South Carolina Strategic Prevention Framework State Incentive Grant: Final Evaluation Report

May 2015

Al Stein-Seroussi Steve Shamblen Bob Flewelling

Sean Hanley Elaine Dowdy Melvin

Supported by a grant from the US Department of Health and Human Services, Substance Abuse and Mental Health Services Administration (award number SP015586-01), to the SC Department of Alcohol and Other Drug Addiction Services (DAODAS).

Chapel Hill Center 1516 E. Franklin Street, Suite 200 Chapel Hill, NC 27514 Tel: 919-265-2610 www.pire.org

TABLE OF CONTENTS Glosssry ........................................................................................................................................... 1

Contents of this Report ................................................................................................................... 3

Project Background ......................................................................................................................... 4

State-Level Implementation and Capacity Enhancement .............................................................. 7

Community-Level Implementation and Capacity Enhancement .................................................. 12

Community-Level Outcomes ........................................................................................................ 15

Overview ................................................................................................................................... 15

SPF SIG Effects on Underage Drinking ...................................................................................... 16

SPF SIG Effects on DUI Crashes ................................................................................................. 24

Capacity ..................................................................................................................................... 30

Summary of Community-level Outcomes Analysis Findings ..................................................... 32

Appendix A. Student Survey Sample Sizes by Community, School, and Year .............................. 36

Appendix B. Underage Drinking Rates by Community ................................................................. 37

Appendix C. Alcohol-Related Motor Vehicle Crashes, by CAST County ....................................... 38

Appendix D. Alcohol-Related Motor Vehicle Crash Deaths, by CAST County .............................. 39

Appendix E. Single Vehicle Nighttime Crashes, by CAST County .................................................. 40

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GLOSSARY

CAST: Community Action for a Safer Tomorrow; a five year Cooperative Agreement from the Substance Abuse and Mental Health Services Administration’s (SAMHSA) Center for Substance Abuse Prevention (CSAP) awarded to the South Carolina (SC) Office of the Governor through the SC Department of Alcohol and Other Drug Abuse Services (DAODAS). The official term SAMHSA uses to describe this agreement is “Strategic Prevention Framework State Incentive Grant” (SPF SIG). CAST is the name SC has decided to use to identify its SPF SIG.

CAST priority issues: Underage drinking and DUI traffic crashes.

Center for Substance Abuse Prevention (CSAP): part of SAMHSA, the federal agency that is funding and overseeing the South Carolina Strategic Prevention Framework State Incentive Grant (SPF SIG), called CAST; provides national leadership in the Federal effort to prevent alcohol, tobacco, and other drug problems.

Contributing local factors: specific aspects of risk factors and underlying conditions that contribute to substance-related consequences and consumption in a particular local area.

Cultural competency: a cross-cutting component of the Strategic Prevention Framework (SPF); refers to being inclusive in every step of the SPF of state and community-level key leaders and stakeholders as well as target population input, and being able to work effectively in cross-cultural situations.

DUI crashes: traffic crashes in which at least one driver was driving under the influence of alcohol and/or drugs.

Environmental scans: a technique often employed in a planning process; a way to assess a community regarding a specific issue by answering questions about the local area.

Focus groups: semi-structured interview conducted with a small group of people; used to gather qualitative information from your community about issues and attitudes; typically led by a facilitator who presents a small number of targeted questions and facilitates the discussion.

Hot spots: areas in the county where the problem behavior is concentrated, though a hot spot could refer to demographic groups chosen as a target population. For example, a county site could choose to specifically target adolescent males with their evidence-based strategies.

Key informant interviews: one-on-one interviews; provide you with the perspectives of people who observe and monitor community functioning.

Needs assessment: a process that helps you prioritize substance abuse problems, ground planning in actual needs and resources, and identify gaps for implementing solutions to address the needs.

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Outcomes-based prevention: an approach to prevention that focuses on reducing the negative consequences of substance abuse by using data to identify consequences, consumption patterns, and causal factors associated with substance abuse. PIRE: Pacific Institute for Research and Evaluation, the organization contracted by DAODAS to conduct the CAST evaluation. Population-level change: focuses on change for entire populations. By entire populations, we mean collections of individuals who have one or more personal or environmental characteristics in common. Risk factors and underlying conditions: the broad factors that cause or contribute to substance-related consequences and consumption in communities. SC CTC Survey: The South Carolina Communities that Care Survey. SC DAODAS: South Carolina Department of Alcohol and Other Drug Abuse Services SPF SIG: Strategic Prevention Framework State Incentive Grant. State Epidemiological Outcomes Workgroup (SEOW): a workgroup of the Governor’s Council on Substance Abuse Prevention and Treatment. Strategic Prevention Framework (SPF): The SPF is a five step process that requires states and communities to systematically: 1) Assess their prevention needs based on epidemiological data, 2) Build their prevention capacity, 3) Develop a strategic plan, 4) Implement effective community prevention programs, policies and practices, and 5) Evaluate their efforts for outcomes. Two cross-cutting components of the SPF are cultural competency and sustainability. Surveys: a collection of questions that are asked of many people in the same manner, and each one of those questions usually has a fixed set of possible responses from which to choose; allows you to collect specific information on individual attitudes, beliefs, and behaviors; can be administered by mail, face-to-face, over the telephone, or via the web. Sustainability: a cross-cutting component of the SPF; refers to integrating our efforts into ongoing operations. US Substance Abuse and Mental Health Services Administration (SAMHSA): federal agency that is funding and overseeing the South Carolina Strategic Prevention Framework State Incentive Grant (SPF SIG), called CAST.

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CONTENTS OF THIS REPORT This report was developed by the Chapel Hill Center of the Pacific Institute for Research and Evaluation (PIRE) as the final assessment of the South Carolina Strategic Prevention Framework State Incentive Grant (SPF SIG). It provides a summary of major project-related activities at the state and county-levels for the period July, 2009 through June 30, 2014. The report also provides outcome data on underage drinking and DUI crashes and factors that contributed to those issues in each of the CAST sites that was funded through this effort. The evaluation was designed to address the following four questions, at both state and county levels. The questions and their associated data collection methods are included in Table 1.

Table 1. Evaluation Questions and Methods/Data Sources

Questions Methods/Data Sources

1. How has South Carolina implemented CAST?

• Meeting observation/participation • Key stakeholder interview

2. Has substance abuse prevention capacity increased as a result of CAST?

• Key stakeholder interviews • State staff and regional coaches’ ratings

of coalition capacity

3. Have underage drinking and DUI crashes been reduced as a result of CAST?

• Communities that Care (CTC) student survey

• Crash data from SC Office of Highway Safety

4. Have associated contributing local factors been reduced as a result of CAST?

• Communities that Care (CTC) student survey

• Varied data collection from local sources (e.g., interviews, focus groups, and compliance checks)

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PROJECT BACKGROUND In July 2009, South Carolina received a five-year Strategic Prevention Framework State Incentive Grant (SPF SIG) from the Center for Substance Abuse Prevention (CSAP) of the Substance Abuse and Mental Health Services Administration (SAMHSA). The South Carolina Department of Alcohol and Other Drug Abuse Services (DAODAS) administered the SPF SIG project, known in the state as the Community Action for a Safer Tomorrow (CAST) project. The project value was $10.6M over the five-year period, with SAMHSA requiring that 85% of those funds be directed to communities to meet the following goals: (1) prevent the onset and reduce the progression of substance abuse, including childhood and underage drinking; (2) reduce substance abuse-related problems in communities; and (3) build prevention capacity and infrastructure. The Strategic Prevention Framework (SPF), depicted below in Figure 1, is a five-step prevention planning model that promotes data-driven decision-making, with an emphasis on epidemiological data using a population-based perspective. The SPF also emphasizes the importance of infusing two overarching themes, cultural competence and sustainability, throughout the five steps. The five steps of the SPF are as follows:

1. Profile population needs, resources, and readiness to address needs and gaps; 2. Mobilize and/or build capacity to address needs; 3. Develop a comprehensive strategic plan; 4. Implement evidence-based prevention programs, policies, and practices; and 5. Monitor, evaluate, sustain, and improve or replace those that fail.

