966). Indeed as Kain (1978) has observed, the real issue is not whether criticisms of some computer modeling applications are justified, but how simulation compares to other quantitative tools used in policy planning-a matter which has yet to be carefully examined. In assessing the utility of the effort reported here, it is important to recognize that this project differed in several critical ways from the vast majority of computer modeling applications to policy and planning. First, an existing model structure was used for the proj-
ect, with only modest modifications (Holder, Blase, & Ryan, 1988). One criticism of the application of computer modeling to urban land use planning in the 1970’s was that planners in hundreds of local jurisdictions were trying to deal with the same conceptual and methodological issues, yet operated largely independently (Kain, 1978). The approach used here has the virtue of not having to start from scratch for each local application. Of course applying a model originally developed as a research tool to a community context raises other issues. Such a model may be more complex and present more data collection requirements than are actually needed for effective community planning applications. Only further testing of the larger model-and its further application to community planning-can resolve this issue. A second difference between the use of the model reported here and most other policy applications is the role of the model itself in the planning process. In most applications, models are used to evaluate alternative policies or provide forecasting estimates in order to make specific action recommendations to policymakers or planners. This was not the role of the model in this project. Rather, as discussed earlier, the model was used as a tool to assist participants in understanding the nature of the complex factors involved in drinking and driving in a community. Thus the role of CAC members in applying the model was as important as the results of the simulations themselves. Further, the simulation results were used not to provide specific action recommendations but to increase participants’ under-
276
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D. HOLDER
standing of specific issues. For example: the simulations showed that interventions targeted only at convicted offenders-whatever their other merits -are likely to have a limited long-term impact on community outcomes, probably because most drinking drivers are never apprehended. The complexity of a sophisticated computer-based model creates a considerable gap between model designers and community participants (see Brewer, 1983; and Pugh, 1977). As an example of the broader dilemma posed by the reliance on technical expertise to solve problems in our society (Brewer, 1983) this raises a number of important issues. To what extent do community policymakers need to grasp the complexity of the model’s design? To what extent do they need to be able to evaluate all the assumptions and necessary interpolations from research and data bases outside their community to use the model appropriately? Of course some decisions must be made by model developers. The model will inevitably bear the stamp of their values (Brewer, 1983), thus providing some limits to community involvement. At some point, however, we create an undesired dependency upon the creativity, interpretation, and ideas of model-builders. Certainly communication is one critical issue here (Fromm, et al., 1974; Pugh, 1977). Yet if community leaders or plan-
and JAMES 0. BLOSE ners are not able (or are unwilIing) to undertake the difficult thinking necessary to improve their understanding of their own community system, then the community selection of prevention interventions will have limited long-term effectiveness. We believe that the long-term reduction of alcoholinvolved traffic problems requires that community prevention planners employ some of the same conceptual work and longitudinal research required for a useful computer model, even if computer modeling technology itself is not used. Such an approach must include not only the examination of individual driver characteristics and patterns of alcohol-inlpaired driving, but also a consideration of the larger social and economic context in which community drinking and driving is imbedded. Contrary to the implicit assumption in much traffic safety countermeasure research, the use of alcohol within the total ~onlmunity-including its retail price, availability, and community values about acceptable and unacceptable drinking-cannot be ignored. In the end, both researchers and community planners will have to extend their thinking about effective countermeasures beyond those factors which have traditionally been considered. Until such thinking is a regular part of our efforts to reduce alcohol-involved traffic problems, we will likely repeat many of the errors of the past.
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Policy Anal-
G. (1973). Foliriciuns, bureaucrats and the cansultunts: of urban problem solving. New York: Basic Books.
BREWER, G. (1983). Some costs and consequences of large wale social systems modeling. Behavioral Science, 28, 166-185. BRYK, A. (Ed.). (1983). Stakeholder-based cisco: Jossey Bass.
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San Fran-
CALIFORNIA STATE DEPARTMENT OF ALCOHOL AND DRUG PROGRAMS. (1982, February). Driving Under the Influence: California Public Opinion 1981 (No. ADP 82-4). Sacramento, CA: Field Research Corp./California State Dept. of Alcohol and Drug Problems. COOK, I., HOLDER, H.D., KENNEDY, F., & SAWYER, L. (1973). Costs for afternative public inebriate services: Atiunta, Ceorgia. Raleigh, NC: The Human Ecology Institute.
FROMM, G., HAMILTON, W., & HAMILTON, D. (1974). Feder01l.v sli~~ported mffihe~n~t~ca~ models: Survey and anaiysb. t;oi. f: Analysts and conchtsions. Boston: Data Resources, Inc. and Abt Associates. GASS, S., & SISSON, R. (Eds.). (1974). il guide to mode/s in governmenru/ planning and operations. Washington, DC: U.S. Environmental Protection Agency, Office of Research and Development. GROSSMAN, M., COATE, D., 6i ARLUCK, G.M. (1987). Price sensitivity of alcoholic beverages in the United States: youth alcohol consumption. In: H.D. Holder (Ed.), Confrol issue.c in a/coho/ ubuse prevention: Strutegies for stoles and communitre;, (pp. 169 - 198). Greenwich, CT: JAI Press, Inc. HELANDER, C. J. (1986). The Co/~~or~?;aDlil co~~fIier~~lea.sure.s.rstern: An evaiuation of system processing und dqficiencies. I/o/. 5 of An evaluation of fhe California Drunk Driving countermeasure ,sy,~tern (Rep. No. CAL-DMV-RSS-86-97). Sacramento, CA: Department of Motor Vehicles. HOLDER, H.D. (1974). ,4&ernarivr upprwxhes IO the pub/ii. inebriate problem in ntetr~~po/jt~n areus: A .rutnmur_~ of firldin~s .for Atlmtta, Georgia and Baltimore, M~r,v/and. Raleigh, NC: The Human Ecology Institute.
COOK, P.J., & TAIJCHEN, G. (1982). The effect of liquor taxes on heavy drinking. &/I Journul of Economics, 13, 379-390.
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HOI.DER, H.D., & BLOSE, J.0. (lY87). Reduction of community aicohol problems: computer simulation experiments in three count&. Journul of Studies on :4lcohol, 4X, 124- 135.
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R.E. (1977). Evaluation of policy simulation models. WashDC: Information Resources Press.
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KAIN, J. (1978). The use of computer simulation analysis. Journal of Urban Analysis, 5, 175-189.
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LEMAN, C., & NELSON, R. (1981). Ten commandments for policy economists. Journal of Policy Analysis and Management, I, 97-117. MANN, R.E., LEIGH, G., VINGILIS, E.R., & DeGENOVA, K. (1983). A critical review on the effectiveness of drinking-driving rehabilitation programmes. Accident Analysis and Prevention, 15,
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A.J.
(1986).
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WALLER, P.F. (1985). Licensing and other controls of the drinking driver. Journal of Studies on Alcohol (SuppI. No. IO), 150-160. WEISS, C. (1973). Where politics and evaluation
Analysis of DUlprocessing from arrest through post-conviction countermeasures. Vol. I. An evaluation of the California drunk driving countermeasure system. (No. PERRINE,
M.W.
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CAL-DMV-RSS-84-89). Vehicles. PHELPS, stitution.
Sacramento,
CA:
Department
C.E. (in press). Death and taxes: An opportunity
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for sub-
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A. (1966). The political economy
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Administration Review, 24, 292-310. WILLIAMS, R., HAGEN, R.E., & MCCONNELL, E.J. (1984). A survey of suspension and revocation efforts on the drinking-driving offender. Accident Analysis and Prevention, 16, 339-350.
Evaluation
0149-7189/88 $3.00 + .OO Copyright 0 1988 Pergamon Press plc
1988
COMMUNITY PLANNING AND THE PREVENTION OF ALCOHOL INVOLVED TRAFFIC PROBLEMS
An Application
of Computer Simulation
HAROLD D. HOLDER and JAMES 0.
Technology
BLOSE
Prevention Research Center, Berkeley, California
ABSTRACT This paper is an assessment of the application of computer simulation to community-based planning for the prevention of alcohol-involved traffic problems in the County of San Diego, CA. In this project, a computer model was used to assist local planners in (a) documenting the extent of problems, (b) better understanding the complex system which yields alcoholinvolved problems, and (c) projecting the potential ejfects OJ-various prevention strategies. Advantages and limitations of such technology in community planning are discussed.