Figure 1: Strategic Prevention Framework

Each SPF SIG state is expected to complete the five step process, with state implementation (Step 4) aimed towards funding communities to do the same. Thus, the SPF is intended to be used by states and communities to establish and address their prevention priorities, and CSAP requires states to direct 85% of the SPF SIG funds to communities to carry out the five steps. The underlying assumption of the SPF SIG is that faithful implementation of the framework will

Sustainability & Cultural Competence

Profile population needs, resources, and readiness to

address needs and gaps

Monitor, evaluate, sustain, and improve or replace those that

fail

Implement evidence-based prevention

programs and activities

Develop a Comprehensive Strategic Plan

Mobilize and/or build capacity to address

needs

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build states’ and communities’ substance abuse prevention capacity and that this increased capacity will result in reductions in substance-related consumption and associated problems. There are several state-level groups that provided guidance and direction for the CAST project. The SPF SIG initiative required each state grantee to have an advisory council to guide and oversee the project. In South Carolina, the Governor’s Council on Substance Abuse Prevention and Treatment oversaw the CAST project, as well as other major initiatives. The SPF SIG initiative also required each state to have a State Epidemiological Outcomes Workgroup (SEOW). The SEOW examines alcohol-, tobacco-, and other drug-related data in order to determine the scope and extent of substance abuse and its related problems within the state and to facilitate data-driven decision-making. An additional group that informed and guided the work of SPF SIG grantees was the Workgroup on Evidence-Based Practice (WEB). The purpose of the WEB is to foster collaboration across state agencies to provide guidance to the state and communities on selecting and implementing evidence-based prevention strategies. These groups were mirrored to a large extent at the county level, with CAST sites having a CAST coalition and, in most cases, strategic planning and needs assessment or data subcommittees that guided and conducted their work. Prior to South Carolina’s receipt of the SPF SIG award, the work of the SEOW identified underage drinking and traffic crashes related to driving under the influence of alcohol (DUI crashes) as top priorities in the state. As a result, these were the two priorities for CAST, with 13 counties funded from 2010 to 2014 to address these priorities (Cohort 1 counties). Seven counties addressed underage drinking and eight counties addressed DUI crashes, with two of these counties addressing both priorities. An additional five counties were funded from 2013 to 2014 (Cohort 2 counties), with two addressing underage drinking and three addressing DUI crashes. (See Figure 2 for a map of the CAST counties.) The CAST project also included four regional capacity coaches who provided technical assistance, regional training, and system building to the CAST sites to support them to effectively implement the SPF.

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Figure 2. Map of CAST Counties

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STATE-LEVEL IMPLEMENTATION AND CAPACITY ENHANCEMENT Question 1: How has SC implemented CAST? Question 2: Has substance abuse prevention capacity increased as a result of CAST? To address Questions 1 and 2 at the state level, this section provides a chronological summary of state-level CAST implementation and capacity enhancement efforts that took place during the grant period. As already noted, DAODAS was awarded the SPF SIG in 2009. The Governor’s Council on Substance Abuse Prevention and Treatment was designated to oversee CAST, as well as provide guidance on a variety of substance abuse issues. Its mission was to create a highly effective statewide comprehensive substance abuse prevention, intervention and treatment resource system that significantly reduces alcohol, tobacco and other drug use among South Carolinians through the development and implementation of a comprehensive statewide strategy at the state and community levels. Members of the Council were representatives from state agencies and community organizations committed to this purpose. Council members were a part of a process to develop multi-agency, ‘silo-busting’ strategic planning to unite prevention systems across the state and set a measureable course for state and local efforts. The Council met monthly during the first six months of implementation and quarterly thereafter. A Kick-Off meeting of the Council was held January 26, 2010 that focused on revisions of By-Laws, introduction of SPF grant and presentation from CSAP project officer. An Executive Steering Committee of the Governor’s Council met prior to the Governor’s Council meetings to discuss issues to be presented to the Council and prepare agendas for the Council meetings. Council Members were given the opportunity to participate in workgroups such as the SC Meth Action Group, the State Epidemiological Outcomes Workgroup, the Underage Drinking Action Group, the Workgroup of Evidence-Based Practices, and the Fetal Alcohol Spectrum Disorder Workgroup. A description of the workgroups follows:

• SC Meth Action Group – A consortium of state and federal agency representatives who coordinated resources and staff to provide a statewide conference yearly since 2004.

• Workgroup of Evidence- Based Practice – Representatives from state agencies that focused on evidence-based programs (EBPs) and environmental strategies. The group’s major goals included: compile literature on EBPs for CAST; provide training on EBPs for CAST; provide recommendations for the SC Toolkit; and provide recommendations to Governor’s Council about the county strategic plans,

• State Epidemiological Outcomes Workgroup – This workgroup was formed in 2006 and comprised of a diverse membership of evaluators and statisticians from state government and higher education institutions. The group generated the SC Profile on Alcohol, Tobacco and Other Substance-Related Indicators, which served as the primary resource for making data-driven decisions for county alcohol and drug abuse agencies and stakeholders. The SEOW examined alcohol, tobacco, and other drug related archival data to determine the scope and extent of substance abuse and its related problems within the state.

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• Underage Drinking Action Group – The UDAG addressed underage drinking issues, promoted interagency collaboration of underage drinking and related prevention efforts, and stayed abreast of pertinent policy issues in the legislature and elsewhere.

In 2009, after reviewing available data, the SEOW recommended four state priorities—underage alcohol use, alcohol related car crashes, substance abuse pregnancy, and youth tobacco use—to the Governor’s Council that were later adopted for the state prevention system. The SEOW then led all counties through a seven-month process of completing county epidemiological profiles, with the majority of profiles pointing to underage drinking or alcohol-related car crashes as the highest level priorities. CAST submitted a Strategic Plan to CSAP in January of 2010 which was approved, with minor modifications, in June of 2010. This plan outlined how the CAST Team intended to implement the SPF SIG at the state and community levels. In addition, DAODAS created and submitted a logic model for underage drinking and DUI crashes that focused on three areas: consumption patterns, risk/protective factors and underlying conditions, and environmental strategies to address those underlying conditions. This model later served as a sample for counties to create their own logic models. These logic models are displayed in Figure 3 on the next page.

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Figure 3. CAST Logic Models for Underage Drinking and DUI Crashes

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Four Regional Capacity Coaches were hired early April 2010 to provide training and technical assistance to CAST sites and non-funded CAST sites in their assigned region of the state. They worked to ensure the readiness of county ATOD authorities and coalitions to implement approved strategies, programs, and polices in their local communities. The Coaches’ role was that of coach, teacher, trainer, and leader to help guide sustainable county or community level change. These coaches had previous work experience in the SC prevention system. DAODAS created a list of basic trainings that coaches would be exposed to during the initial months of employment. This training list consisted of the following: prevention platform, Substance Abuse Prevention Specialist Training materials, system overview, SPF and CAST overview, grant/job expectations, state data overview, Enforcement of Underage Drinking Law/Alcohol Enforcement Teams (EUDL/AETs) overview, federal and state reporting tools, cultural competency, coalition development, and how to assess counties. Following the initial coaches training in April, DAODAS held a series of technical assistance calls for three months. DAODAS then hosted several trainings facilitated by the Southeast Center for the Application of Prevention Technology (CAPT) and Community Anti-Drug Coalitions of America (CADCA). The purpose of these trainings was to support coalitions in all aspects of their community-based prevention work, including community mobilization, coalition readiness, evidence-based prevention strategies (including environmental approaches), cultural competence, and sustainability. DAODAS also hosted a Regional Capacity Coaches Host Agencies Meeting, June 8, 2010. This meeting consisted of local alcohol and drug abuse directors and financial officers where regional capacity coaches were housed and supervised. The agenda covered CAST structure, roles and functions of the regional capacity coach, and benefits and challenges. On July 7, 2010, a special session of the Governor’s Council was convened to hear the SEOW’s rankings and recommendations of county alcohol and drug abuse agencies based on greatest need in the two priorities–underage drinking and DUI crashes. Nine counties were recommended for funding to address DUI crashes, eight counties were recommended for funding to address underage drinking, and three were counties were recommended to address both priorities. The Council approved the recommendations and the timeline to move forward with next steps of CAST. On July 12, 2010, DAOAS sent the recommended counties an award application acceptance package. Counties that were interested in accepting funding were asked to return the completed acceptance package to DAODAS by August 2, 2010. Funding was based on a four-tier approach (ranging from $90,000 to $265,000); if a county declined an award or had to be terminated for non-compliance, the next highest ranked county in the same tier received the award. In August of 2010, DAODAS issued contracts to fund six counties for DUI crashes, five for underage drinking, and three for both priorities. One of the counties that targeted both priorities was defunded after the first year of local funding, leaving a total of 13 CAST sites. Although the counties themselves were chosen for funding based on their need, and the funding was administered through county alcohol and drug abuse agencies, each county was required to channel funds to a local coalition of prevention stakeholders to carry out the five-step SPF process within the counties.

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In addition to the initial set of communities funded in 2010, DAODAS funded five communities in 2013 with SPF SIG funds that had been previously unspent. Those five communities were considered to be the most high need and high capacity among the initial applicant pool. Thus, a total of 18 communities received CAST funds: 13 Cohort 1 communities (five addressed underage drinking only, six addressed DUI crashes only, and two addressed both priorities) and 5 Cohort 2 communities (two addressed underage drinking and three addressed DUI crashes).

Answers to the Questions: How has South Carolina implemented CAST and has substance abuse prevention capacity

increased as a result of CAST

• At the state level, South Carolina implemented the SPF SIG with faithful consideration of the 5-step SPF model, engaging in a thorough process to accomplish each step and to meet its federal grant requirements. Activities in support of the SPF included conducting a statewide needs assessment, creating an epidemiological profile, generating a statewide strategic plan, establishing a Workgroup of Evidence-Based Practices and an Underage Drinking Action Group, and developing Regional Capacity Coaches.

• South Carolina increased state-level capacity by conducting substantial training and technical

assistance related to the five SPF steps for state-level stakeholders, particularly among DAODAS staff.

Key stakeholders reported gains at the state level in the areas of assessing and prioritizing state needs, strategic (i.e., data-driven) planning, and focusing on outcomes.

“This project has helped the state make great strides in planning and implementation.”

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COMMUNITY-LEVEL IMPLEMENTATION AND CAPACITY ENHANCEMENT Question 1: How has SC implemented CAST? Question 2: Has substance abuse prevention capacity increased as a result of CAST? To address Questions 1 and 2 at the community level, this section provides a chronological summary of community-level CAST implementation and capacity enhancement efforts that took place during the grant period. On August 9, 2010, DAODAS held its kick-off meeting for CAST sites. This one-day meeting covered the following topics: CAST overview; staff roles; FY2011 expectations; mandatory trainings; financial fundamentals; and preview of CAST needs assessment, processes, and technical assistance. Following this meeting, CAST sites began to advertise and hire CAST Coordinators. Based on feedback from the Regional Capacity Coaches, a CAST Talking Points document was developed by DOADAS and shared with coordinators, coalition members, and other interested parties to address questions and concerns about CAST. CAST sites were given a CAST Deliverables and Special Events document at the beginning of each fiscal year starting in FY 2010. These deliverables highlighted the year’s expected deliverables, due dates, persons responsible, and deliverable recipients. CAST sites were also instructed to share drafts of particular deliverables (e.g., strategic plan, sustainability plan, and evaluation plan) with their Regional Capacity Coach two weeks in advance of the due date to allow additional feedback prior to submission. CAST members were required to attend Prevention Quarterly Meetings, which are gatherings of South Carolina’s county and state prevention professionals. During the SPF SIG, the quarterly meeting was composed of two major blocks of time. The morning session was devoted to general information sharing and updates of state and national prevention issues. The afternoon session was set aside for the ‘Prevention Academy’ which was facilitated by DAODAS CAST staff and was designed to foster discussion among CAST coordinators and coalition members. In October of 2010, CAST sites embarked upon the first of the SPF steps—needs assessment. As such, each coalition was required to submit a needs assessment plan to DAODAS by November. The plan was to outline how the County CAST Coordinator and the coalition would complete a countywide needs assessment of the priority issue(s). CAST sites received training on data collection methods and how to conduct surveys, focus groups, and key informant interviews. To further assist sites in the preparation of their needs assessment plan, DAODAS produced a document, How to Conduct Needs Assessment: A Guidance Document, for CAST sites and hosted a training on November 16, 2010. Regional capacity coaches and the evaluation team provided technical assistance to sites. The WEB hosted a training session on evidence-based strategies on January 26, 2011 that addressed logic models for prevention of underage drinking and DUI crashes, and provided an overview of nine evidence-bases strategies. Those strategies included law enforcement, happy hour restriction, and restricting alcohol sales at public events; responsible beverage service; social host laws; graduated drivers’ license; keg registration; increasing taxes on sales of alcohol; reducing alcohol outlet density; and media advocacy.

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County needs assessments were due to the DAODAS in March of 2011. The WEB reviewed the county needs assessments and the Governor’s Council approved them in August of 2011. County evaluation plans were due in October of 2011. Evaluation trainings were held in two locations in July—one for Regions I and II and another for Regions III and IV. This training provided an overview of the CAST evaluation, examples of logic models for each of the priorities, and documents for the counties to conduct a strategy implementation assessment. From late in 2011 through June of 2014, the CAST sites implemented a series of strategies aimed at reducing underage drinking and DUI crashes. Table 2 displays the strategies used to prevent underage drinking and DUI crashes. As can be seen, there is overlap in strategies, with merchant education, compliance checks, and greater enforcement of existing policies having been implemented for both reducing underage drinking and DUI crashes.

Table 2. CAST Strategies

Underage Drinking DUI Crashes • Community mobilization to establish a

social host ordinance utilizing the Parents Who Host Lose the Most campaign

• Merchant education (Promoting Retailers Education Program, PREP)

• Compliance checks • Party patrols • Shoulder taps • Improved screening and referral • Ensuring specified penalties/diversion

programs are imposed and carried out

• Merchant education (PREP) • Help retailers improve policies regarding

the over-service of alcohol to intoxicated patrons

• Compliance checks • Public safety checkpoints • Social event monitoring and

enforcement • Over-service operations/Bar checks • DUI prosecution training • Ensure follow-through on sanctioned

penalties for convicted DUI offenders

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Answers to the Questions: How has South Carolina implemented CAST and has substance abuse prevention capacity

increased as a result of CAST?

• At the community level, South Carolina implemented the SPF SIG with faithful consideration of the 5-step SPF model, engaging in a thorough process to accomplish each step and to meet its federal grant requirements. Activities in support of the SPF SIG at the community level included funding 18 high-need community coalitions to address underage drinking and/or DUI traffic crashes, supporting those communities with substantial training and technical assistance from the Regional Capacity Coaches as they navigated the five SPF steps.

• South Carolina increased community-level capacity by conducting substantial training and

technical assistance related to the five SPF steps for the funded coalitions, particularly through the Regional Capacity Coaches. Capacity enhancement was reported for all steps of the SPF, as well as implementing environmental strategies.

Key stakeholders reported community-level gains across all the SPF steps.

“The needs assessment process was significant. Now that we are funding non CAST sites to conduct needs assessment, this is truly an opportunity for this process to become uniform

throughout the state.”

“The process works. Given the resources and guidance, low capacity coalitions can make a difference in their communities.” “Each coalition has learned something along the way about not only their community but strategic planning—connecting the dots.”

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COMMUNITY-LEVEL OUTCOMES Question 3: Have underage drinking and DUI crashes been reduced as a result of CAST? Question 4: Have associated contributing local factors been reduced as a result of CAST? This section provides the results of our community-level outcomes analyses. We first provide descriptive data and, where appropriate, inferential analyses to determine whether changes across time were statistically significant. Our analytic methods are discussed below. Overview South Carolina’s 18 funded SPF-SIG communities were geographically defined as counties and comprise two cohorts. Cohort 1 communities were funded in 2011 and began implementing interventions between September 2011 and June 2012. Cohort 2 communities were funded in 2013 and began implementation in October of that year. Most communities focused on only one of South Carolina’s two outcome priorities for the SPF SIG (reducing underage drinking and reducing DUI crashes). Two communities, Darlington and Horry Counties, focused on both priorities. The funded communities, along with their cohort, intervention start date, and targeted priorities are shown in Table 3 below.