INTRODUCTION education efforts to reduce drinking and driving recognize that this situation may very well influence the success of their program. There are many parts of the community system contributing to the use of alcohol in conjunction with driving which planners need to recognize. Exogenous factors such as trends in employment or disposable income, largely outside the domain of traffic safety prevention efforts, can impact alcohol use and misuse (see Cook & Tauchen, 1982; Grossman, Coate, & Arluck, 1987; Moore & Gerstein, 1981; and Ornstein & Hanssens, 1981). While planners and community leaders may intuitively appreciate that alcohol-involved traffic problems are impacted by many diverse factors, they do not generally have at their disposal the tools or technology to translate intuitive understandings into concrete relationships. This paper describes one such technology, computer simulation, and its use in a recent community planning effort. See Holder, Blose, and Ryan (1988) for a detailed evaluation of the project and documentation of the computer model utilized.
While the importance of community planning for the prevention of traffic safety problems has been acknowledged, local planning efforts are often handicapped by a lack of resources. Given the limited availability of funds to implement and operate prevention programs, it is not surprising that resources for the technological support of prevention planning are seriously constrained. It is an understatement to observe that traffic safety problems (manifested as fatalities, injuries, and property damage) are part of a complex system. The issue of alcohol involvement further complicates the picture. Not only must the factors of speed, weather, road, and vehicle design, and “normal” driver errors be considered, but so must driver impairment resulting from drinking, as well as the social and economic factors surrounding the community use of alcohol in high-risk behavior. Unfortunately, this complexity is rarely appreciated in practice. For example: in many states chilled beer (the beverage of choice among the young) is readily available at gas stations at a price comparable to soft drinks. Yet seldom do those instituting school-based
Requests for reprints should be sent to Harold D. Holder, PhD, Prevention Research Center, 2532 Durant Avenue, Berkeley, CA 94704.
267
268
HAROLD
D. HOLDER
and JAMES
0. BLOSE
PROJECT GOALS The purpose of the project described here was to assess the ~ppIication of computer simulation as a means to aid community planning to reduce alcohol-involved traffic problems. Under joint funding from the County of San Diego (California) Alcohol Program and the National Highway Traffic Safety Administration, the project was part of a national-local collaboration to develop and test new approaches and technologies to
FORMATION
aid communities to reduce alcohol-related problems in general and traffic probtems in particular. The development of a San Diego County computer model was an effort to involve the leadership of San Diego County in identifying alcohol-involved traffic problems which the community desired to address and to increase awareness of the larger community system of which drinking and driving is but a part.
OF A COMMUNITY
Because an ongoing interagency group concerned with drinking and driving did not exist in San Diego County, a Community Advisory Committee (CAC) was formed for the project. The group was designed to be diverse in membership and representative of the major interests and institutions involved with traffic safety issues. Included were representatives of law enforcement, the district attorney’s office, the judiciary, public schools, local colleges, U.S. Navy (a major Naval facility is located in the San Diego area), and the County Alcohol Program. The CAC provided community Ieaders the opportunity for direct participation in model specification and in designing potential community prevention strategies which could be evaluated through computer simulation. Using driver fatalities and injury crashes as focal points, the CAC meetings documented the historical levels and patterns of alcohol-involved traffic problems in San Diego County, and stimulated discussion and documentation of the community system which involved alcohol use in general and drinking and driving in particular. One of the tasks undertaken by CAC members was to recommend new prevention strategies (i.e., ones which were not a part of existing efforts or current law) which they thought would reduce driver fatalities and injury crashes in the long term. Eighteen such strategies were identified. Before the impact of any particular strategy could be evaluated via computer simulation, it was necessary to understand the relationship between the system changes which the strategy would bring about and traffic outcomes. Thus the existence of adequate research and evaluation data on each strategy was a prerequisite for its inclusion in the simulations. Based on the availability of data on each countermeasure, eight were selected for simulation: I. School-based Education Program: A school-based program focusing on alcohol consumption was postuiated, having the assumed objectives of teaching students about alcohol and its effects and creating appropriate attitudes toward alcohol use and
2.
3.
4.
5
7.