Table 3. Funded CAST Communities

County Cohort Intervention

Start Date Underage Drinking

DUI Crashes

Abbeville 1 Mar 2012 Berkeley 1 May 2012 Cherokee 1 June 2012

Colleton 1 Sept 2011 Darlington 1 Apr 2012

Edgefield 1 June 2012

Greenwood 1 Mar 2012 Horry 1 Apr 2012

Kershaw 1 Oct 2011

Laurens 1 Mar 2012

Lexington 1 Apr 2012

Newberry 1 Mar 2012 Williamsburg 1 June 2012

Florence 2 Oct 2013 Greenville 2 Oct 2013

Lancaster 2 Oct 2013

Richland 2 Oct 2013

Sumter 2 Oct 2013

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The degree of success in addressing the two priorities for the South Carolina SPF-SIG was assessed using different data sources and analytic approaches for each outcome. The details regarding the methodology, along with the findings, are reported in separate subsections below. SPF SIG Effects on Underage Drinking Data Source and Measures Data from South Carolina’s Communities that Care (CTC) student survey were used to assess SPF SIG effects on outcomes related to underage drinking. For these analyses, the term “funded communities” refers to the subset of communities that received SPF SIG funding and targeted underage drinking. Communities funded by the SPF SIG were required to ensure participation in the survey by the school districts serving their county, on a biennial basis beginning in 2010. In addition, DAODAS also arranged for the survey to be conducted in a number of school districts serving counties not receiving SPF SIG funds. Students in grades 9 through 12 were eligible to participate, although many schools used survey sampling and administration procedures designed to primarily include students in 9th and 11th grades. The CTC survey includes questions about alcohol use and perceptions and behaviors associated with alcohol use. Many of these items were relevant to the goal of reducing underage drinking, either as direct measures of alcohol use and various risky drinking behaviors, or as intermediate outcomes (also referred to as risk factors or contributing factors) associated with use. Although the intermediate outcomes were assessed using four-point scales, for the analyses presented here they were dichotomized in order to provide consistency in the statistical models employed across all outcomes examined. The outcome measures analyzed for this evaluation were the following:

• past 30-day alcohol use, • past 30-day binge drinking, • initiation of alcohol use by age 13, • initiation of regular alcohol use by age 13, • past 30-day drink and drive, • past 30-day rode with drinking driver, • past year drunk or high at school, • disapproval of alcohol use, • parental disapproval of alcohol use, and • availability of alcohol.

Sample Composition

Selecting the sample of schools to include in the analysis was guided by several considerations. Based on the intervention start dates of the funded communities, we established 2010 and

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2012 as pre-intervention (i.e., baseline) years and 2014 as post-intervention (i.e., follow-up).1 The first criterion applied to selecting schools was whether data from each school were available for 2014 and either one or both of the two pre-intervention years. Schools that did not meet the criterion were excluded. The inclusion of data from either of the two baseline years served to substantially increase the sample size, as some schools participated in only one or the other of these two years. In addition, we examined the number of students from each school and each grade level (9 through 12) that participated each year. 2010 data were used from each school and grade level only if the number of student survey respondents for the same school and grade level was not less than half and not more than twice the corresponding number in 2014. The same criterion was applied to the 2012 data. These criteria for inclusion were applied to help ensure that data used in the analysis were from only those schools and grade levels for which a similar sampling design (or student recruitment strategy) was used across years (under the assumption that if sample sizes differed substantially across years, the sampling/recruitment strategies employed were probably not consistent). The numbers of survey respondents, by community, school, and year, for funded and comparison communities, are provided in Appendix A. Although the grade-specific sample sizes are not shown in the table, the numbers reflect the sample sizes after the grade-specific sample selection criteria described in the preceding paragraph were applied. As indicated in the table, eight of the nine SPF SIG communities that targeted underage drinking contributed to the analysis, along with six non-funded communities. Analysis Strategy An initial set of analyses was conducted for descriptive purposes. Specifically, we examined summary measures (in the form of percentages) for background student demographic variables and all of the outcome measures selected for the evaluation, by condition (i.e., funded or comparison community) and intervention phase (i.e., pre- or post-intervention). The summary descriptive analysis of the outcome measures was conducted to provide an initial indication of the presence and magnitude of possible intervention effects. For the descriptive analyses, we weighted the data using school enrollment figures by grade and race to more accurately reflect the school population from which the data originated at each point in time (i.e., pre-test and post-test).

1 The CTC survey is typically administered in February or March. Based on the intervention start dates of each funded community, and the observation that actual exposure (and response) to an intervention by the target population is generally not instantaneous and may often take several months, data from the 2012 CTC survey were considered to be “pre-intervention” for all funded communities except Colleton. Because intervention activity began in Colleton in September of 2011, assignment of their data to the pre-intervention phase was questionable. Nor was it advisable to assign those data to the post-intervention phase, as all 2012 data from comparison communities were assigned to the pre-intervention phase. Consequently, Colleton 2012 CTC data were simply excluded from the analyses reported here.

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We then employed a statistical modeling strategy to more accurately gauge the intervention effects on each outcome measure examined, and also assess the statistical significance of those effects. Specifically, a statistical model was needed to control for differences in demographic variables and to accommodate the nesting of students within schools (or communities). Because the outcome variables to be analyzed were dichotomous, a generalized linear mixed model approach (using SAS PROC GLIMMIX) was employed. Designating school-level intercepts as a random effect, student-level survey data were nested within schools to accommodate clustering effects and to reduce potential biases that could otherwise occur due to the variability in the pre-intervention years in which each school participated in the CTC survey.2

In the statistical models, phase was defined as a binary variable to distinguish pre-intervention from post-intervention years. The intervention effect (i.e., the degree to which pre- to post-intervention change in the desired direction was greater in the funded communities relative to the change in the comparison communities) was captured in the phase by condition interaction term included in each model. Student-level covariates (included in all models) were gender, grade level, and minority status (white or non-white). Also included was a term for year (1,2, or 3), which was used to capture any secular trends in the outcome measures (independent of the intervention effect) and therefore reduce the error variance that could otherwise occur due to the inclusion of two pre-intervention time points. This term was omitted from models examining the two drinking and driving outcomes, however, because data for these outcomes were not available in 2010. Descriptive Analysis Findings Demographic data from our student sample, by intervention phase and condition, are provided in Table 4. No substantial differences between conditions with respect to demographic characteristics are evident, except that fewer 10th and 12th grade students and more 11th grade students in the funded communities participated in the student survey compared to the comparison communities. This was true for both the pre-intervention and post-intervention surveys. For both conditions, there was an increase in the percent of white students who provided data in the post-intervention year. Due to the inclusion of these variables as controls in the statistical models, none of the differences just noted pose significant concerns to the validity of the modeling results.

2 A preferable approach, if feasible, would have been to model clustering of both student within schools and schools within communities. In this case, because some communities had only one participating school, clustering at both levels could not be accommodated. The choice to model the clustering of students within school (rather than within community) was made to help reduce any biases caused by schools participating in only one or the other pre-intervention years, and follows procedures employed in a recently completed and similarly designed study by PIRE investigators using student survey data from Oregon communities (Flewelling et al., 2012).

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Table 4. Demographic Characteristics by Phase and Condition

Funded Comparison Prea

(n=5187) Postb

(n=3782) Prea

(n=3558) Postb

(n=2838) Gender (% female) 50.59 48.37 48.82 50.19 Race (% white) 50.86 56.84 47.05 58.15 Grade (%)

9th 52.81 48.78 50.90 44.75 10th 7.15 8.09 13.55 16.35 11th 32.72 36.49 25.15 25.44 12th 7.33 6.64 10.40 13.46

a2010-2012 b2014

Table 5 displays descriptive, weighted data for the outcome variables. It is evident that the funded communities had somewhat higher rates of most alcohol use and risky drinking behaviors in the pre-intervention phase relative to the comparison communities. This is not surprising, considering that communities were selected based on indicators of being at high risk, including elevated rates of underage drinking. Surprisingly, drinking and driving and riding with a drinking driver exhibited the opposite pattern. Patterns observed for intermediate outcome measures were consistent with those for the alcohol use measures (i.e., funded communities exhibited higher levels of risk than comparison communities). The descriptive data in Table 5 suggest that funded communities experienced favorable changes (e.g., larger declines) relative to the comparison communities, for several outcome measures. The most sizeable of these relative differences were for 30-day alcohol use and binge drinking (see Figure 4 for a visual representation of these differences). A substantial difference favorable to the funded communities was also observed for parental disapproval of alcohol use. (See Appendix B for descriptive, weighted data for all CAST communities on the two primary underage drinking variables of interest—past 30-day use and past 30 day binge drinking.)

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Table 5. Descriptive (Weighted) Outcome Measures, by Phase and Condition

Funded Comparison Prea

(n=5187) Postb

(n=3782) Prea

(n=3558) Postb

(n=2838) 30-day alcohol use 38.69 27.04 32.71 26.26 30-day binge drinking 19.83 17.13 12.74 15.31 Initiation of use by age 13 33.16 32.03 36.83 34.18 Initiation of regular use by age 13 4.13 5.01 4.12 5.94 30-day drink and drive 2.49 4.50 5.64 4.58 30-day rode with drinking driver 18.57 17.99 21.75 17.17 Past year drunk or high at school 19.41 16.29 17.65 15.02 Disapproval of alcohol usec 26.22 33.35 31.17 37.58 Parental disapproval of alcohol usec 56.44 68.11 63.54 67.67 Availability of alcohold 60.67 60.38 59.47 60.91 a2010-2012 b2014 cDichotomized as “very wrong” (1) vs. other (0). Item is in reference to use by themselves or someone their age. dDichotomized as “very easy” or “sort of easy” (1) vs. “very hard” or “sort of hard” (0).

Figure 4. Past 30-Day Alcohol Use and Binge Drinking (Percent), Descriptive Analyses

30-day alcohol use

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Model-Based Analysis Findings The results from the statistical models used to asses SPF SIG effects are provided in Table 6. Due to the number of outcome measures examined, only the coefficients for the intervention effect itself (i.e., the phase by condition interaction term) and the statistical significance of these coefficients are displayed.

Table 6. GLIMMIX Model Results (N=15,365)

Outcome Measure

B for Intervention

Effect P value

Adjusted Means

Funded (Pre)

Funded (Post)

Comp (Pre)

Comp (Post)

30-day alcohol use -0.229 0.004* 34.55 30.84 27.73 28.95 30-day binge drinking -0.527 <0.001* 17.38 17.09 11.32 17.49 Initiation of use by age 13 -0.109 0.300 35.65 35.23 33.56 35.60 Initiation of regular use by age 13 -0.307 0.056** 3.94 5.69 3.72 7.18 30-day drinking and driving 0.514 0.060** 1.83 3.45 3.78 4.30 30-day rode with drinking driver -0.035 0.813 19.90 18.09 19.91 18.61 Past year drunk or high at school -0.124 0.189 16.80 18.34 14.77 17.91 Disapproval of alcohol usea 0.136 0.073** 26.33 33.88 31.70 36.74 Parental disapproval of alcohol usea 0.384 <0.001* 58.38 71.46 64.31 68.67 Availability of alcoholb 0.033 0.650 58.78 62.80 56.70 60.01 Note 1: Parameter estimates are for phase-by-condition interaction term. Note 2: Models include controls for gender, race (white vs. non-white), and grade. aDichotomized as “very wrong” (1) vs. other (0). Item is in reference to use by themselves or someone their age. bDichotomized as “very easy” or “sort of easy” (1) vs. “very hard” or “sort of hard” (0). * Indicates statistical significance, i.e.., p < .05. ** Indicates marginal statistical significance, i.e., p < .10.

Other than being positive or negative, the coefficients (i.e., the betas, or “B”s) shown in the table are values that are not intuitively meaningful as effect sizes. For that reason, the model-adjusted means for each outcome measure (expressed as percentages), by phase and condition, are also provided. And examination of these means and, in particular the difference between funded and comparison communities in their change over time, provides a more direct and interpretable indication of the magnitude of the SPF SIG effects. The signs of the B values indicate that most outcomes changed in a manner that indicates a favorable effect of the SPF SIG funding. Note that because the two measures tapping disapproval of alcohol use are coded such that higher values are desirable, favorable SPF SIG effects are indicated by positive (rather than negative) B values. Of those favorable to the SPF SIG, the effects on any 30-day alcohol use, binge drinking, and parental disapproval of alcohol use by their child were all statistically significant (i.e., the significance levels were less than .05). The effects on regular use of alcohol by age 13 and the respondents’ own disapproval of alcohol use by someone their age were also marginally significant (p=.056 and .073, respectively).

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The two outcome measures for which the effect was in an unfavorable direction were driving after drinking and perceived availability of alcohol. Of those two exceptions, the undesirable effect on driving after drinking was marginally significant (p=.060). To help visualize the magnitude of the apparent SPF SIG effects, the adjusted means (again reported as percentages) for all outcomes in which the intervention effect was at least marginally significant (p<.10 or stronger) are displayed in Figure 5. The plots include the five outcomes for which an effect in the favorable direction was observed, as well as the single outcome that was in the unfavorable direction.

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Figure 5. Adjusted Means for Statistically Significant SPF SIG Effects (including p<.10)

30-day alcohol use (p=.004)

30-day binge drinking (p<.001)

Initiation of regular use by age 13 (p=.056)

30-day drinking and driving (p=.060)*

Disapproval of alcohol use (p=.073)

Parental disapproval of alcohol use (p<.001)

*Effect was not in the desired direction.

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SPF SIG Effects on DUI Crashes Data Sources and Measures To assess the effects of the CAST project on DUI crashes, we obtained county-level data on alcohol-related motor vehicle crashes (ARMV) from the SC Office of Highway Safety (SCOHS). Crash data, at the level of each individual crash, were obtained from SCOHS from January 2008 through April 2014. The records include the month and year of the crash, the county where the crash occurred, and an indicator regarding whether alcohol was a contributing factor in the crash as based on either breath or blood alcohol analysis or the investigating officer’s judgment. The records also indicated the number of deaths (if any) that occurred among the persons involved. Other attributes of each crash reported in the data files included the time of day the crash occurred and the number of vehicles involved. Based on the files received, the following three measures were calculated to support a descriptive analysis of SPF SIG effects on DUI crashes:

• Alcohol-related crashes (ARMVCs)as a percent of all crashes • Alcohol-related crash deaths (ARMVCDs) as a percentage of all crash deaths • Single vehicle nighttime crashes as a percent of all crashes

Notably, none of these measures is a perfect measure of DUI crashes. The first measure is limited because of the variability in how “alcohol-related” is determined and reported. Deaths from motor vehicle crashes are more thoroughly investigated and, therefore, are more likely to be accurately assessed and reported as being alcohol-related; they, however, have a much lower frequency rate and, therefore, are considered less stable than crashes. Finally, SVNCs have been shown to be often the result of DUI, while also being less subject to variability in assessment and reporting practices to the extent that “alcohol-related” crashes are. SVNCs also occur with a much greater frequency than crash deaths, and, therefore, less subject to analysis and interpretation challenges posed by low frequent outcomes. Not all SVNCs are alcohol-involved, however; so analyses of SVNCs necessarily include some cases that are false positives. For the descriptive analysis, these measures were aggregated across months into pre-intervention and post-intervention phases, and by also experimental group (see next section explanation of groups A, B, and C).