PLANNING
GROUP
drinking and driving (see Mann, Vingilis, Leigh, Anglin, & Blefgen, 1986; McKnight, 1986; and Schaps, Moskowitz, Malvin & Schaeffer, 1984). increased Driving-Under-the-influence (DUI) Enforcement in High Risk Areas: An enforcement program similar to the successful well-documented Stockton, California, effort was proposed. The Stockton program was a special enforcement effort with accompanying publicity which utilized special patrol forces as a supplement to regular patrols on Friday and Saturday nights between 8 p.m. and 4 a.m. The special project involved a series of enforcement waves to sustain its effect on reduced drinking and driving (see Voas & Hause, 1987). Mandatory License Suspension for Convicted DUI Offenders: This involved a six-month mandatory license suspension for all convicted DUI offenders. While judges currently have the authority to issue such sentences, this sanction is seldom used in San Diego (see Peck, Sadler, & Perrine, 1985; Sadler & Perrine, 1984; Tashima & Peck, 1986; and Williams, Hagen, & McConnelI, 1984). Mandatory Jail for Convicted DUI First Offenders: A two-day mandatory jail term for all persons, adult and juveniIe, convicted of a first DUI offense was postulated (see California Department of Drug and Alcohol Programs, 1982; Helander, 1986; National Highway Traffic Safety Administration, 1983, 1986; Ross, 198.5; and Voas, 1986). and 6. Lower the Legal BIood-AIcohol-Concentration (BAC) Limit for conviction of drinking and driving to .08 or to .05. Currently, in California if a person is arrested with a tested BAC of .lO, then they are considered an alcohol-impaired driver. In other words, the current per se level for conviction for Driving-Under-the-Influence (DUI) is .lO. These proposed interventions would require legislation to Iower the per se BAC level for conviction of DUI to .08 or to .05 (see Holder & Blose, 1983; and Waller, 1985). Increased Retail Price of AIcoholic Beverages: This intervention assumed a 25% increase in retaii price but did not specify the particular means to accom-
Community plish this (whether via a tax increase, price setting, etc.) (see Cook & Tauchen, 1982; Grossman et al., 1987; Ornstein & Hanssens, 1981; and Phelps, in press). 8. Increased Treatment for Multiple DUI Offenders: This intervention involved a doubling of the propor-
COMPUTER
MODELS
Computer simulation is a research and policy evaluation technique which has been utilized to investigate traffic problems and changes in traffic indicators as a result of system-level shifts. However, this technique has rarely been applied to reducing alcohol-involved traffic problems. Some researchers (Cook, Holder, Kennedy, & Sawyer, 1973; Holder, 1974; and Schlenger, Haywood, & Hallan, 1976) have applied computer models to various aspects of alcohol misuse and abuse but did not include drinking and driving. Summers and Harris (1978) conducted a computer simulation of the general deterrence of driving while intoxicated but this was not applied to specific communities and did not consider variations in drinking behavior. This project utilized a computer-based model which had been developed and tested initially utilizing
MODEL OVERVIEW While the full documentation of the computer model is beyond the scope of this paper (see Holder & Blose, 1983, 1987; Holder et al., 1988), the major conceptual components of the drinking and driving subsystem of the model are shown in Figure 1. This illustrates in general form the interaction between the community consumption of alcohol (quantity and frequency by gender and age) and the distribution of driving events by Blood Alcohol Concentration (BAC) by age, gender, and drinking practices. Drinking in the model is influenced by previous drinking patterns, cultural values and norms, retail price, disposable income, beverage marketing, and the minimum purchasing age. The drinking and driving distribution is influenced by community drinking patterns and the amount of driving, as well as by public pressure for enforcement and conviction, the specified BAC level for conviction for DUI, actual enforcement and conviction levels, and the perceived risk of sanction. The distribution of driving events interacting with the risk of driver fatalities and injury crashes yields annual model estimates for these outcomes. The model structure is based upon research concerning the various system components as well as on national data. Local data were used to load and run the San Diego County model. A summary of both the model structure and the local data sources is provided in Holder et al. (1988). Before the model was used to project future trends,
Planning
269
tion of repeat DUI offenders who are referred to multiple offender rehabilitation programs (see Helander, 1986; Mann, Leigh, Vingilis, & DeGenova, 1983; Peck et al., 1985; Perrine, 1984; and Waller, 1983).
AND SIMULATION national level data and subsequently field-tested in three U.S. counties: Wake County, NC; Washington County, VT; and Alameda County, CA. This prototype model, which simulates the diverse variables comprising the system of drinking patterns and drinking/ driving behavior in a community, had been used to assess the likely impact of various intervention strategies on alcohol-involved traffic problems but was not previously undertaken with the direct participation of any of the three communities in its design or application (Holder & Blose, 1983, 1987). A unique feature of the project described here was the joint participation of CAC members and project staff in loading the model with San Diego County data and in specifying the alcohol countermeasures and prevention strategies to be evaluated.
AND VALIDATION a historical validation was carried out to verify that it could successfully recreate past trends. A 17-year period (1970 to 1986) was used to validate both the alcohol consumption subsystem (the overall use of alcohol in the community) as well as the drinking and driving subsystem. Validation is a test of the understanding of the overall system as expressed by the relationships among variables in the model. The historical data used for benchmark validation were not loaded directly into the model. Figure 2 illustrates that the model estimates of driver fatalities for the 1970-1986 period provided a relatively good fit to the actual data. Similar results were obtained for other measures. For a more detailed discussion of model validation see Holder et al. (1988). As shown in Figure 2, the model projected both driver fatalities and injury crashes into the future (1987-1994) to establish a benchmark for comparison with projected changes from alternative alcohol countermeasures. The benchmark projections were based on the following assumptions: (a) no new traffic safety prevention efforts are instituted during the 1987-1994 period; (b) projections of County population and personal income (1987-1994) developed by the San Diego Association of Governments (SANDAG) were used; (c) changes in the retail price of alcoholic beverages and alcohol marketing trends will be similar to trends during the previous 10 years; and (d) per capita vehicle miles traveled will remain roughly constant.
HAROLD
D.
HOLDER
and JAMES
0. BLOSE
POPlJlAnON CONSUMPnON DlSTRl6lJTlON
DlSTRlSUTlON Of DRIVING E-VENTS BY SAC FOR INDIVIDUALS OF DIFFERING AGE. SW. CONSUMPnON
Figure
1. Drinking
MODEL
and driving
sector: conceptual components.
SIMULATION
For purposes of the simulations it was assumed that each countermeasure could be successfully implemented and that each would result in the system changes that the research literature suggests. The project did not assess if the strategy was politically or economically feasible. For example, in the case of the price increase strategy, we assumed that a price increase of specified magnitude was carried out and predicted declines in consumption occurred as a result. We did not specify the specific operational form of the price increase nor assess its political feasibility. Each prevention effort was operationalized in the model by modifying the key system factors which each
RESULTS
program could be expected to effect based on published research and evaluation studies. The internal dynamics of the model, as a representation of the actual system, then determined the eventual impact of the program on traffic safety outcomes. Figure 3 provides a general illustration of how a program which alters some component of the system may cause a change in community consumption of alcohol and/or drinking and driving behavior, ultimately affecting traffic outcomes. Each strategy was implemented in the model in 1987. The simulated impact of each strategy on driver fatalities and injury crashes was then compared with the “business-as-usual” projections described earlier. Fol-
Community
271
Planning
Q. Actual .I- Computer Projsctionr
0-l
I
; : : : : : : : : : : : : : : : : : : : : : : IYsar
1970 1972
1974
1976
1970
Figure 2. Driver fatalities.
Program B
Program A
I
1980
1982
1984
Actual and computer
Program
C
Specific Changes in System FaCtor
J \
Community Consumption of Alcohol _
Or inking and Driving &bvior
Traffic Problems Figure 3. Illustration termeasures.
of system changes suggested by alcohol coun-
lowing is a brief discussion of the simulation results, grouping the interventions by general approach: (a) strategies targeting convicted DUI offenders, (b) strategies focused on enforcement, and (c) strategies focused on the community as a whole. Plots of computer simulation results, comparing “business-as-usual” and alternative prevention strategies, are shown in Figures
1986
1988
projections,
1990
1992
1994
San Diego County,
CA - 1970 to 1994.
4 through 6 for driver fatalities. The projected in injury crashes, although somewhat smaller, ilar in pattern.
changes are sim-
Enforcement Strategies Of this group of strategies, the high-risk area enforcement campaign had the greatest short-term impact (driver fatalities dropped by 15% during the first year and injury crashes by 6.4%). By the end of the simulation period much of this effect had dissipated. Lowering the legal BAC limit produced a smaller initial reduction which increased slightly over the period of the simulation. A reduction in the legal limit to .05 BAC yielded a short-term projected reduction of 5.4% and a long-term reduction of 7.6% in driver fatalities. DUI Offender Strategies Mandatory license suspension had a substantial immediate impact followed by a decay in effect. While short-term effects (9.3% for driver fatalities and 4.2% for injury crashes) were somewhat lower than for the targeted enforcement campaign, the long-term impact was comparable. The effect of mandatory jail for first offenders followed a similar trend, although the impact was much more modest. Increased treatment of multiple offenders had virtually no impact on either outcome measure. Community Strategies The price increase strategy yielded slight reductions in driver fatalities and injury crashes within one year, but by 1994 the projected impact amounted to a 12.7% reduction in driver fatalities and a 4.6% decline in injury crashes. The school-based educational effort
HAROLD
212
D. HOLDER
and JAMES
0. BLOSE
Deaths 350 340
320-310-300 ^_ 290280 --
Usual(Basaline) + Mandated Jail
Figure 4. Driver fatalities.