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Sample Composition For the motor vehicle crash data, availability of the data (starting with January 2008) was not a consideration in defining the sample for inclusion in the analysis, as the crash data were available for all SC counties. The counties were, however, placed into one of three mutually exclusive groups:

A. the 11 funded counties targeting DUI crashes B. the 7 funded counties not targeting DUI crashes C. all 28 non-funded counties

Group A was further differentiated by cohort, with eight communities belonging to cohort 1 and three belonging to cohort 2. The composition of groups B and C did not vary by cohort, but the findings reported for these two groups are provided in a manner that facilitates comparisons to both cohort 1 communities (i.e., using the cohort 1 pre- and post-intervention cut-points) and cohort 2 communities (using the cohort 2 pre- and post-intervention cut-points). Analysis Strategy Descriptive analyses were first conducted to obtain an overall understanding of the patterns in the outcome measures with respect to pre-post changes in group A relative to groups B and C. For each group, the differences in the outcome measures between the pre- and post-intervention phases on each of the two outcome measures were examined, and these differences were then compared across the groups. Intervention effects were suggested by patterns in which relatively larger decreases in each measure were observed for Group A in comparison to the other two groups. The analysis was augmented by independent group Z tests for the statistical significance of pre-post differences in proportions for each group, by cohort. Although communities not targeting DUI crashes (i.e., groups B and C) could have been considered as a single comparison group, it was plausible that some effects of the SPF SIG-funded underage drinking prevention activities in the group B communities could also influence DUI crash rates in those communities. As a precaution, therefore, groups B and C were kept separate, and pre-post changes experienced in each of those two groups were used as independent comparisons to help interpret the changes observed in the group A communities. As was done for the analysis of the student survey data, it was necessary to establish a common cut-point across communities to distinguish pre-intervention from post-intervention months. Due to the large difference in intervention start dates between cohorts 1 and 2, data from each cohort were analyzed separately. For each cohort, the median intervention start date among the funded communities was used to determine the start of the post-intervention phase. Those dates were then also applied to the data for the comparison groups B and C. For comparing the time series results for cohort 1 communities, the pre- and post-intervention periods for groups

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B and C were defined using the cohort 1 median start dates. For comparisons to cohort 2 communities, the median start dates for cohort 2 communities were applied. The second analytic approach applied to the crash data was time series modeling. This approach offers a more rigorous and potentially more powerful technique for assessing intervention effects than the descriptive analysis approach described above. These models examine the variability in multiple measurement points over time to determine whether there are statistically significant disruptions in trends coincident with external events, such as the implementation of preventive interventions. Because time series models use the pre-intervention data points as the “control” values in assessing intervention effects, they provide a basis for attributing intervention effects using only data from the intervention site(s). In the analysis reported here, however, we also analyzed the time series in the outcome measures of interest for groups B and C, to provide additional context for interpreting the findings based on group A. As with the descriptive analyses, all the time series models were assessed separately for cohorts 1 and 2. Details regarding the methodology employed are embedded into the section of findings below. Descriptive Analysis Findings The logic of the descriptive analysis is simply to compare funded communities that targeted DUI crashes with two comparison groups of communities, with respect to overall changes between pre- and post-intervention months on the selected outcome measures. Table 8 shows the numbers of ARMVCs and the percent of all crashes that were alcohol related, by cohort, group, and intervention phase. Rows of data pertaining to the SPF SIG communities that targeted DUI crashes (i.e., group A) are highlighted. As shown in the table, pre-post changes in the percentages of crashes that were alcohol related were slight, ranging (across the cohorts and groups) from a 0.2 percentage point drop to a 0.4 percentage point increase. Within the cohort 1 data, group A experienced the largest drop, although the pre-post difference was not statistically significant. Furthermore, data for the two cohort 1 comparison groups did not suggest any meaningful differences between them and group A in terms of pre-post changes. A similar interpretation applies to the cohort 2 data.

Table 8. Number and Percent of Motor Vehicle Crashes that Were Alcohol Related (AR), by Cohort, Group, and Intervention Phase.

Cohort Group Pre-Intervention Post Intervention Percentage

Point Diff Z value P value Number Percent AR Number Percent AR 1 A 5356 6.3 2617 6.1 -0.2 -1.58 .113 1 B 1693 5.8 840 5.8 0.0 -.02 .984 1 C 10155 4.9 5033 4.8 -0.1 -.91 .365 2 A 5700 4.1 652 4.2 0.1 .33 .745 2 B 1822 5.4 213 5.8 0.4 1.11 .267 2 C 13809 4.9 1379 4.7 -0.2 -1.25 .211

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With respect to ARMVCDs, the findings in Table 9 below indicate that significant pre-post decreases in the percent of motor vehicle crash deaths that were alcohol related were observed for all groups and both cohorts. These findings reflect a statewide overall secular downward trend in this measure. Although large and statistically significant decreases were observed for group A communities, the decreases were generally of equal or even greater magnitude for the other groups.

Table 9. Number and Percent of Motor Vehicle Crash Deaths that Were Alcohol Related (AR), by Cohort, Group, and Intervention Phase.

Cohort Group Pre-Intervention Post Intervention Percentage

Point Diff Z value P value Number Percent AR Number Percent AR 1 A 329 44.4 120 33.5 -10.9 -3.50 .000* 1 B 128 40.9 35 25.0 -15.9 -3.41 .001* 1 C 709 38.3 205 25.5 -12.7 -6.62 .000* 2 A 300 42.8 25 29.8 -13.0 -2.44 .015* 2 B 97 32.1 2 9.1 -23.0 -3.38 .001* 2 C 860 35.3 54 24.9 -10.4 -3.35 .001*

* Indicates statistical significance, i.e.., p < .05.

As was the case for ARMVCs, significant pre- to post-intervention decreases were also observed in group A communities (both cohorts) for the percent of crashes that were SVNCs (Table 10). Even larger percentage point decreases, however, were observed for the unfunded (group C) communities. (See Appendices C – E for pre-intervention and post-intervention descriptive outcome data for all CAST communities regarding their DUI crash outcomes.)

Table 10. Number and Percent of Motor Vehicle Crashes that Were Single Vehicle Nighttime Crashes (SVNC), by Cohort, Group, and Intervention phase.

Cohort Group Pre-Intervention Post Intervention Percentage

Point Diff Z value P value Number Percent SVNC Number Percent SVNC 1 A 8172 9.7 3913 9.2 -.5 -3.08 .002* 1 B 3311 11.3 1580 10.9 -.4 -1.39 .165 1 C 19,813 9.5 9366 8.9 -.6 -5.23 .000* 2 A 8727 6.3 916 5.9 -.4 -2.22 .026* 2 B 3088 9.1 344 9.4 .3 .59 .556 2 C 26,615 9.3 2564 8.7 -.6 -3.62 .000*

* Indicates statistical significance, i.e.., < .05. ** Indicates marginal statistical significant, i.e., < .10.

Findings from Time Series Modeling Analysis For these analyses we used Auto Regressive Integrated Moving Average (ARIMA) modeling with both non-seasonal (i.e., applying to the whole time series) and seasonal (i.e., recurring yearly patterns) components. The outcome measures analyzed were the monthly counts of ARMVCs, ARMCDs, and SVNCs. The external predictors included in each model were a dummy variable representing when the “interruption” in the series occurred (i.e., the median SPF-SIG intervention start date) and a statistical control for the total number of motor crashes (or

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deaths, depending on the measure being analyzed) that occurred each month.3 The software employed to run the analysis was SPSS 20 Expert Time Series Modeler. A preliminary step in the analysis was to determine the best fitting model with both seasonal and non-seasonal components for the time series only with no additional predictors. More specifically, the models examined the autoregressive [AR: correlation of time point with preceding time point(s)], integrated [I: differencing between time point with preceding time point(s)], and moving average (MA: moving average across time points as the best point estimate) components of the time series. The SPSS expert modeler determines the number or time points across which these adjustments should occur to produce the best fitting stationary model. The number of time points used for each model component (AR, I, and MA, respectively) is indicated in (x, y, z) format. After the best fitting model was identified, we then added the intervention predictor and statistical control measure to this best fitting model. Thus, the primary effect of interest is the intervention predictor in the final model, which represents whether after explaining changes that occurred over time, whether there was additional variability over time explained by the SPF-SIG intervention. This parameter has no intuitively meaningful interpretation, but its direction and statistical significance is reflected in an accompany t-test statistic as shown in the tables below. Negative values of the t-test indicate intervention effects associated with a reduction in the outcome being assessed. Findings from the time series models, for each of the three outcome measures analyzed, are shown in Tables 11 through 13. Information provided in these tables includes, for each cohort and group:

• the structure of the model that was used to generate the findings reported • the stationary r2 value (the extent to the non-seasonal and seasonal model components

could explain the variability across time; larger values indicate a better model fit and range between .00 and 1.00)

• the Ljung-Box Q and accompanying probability (p) (this statistic is a test for whether unexplained autocorrelation remains in the residuals; p values <.05 indicate a significant level of unexplained autocorrelation, which would suggest a bias if a statistically significant intervention effect was found)

• t-test value for the interruption effect and accompanying probability (p) Similar to what was found in the descriptive analysis, the time series analysis revealed no statistically significant effects on ARMVCs (see Table 11). The directions of the effects observed, although not significant, were consistent with those from the descriptive data; i.e., in the desired direction for cohort 1 communities but not for cohort 2 communities.