Business as usual and IXJI offender
strategies,
San Diego County,
CA - 1985 to 1994.
330
310--
290
270--
230 6,: 1965
i Year 1966
1987
Figure 5. Driver fatalities.
1988
1969
1990
1991
Business as usual and enforcement
produced a 2.5% short-term reduction among young driver fatalities and only a slight long-term impact. Because the outcome measures used in these simulutions include all driver fatalities and injury crashes, reductions in alcoh~~l-in~~~ived driver fatalities and injury crashes would be expected to be much greater DOClJMENTATION
1992
strategies,
1994
San Diego County,
CA-
1985 to 1994.
than those just described.
The simulation results presented above-indeed the results of any simulationare only as accurate as the data and research findings used to build the model and the assumptions made about future exogenous factors.
OF CHANGES
One of the central goals of this project MQSto increase awareness among community leaders of the nature and potential impact of various co~lntermeas~lres for rcdui-
1993
IN BELIEFS
ing drinking and driving problems, and especially to increase their awareness of system-level interventions. in order to document these changes we adrt~inistered a
Community
273
Planning
i
.Q School-based Education
( -+Price 1
230 -i
1985
1986
1987
Figure 6. Driver fatalities.
I
Increase j
I Year 1988
1989
1990
1991
Business as usual and community
set of surveys of perceptions, values, and beliefs regarding drinking and driving and the use of alcohol more generally. The instrument has been used in other efforts in California to document community leader opinions about alcohol prevention policy. Results are shown in Table 1. The preliminary wave of surveys was given at the beginning of the first CAC meeting (January 1987) prior to the initiation of any discussion or training. The second wave was administered at the end of the final meeting after the CAC had reviewed and discussed the results of the computer simulations (September 1987). Respondents were asked to indicate intensity of
1992
strategies,
1994
San Diego County,
CA-
1985 to 1994.
agreement or disagreement on a 1 to 5 scale-the lower the score the more agreement with the statement. The results suggest that after working with the computer model, CAC members had both an increased understanding of systemic factors impacting drinking and driving, as well as a greater appreciation of the potential of certain countermeasures. For example, Table f shows that respondents were more likely to support increased alcohol prices and limits on alcohol availability. Respondents were also more likely to favor use of license suspension and less likely to favor mandatory jail terms or school-based education than they had been earlier.
OBSERVATIONS ON SIMULATION THE COMMUNITY PLANNING The Community Advisory Committee was constituted specifically for this project. GAC members did not have any previous forum for sitting together around a common concern for alcohol-involved traffic issues and for identifying the various approaches and contributions which each could make to community-oriented prevention. The project provided a process in which the participants could learn from each other and could integrate ideas about the community as a system. The computer model-its operationalization with local data, its validation, and its use to simulate a series of potential countermeasures-provided a focal point for this learning. Participation in the CRC process allowed the diverse concerns and interests of the participants to be discussed and a concrete product, a series of computer evaluations of potential countermeasures, resulted.
1993
MODELING PROCESS
AND
While the CAC functioned as a mechanism for information sharing and dissemination, a critical first step in community planning, an ongoing community planning process would involve much more than this. It would be unrealistic to expect the CAC process to result in concrete planning steps or programmatic decisions during the nine month time frame of the project -and indeed the CAC had no such mandate. One basic question to be addressed at the outset was whether a computer simulation would be accepted as a tool in a community context. In the present case the answer is clearly yes. In evaluating the implications of this conclusion we recognize that San Diego County officials appeared well-informed about alcohol problems. Members of the CAC recognized that their organizations and agencies currently deal with only a small part of the picture and that there were factors
274
HAROLD
D. HOLDER
and JAMES
0. BLOSE
TABLE 1 COMMUNITY Some
of the following
strategies
the level of your agreement
a)
Require
labels
LEADER
have been
suggested
or disagreement
on all alcoholic
SURVEY:
for reducing
containers
OF RESULTS
the amount
position statement
with each
beverage
SUMMARY
(PRE AND POST]
and severity
of alcohol-related
problems.