3 For the SVNC models, following methods used in previous studies, the control variable used was the number of multi-vehicle daytime crashes rather than the total number of crashes. Use of this variable to control for crash propensity resulted in more stable models.

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The models for the ARMVCDs, however (see Table 12), revealed a statistically significant (p=.02) favorable effect for cohort 2 communities, and a marginally significant (p=.07) favorable effect for the cohort 1 communities. Except for the even stronger favorable effect for group B when compared to group A cohort 1, and contrary to the descriptive analysis findings, there was no evidence of a favorable effect for any of the other comparison groups, thereby strengthening the support for a real SPF SIG effect on ARMVCDs. Similar to the time series modeling results for ARMVCs, no significant intervention effects were found for SVNCs (Table 13). In this case, however, the intervention effects observed for Group A were at least in the desired direction for both cohorts 1 and 2.

Table 11. Time series model results for ARMVCs

Cohort Group

Non-Seasonal

Model Seasonal

Model r^2

(stationary) Ljung-Box

Q

p value for Ljung-

Box Q

t-test for intervention

effect

p value for t-test

1 A 0,0,0 1,0,0 .33 20.22 .26 -.78 .44 1 B 0,0,0 0,0,0 .14 15.15 .65 .07 .95 1 C 0,0,0 0,0,0 .33 13.85 .74 -.52 .61 2 A 1,0,1 0,0,0 .28 12.19 .73 .71 .48 2 B 0,0,0 0,0,0 .18 20.08 .33 1.39 .17 2 C 0,0,0 0,0,0 .33 12.75 .81 -.89 .38

Table12. Time series model results for ARMVCDs

Cohort Group

Non-Seasonal

Model Seasonal

Model r^2

(stationary) Ljung-Box Q

p value for

Ljung-Box Q

t-test for intervention

effect p value 1 A 2,0,0 0,0,0 .42 16.28 .50 -1.83 .07** 1 B 0,0,1 0,0,0 .27 8.99 .94 -2.88 .01* 1 C 0,1,0 0,0,0 .19 22.31 .22 1.62 .11 2 A 0,0,0 0,0,0 .30 10.44 .92 -2.29 .02* 2 B 0,1,1 0,0,0 .51 9.37 .93 .16 .88 2 C 0,1,0 0,0,0 .16 21.42 .26 .49 .62

* Indicates statistical significance, i.e.., < .05. ** Indicates marginal statistical significance, i.e., < .10.

Table 13. Time series model results for SVNCs

Cohort Group

Non-Seasonal

Model Seasonal

Model r^2

(stationary) Ljung-Box Q

p value for

Ljung-Box Q

t-test for intervention

effect p value 1 A 1,0,1 0,0,1 .25 28.94 .02 -.70 .49 1 B 1,0,1 0,0,0 .11 19.32 .25 -1.08 .28 1 C 0,0,1 0,1,0 .11 22.57 .16 .17 .87 2 A 0,0,1 0,0,0 .21 13.20 .72 -.86 .39 2 B 0,0,0 0,0,0 .03 11.36 .88 1.54 .13 2 C 0,0,1 0,1,0 .11 22.19 .18 .24 .81

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Capacity We were interested in assessing whether coalition capacity at pre-intervention had an impact on changes in the outcome measures. Coalition capacity was assessed by asking state project staff and regional coaches to rate the coalitions on pre-SPF SIG capacity, using a rating scale of 1 – 3 (1 = high capacity, 2 = moderate capacity, 3 = low capacity). State project staff and regional coaches used a consensus process to generate one rating per CAST coalition. For our analyses, we divided the CAST sites into the three capacity groups (three high, five moderate, and ten low) and compared them to assess whether pre-SPF capacity had an effect on priority outcomes. For these analyses, all CAST sites were included, regardless of whether their priority was underage drinking or DUI crashes. Underage Drinking Figures 6 and 7 below display descriptive data for past 30 day alcohol use and binge drinking (the two primary outcomes of interest). These figures simply show the mean pre-intervention prevalence rate across the communities (using 2010 and 2012 data combined) and the mean post-intervention prevalence rate across communities (using 2014 data). For past 30-day alcohol use, it appears that (a) moderate capacity communities had higher use rates at pre and post, (b) all capacity groups demonstrated decreases in use, and (c) the reductions in use were at about the same rate across groups. This suggests that pre-CAST capacity did not play a role in reductions in past 30 day alcohol use. Our inferential analyses (controlling for demographic factors and clustering of students) confirm this conclusion, in that there were no statistically significant differences between capacity groups.

Figure 6. Past 30 Day Alcohol Use in CAST Communities,

by Pre-Intervention Coalition Capacity

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For binge drinking, it appears that (a) moderate capacity communities had higher use rates at pre and post, (b) all capacity groups demonstrated decreases in binge drinking, and (c) the reductions in binge drinking among the low capacity group may have been more pronounced than the other two groups. Our inferential analyses did not, however, detect any differences between the capacity groups on binge drinking. Therefore, it appears that pre-CAST capacity did not have an impact on binge drinking.

Figure 7. Binge Drinking in CAST Communities,

by Pre-Intervention Coalition Capacity

DUI Crashes Figure 8 below displays descriptive data for alcohol-related traffic crash deaths (ARTCDs), the one DUI-related variable for which we found an overall effect of the CAST funding. (That is, no overall effects were found for alcohol-related crashes or single vehicle nighttime crashes.) This figure simply shows the mean pre-intervention rate of crash deaths that were alcohol-related and the mean post-intervention rate of crash deaths that were alcohol-related. It appears that (a) high capacity communities had a lower ARTCD rate at pre and post, (b) all capacity groups demonstrated decreases in ARTCD rates, and (c) the reductions in rates were more pronounced for high capacity communities. This suggests that pre-CAST high capacity communities may have been more successful at reducing ARTCD rates than other communities. Our inferential analyses (examining monthly data and controlling for differences across communities) support this suggestion, in that high capacity CAST communities showed statistically significant intervention effects for Cohort 1 and Cohort 2 communities, whereas there was no consistent pattern regarding moderate and low capacity communities in Cohorts 1

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and 2. Thus, although the data should be interpreted cautiously, it appears that higher capacity CAST communities were more likely to contribute to desired changes in alcohol-related traffic crash deaths.

Figure 8. Alcohol-Related Traffic Crash Deaths in CAST Communities, by Pre-Intervention Coalition Capacity

Summary of Community-level Outcomes Analysis Findings SPF SIG Effects on Underage Drinking With respect to underage drinking prevention, the findings in this report are compelling. Whereas prevalence rates of both any alcohol use and binge drinking among high school students increased in comparison communities, both measures decreased in the SPF SIG funded communities that targeted underage drinking (using the adjusted prevalence rates). The differences between funded and comparison communities in the changes over time for both measures were statistically significant. Furthermore, three additional outcomes related to underage drinking (regular drinking by age 13, disapproval of alcohol use, and parental disapproval of alcohol use) all exhibited changes that were more favorable in the SPF SIG communities and attained at least marginal (p<.10) levels of statistical significance. The lone outcome measure that exhibited a marginally significant effect in the direction opposite of what was expected was driving after drinking in the past 30 days. The positive results obtained most of the other outcome measures make this finding all the more difficult to explain. It is, however, important to note the extremely low pre-intervention value for this measure in the funded communities (1.8% after adjustment for covariates). Typically,