Please
indicate
below.
to warn the user of potenital
long and short
term health
Pre
Post
Mean * --
Mean *
1.83
1.76
1.67
7 -41
1.67
1.65
1.94
1 35
1.89
2 06
nsks
bl
Increase
police
cl
Restrtct
alcohol
sales
In locations
d)
Increase
excise
taxes
on all alcoholic
e)
Impose
mandatory
f)
Increase
funding
9)
Permit
h)
Increase
funding
includrng
sales
i)
Requtre
k)
and/or
to state
Require
broadcasters
the health
(e.g. sports
stadrum,
outdoor
concerts)
stops
Beverage
for all persons
programs
to test for cinving
Control
convicted
of driving
under
the influence
tn the schools
agencies
under
to improve
the influence enforcement
of regulations
1.61
1 88
2 28
1 88
2.1 1
2 18
2.33
2 00
2.17
1.94
2.33
2.00
2.33
1 88
2.39
1 65
laws
who then
the advertising
to follow
beverages
random
Alcoholtc
and driving
is likely
of at least 46 hours
that bars and restaurants
Prohibit
of drinking driving
and drug education
acceptable
to minors
patrols where
jail sentences for alcohol
constitutionally
age customer
ji
surveillance
share
harms another of alcoholic
to provtde
financial
responsibility
if they
serve an obviously
intoxrcated
or under
person
beverages
time equal
on radio and television
to that devoted
to alcohol
advertising
for messages
regarding
risks of alcohol
1)
Requtre
Immediate
ml
Prohibit
the sale of alcoholic
adminrstratrve
revocation
beverages
of drivers
license
for drivrng
at gas stations
under
the influence
N= *Based
on scale
of “1 ” (Strongly
Agree)
to “5”
(Strongly
Strengths
introducing
N=
17
Disagree)
outside their direct control which affect the extent of drinking and driving. A systems approach to alcoholinvolved traffic problems thus did not appear to pose great difficulty for many Committee members. We should also point out that San Diego is the home base of the America’s Cup and the winning sail boat skippered last year by Dennis Conners. This event received extensive publicity and the community was quite aware that computer models of hull design were utilized during the developmental phase of the sail boat project. As a result, simulation technology was already viewed as capable of producing viabte results. It is possible that efforts to utilize and gain acceptance of computer modeling by community leaders in other locations may have more difficulty. Our experience in this project indicated a number of specific strengths and limitations of applying computer modeling to commLlnity ptanning for the prevention of alcohol-involved traffic problems.
1. A computer
18
model can be an effective means of local data-as well as knowledge from
research and evaluation studies-into a planning process. While there are certainly other methods of accomplishing this, the model is a data-based tool that does not require users to absorb and analyze the data themselves. Rather they evaluate the results of simulation projections which use data and research findings as input, awareness of the 2. The model increases community system surrounding drinking and driving behavior. The model can assist community leaders and planners to recognize and understand relationships between diverse issues and factors which operate in the real world. Many of these factors and relationships are largely unrelated to their specific professional responsibilities but can affect the results of their efforts to reduce problems. The total system is composed of many parts; each part is often the concern of separate go~ernnleIlt agencies which may interact only minimally. Thus no agency can see the whole system. 3. The computer model facilitates the comparison of alternative programs, stimulating planning group discussions about the relative long-term merits of diverse strategies and providing a more en~piricalIy
Community grounded basis for deliberations than might otherwise occur. 4. A computer model is a versatile, multi-purpose tool. In addition to simulating prevention strategies, it can be used to assess differing assumptions about the likely course of future events (e.g., trends in population growth or the economy) and can be modified to reflect differing assumptions about the nature of the drinking and driving system itself. Limitations 1. Some data collection is required-and thus some level of community commitment and resources. Most communities are unlikely to have all the necessary data readily available. Few longitudinal studies of some key system variables existed in San Diego. For example, perceived risk of detection could only be inferred from a newspaper content analysis. This situation is quite likely to exist in other localities as well.
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2. At least in its present form, the model is not a tool that a community can use on its own. Assistance from technical experts is required to use it effectively. 3. Although the model incorporates the best available knowledge about countermeasures, that knowledge is incomplete and the accuracy of model predictions of the potential impact of strategies is variable. Estimates for some interventions are likely to be more on the mark than others because we know more about some strategies. 4. It is important that participants in the planning process understand the limitations of the model (or any other planning aid or technology) if it is to.be appropriately used. Assuring such understanding is not an easy task. Many people may be likely to place undue confidence in anything involving computers. If participants do not understand the limitations and end up making decisions expecting all model predictions to come true, they may be soured on future uses of modeling or planning in general.
IMPLICATIONS Over the past two decades, computer modeling has been widely applied to governmental planning and operations, especially at the Federal level (see Fromm, Hamilton, & Hamilton, 1974; Gass & Sisson, 1974; Pugh, 1977; and U.S. GAO, 1982). The usefulness of many of these models has been questioned by some (see Ascher, 1978; Brewer, 1973, 1983; and Fromm et al., 1974). Among the issues raised are the concern that the utility of models may sometimes be oversold by their designers; that due to inappropriate design, delays in development or other reasons many models are in fact never used in practice; that modelers are too often insensitive to the political and organizational context within which their work will be used and insufficiently aware of differences in perspectives between researchers and model designers on the one hand and policymakers and program designers on the other. These concerns are strikingly similar to those raised by evaluators and policy analysts regarding their own work (for example, Behn, 1981; Bryk, 1983; Etzioni, 1985; Leman & Nelson, 1981; Patton, 1978; Weiss, 1973; and Wildavsky, 1966). Indeed as Kain (1978) has observed, the real issue is not whether criticisms of some computer modeling applications are justified, but how simulation compares to other quantitative tools used in policy planning-a matter which has yet to be carefully examined. In assessing the utility of the effort reported here, it is important to recognize that this project differed in several critical ways from the vast majority of computer modeling applications to policy and planning. First, an existing model structure was used for the proj-
ect, with only modest modifications (Holder, Blase, & Ryan, 1988). One criticism of the application of computer modeling to urban land use planning in the 1970’s was that planners in hundreds of local jurisdictions were trying to deal with the same conceptual and methodological issues, yet operated largely independently (Kain, 1978). The approach used here has the virtue of not having to start from scratch for each local application. Of course applying a model originally developed as a research tool to a community context raises other issues. Such a model may be more complex and present more data collection requirements than are actually needed for effective community planning applications. Only further testing of the larger model-and its further application to community planning-can resolve this issue. A second difference between the use of the model reported here and most other policy applications is the role of the model itself in the planning process. In most applications, models are used to evaluate alternative policies or provide forecasting estimates in order to make specific action recommendations to policymakers or planners. This was not the role of the model in this project. Rather, as discussed earlier, the model was used as a tool to assist participants in understanding the nature of the complex factors involved in drinking and driving in a community. Thus the role of CAC members in applying the model was as important as the results of the simulations themselves. Further, the simulation results were used not to provide specific action recommendations but to increase participants’ under-
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standing of specific issues. For example: the simulations showed that interventions targeted only at convicted offenders-whatever their other merits -are likely to have a limited long-term impact on community outcomes, probably because most drinking drivers are never apprehended. The complexity of a sophisticated computer-based model creates a considerable gap between model designers and community participants (see Brewer, 1983; and Pugh, 1977). As an example of the broader dilemma posed by the reliance on technical expertise to solve problems in our society (Brewer, 1983) this raises a number of important issues. To what extent do community policymakers need to grasp the complexity of the model’s design? To what extent do they need to be able to evaluate all the assumptions and necessary interpolations from research and data bases outside their community to use the model appropriately? Of course some decisions must be made by model developers. The model will inevitably bear the stamp of their values (Brewer, 1983), thus providing some limits to community involvement. At some point, however, we create an undesired dependency upon the creativity, interpretation, and ideas of model-builders. Certainly communication is one critical issue here (Fromm, et al., 1974; Pugh, 1977). Yet if community leaders or plan-
and JAMES 0. BLOSE ners are not able (or are unwilIing) to undertake the difficult thinking necessary to improve their understanding of their own community system, then the community selection of prevention interventions will have limited long-term effectiveness. We believe that the long-term reduction of alcoholinvolved traffic problems requires that community prevention planners employ some of the same conceptual work and longitudinal research required for a useful computer model, even if computer modeling technology itself is not used. Such an approach must include not only the examination of individual driver characteristics and patterns of alcohol-inlpaired driving, but also a consideration of the larger social and economic context in which community drinking and driving is imbedded. Contrary to the implicit assumption in much traffic safety countermeasure research, the use of alcohol within the total ~onlmunity-including its retail price, availability, and community values about acceptable and unacceptable drinking-cannot be ignored. In the end, both researchers and community planners will have to extend their thinking about effective countermeasures beyond those factors which have traditionally been considered. Until such thinking is a regular part of our efforts to reduce alcohol-involved traffic problems, we will likely repeat many of the errors of the past.
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