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prevalence levels this low are unlikely to decline further and will often drift higher in subsequent measurements. That appears to be what has happened here. Additionally, even though funded communities experienced a greater increase in this measure, the post-intervention prevalence rate was still lower among students in funded communities relative to comparison communities. One potential limitation of the methodology used for this study, with regard to making causal attributions to the role of the SPG SIG funding, is the fact that communities were not randomly assigned to condition. Indeed, funded communities were specifically selected based on prior evidence of being at high risk for underage drinking or DUI crashes. This design is susceptible to “regression to the mean,” in which communities that happened to have higher than average values on outcome measures at one point in time tend to move towards the average value at later points. The degree to which this occurs varies, and depends on the level of random variation over time that exists in the outcome measures being examined. In reviewing the plots shown in Figure 4, it appears that some degree of regression to the mean may have been operating for several of the student survey measures, particularly any alcohol use and binge drinking. To investigate this potential concern further, the data were analyzed again with the same models but this time only 2012 survey data were used to represent the pre-intervention phase. The exclusion of 2010 CTC data helps to reduce concern regarding regression to the mean, as 2012 data were not available at the time that cohort 1 communities were selected to receive SPF SIG funds. Furthermore, if this additional analysis did not substantively change the overall pattern of findings, this would indicate that different levels of the outcome measures at baseline were more likely reflective of stable differences over time rather than random variability, and therefore less prone to regression to the mean. The findings from this analysis largely support the notion that regression to the mean was not fully driving the observed changes over time. Specifically, all there positive and statistically significant (p<.05) SPF SIG effects remained so when only 2012 data were used for baseline measures. Although the potential biasing effect of regression to the mean cannot be totally ruled out, it appears unlikely that it could be considered as a sole or even the main explanation for the positive SPF SIG effects observed in this study. SPF SIG Effects on DUI Crashes Purely based on the descriptive data analysis, in which monthly motor vehicle crash data were aggregated into pre- and post-intervention phases, no consistent and compelling evidence for SPF SIG effects on DUI crashes emerged from the findings. Although decreases were observed among funded communities in either one or both cohorts for all three outcome measures examined, the decreases were either not statistically significant (as in the case of ARMVCs), or they were significant but not notably greater (and sometimes less) than the decreases experienced in either or both of the comparison groups (as in the case of both ARMVCDs and SVNCs). Based on these descriptive findings only, it would be unwarranted to conclude that SPF

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SIG funding contributed to decreases in any of the three outcome measures used to reflect DUI crashes. Use of the time series modeling approach took full advantage of the precise (i.e., monthly) time periods for which the crash data can be disaggregated. As such, this technique provided a more sensitive assessment of whether an intervention effect was evident at the point where intervention activities began in the funded communities. Even using this more rigorous technique, however, no significant effects on SPF SIG funding on ARMVCs and SVNCs were found. At least marginally significant effects favorable to the SPF SIG were observed, however, on ARMVCDs, both for cohort 1 (p=.07), and cohort 2 (p=.02). The certainty of the positive finding regarding SPF SIG effects on ARMVCDs is mitigated to some degree by the fact that no such effects were observed for the other two DUI crash indicators. It is unclear why SPF SIG activities directed to reducing DUI crashes would affect ARMVCDs only, and not also ARMVCs and SVNCs. The significant intervention effect observed for Group B (cohort 1) adds to the uncertainty as to whether SPF SIG activities may have contributed to decreases in ARMVCDs, although it should be noted that many of the CAST underage drinking strategies include or overlap with DUI prevention strategies and, therefore, may have had a positive effect on ARMVCDs. The strongest evidence for a SPF SIG effect on ARMVCDs is based on the findings for the cohort 2 communities, for which a downward shift in the outcome measure was found only for group A, and that effect was statistically significant. Based on the entirety of the findings from both the descriptive and the time series analyses, the mixed (but mostly null) results do not lend themselves to conclude that SPF SIG activities decreased DUI crashes overall in the funded communities. That said, there is some evidence that these activities did contribute to declines in one particular DUI crash type, namely ARMVCDs (particularly in cohort 2 communities). Capacity We found no significant effects of coalition capacity on 30-day alcohol use or binge drinking, but did find an effect on ARTCDs. Although the data should be interpreted cautiously, it appears that high capacity CAST communities were more likely to contribute to desired changes in alcohol-related traffic crash deaths than low or moderate capacity communities.

35

Answer to the Questions: Have underage drinking and DUI crashes been reduced as a result of CAST and have

associated contributing local factors been reduced as a result of CAST?

When compared to communities that were not funded through the CAST project, CAST communities had statistically significant reductions in underage drinking and factors related to underage drinking. In particular, we found desirable relative changes in CAST communities in the following: • Past 30-day alcohol use; • Binge drinking; • Early initiation of regular alcohol use; • Approval of alcohol use; and • Perceived parental approval of alcohol use. We also found some evidence that CAST communities (particularly those in Cohort 2) had significant reductions in DUI crash deaths when compared to non-CAST communities.

36

APPENDIX A. STUDENT SURVEY SAMPLE SIZES BY COMMUNITY, SCHOOL, AND YEAR

Community School 2010 2012 2014

Funded Communities Abbeville Abbeville High School 287 162 201 Abbeville Dixie High School 169

200

Berkeley Cane Bay High School 103 151 104 Berkeley Cross High School

10 20

Berkeley Goose Creek High School

83 54 Berkeley Hanahan High School 10 90 66 Berkeley Stratford High School

38 34

Colleton Colleton County High School 306

240 Darlington Darlington High School

25 26

Darlington Hartsville High School

9 9 Darlington Lamar High School 57

66

Florence Hannah Pamplico High School 180

103 Florence Johnsonville High School 36

23

Greenwood Emerald High School

140 199 Greenwood Greenwood High School

442 545

Greenwood Ninety Six High School 355 58 211 Greenwood Ware Shoals High School 134 200 232 Horry Aynor High School 120 125 122 Horry Carolina Forest High School 67

59

Horry Green Sea Floyds High School 127 87 119 Horry Loris High School 164 129 139 Horry Myrtle Beach High School 79

76

Horry North Myrtle Beach High School 215 237 343 Horry Socastee High School 323 104 320 Horry St. James High 21 6 30 Newberry Mid-Carolina High School 121 137 116 Newberry Newberry High School

60 96

Newberry Whitmire High School

20 29 Comparison Communities Allendale Allendale Fairfax High School 152 121 Anderson Wren High School 149 239 Barnwell Barnwell High School 244 450 498 Barnwell Blackville-Hilda High School 170 42 106 Barnwell Williston-Elko High School 200 175 238 Chesterfield Central High School 209 174 Chesterfield McBee High School 64 113 Clarendon Manning High School 37 54 Clarendon Scott's Branch High School 105 133 100 Fairfield Fairfield Central High School 162 99 Pickens Easley High School 596 554 Pickens Liberty High School 87 158 Pickens Pickens High School 583 384

37

APPENDIX B. UNDERAGE DRINKING RATES BY COMMUNITY

Past 30 Day Alcohol (%) Past 30 Day Binge Drinking (%)

2010 2012 2014 2010 2012 2014 Funded Communities Abbeville 41.2 40.29 23.47 24.3 25.46 14.22 Berkeley 38.3 35.73 33.43 22.6 17.64 20.32 Cherokee 25.5 -- 19.44 16.4 -- 10.80 Colleton 50.6 52.22 36.18 32.2 -- 27.18 Darlington 45.3 22.40 22.54 31.9 14.43 16.82 Florence 31.5 -- 14.74 17.4 -- 9.91 Greenwood 35.5 37.84 31.16 23.1 22.20 20.46 Horry 45.4 40.06 30.76 27.2 21.02 19.56 Newberry 50.7 25.39 25.22 22.0 14.71 15.36 Comparison Communities Allendale -- 47.67 29.28 -- 19.58 17.37 Anderson 31.1 -- 17.84 20.0 -- 8.10 Barnwell 33.5 28.97 33.43 18.6 15.26 24.05 Chesterfield 28.8 -- 32.82 16.2 -- 20.66 Clarendon 36.6 30.94 23.18 22.3 13.96 13.96 Fairfield 39.4 27.97 21.91 17.3 -- 15.10 Pickens 31.6 -- 27.76 20.4 -- 16.20 Note: These data are weighted by grade and race, for each community and each year separately. No adjustments were made between communities or between years. Therefore, comparisons across community and across time should be made with caution.

38

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