Social and epidemiological assessment of drug use: a case study of rural youth in Missouri.
Abstract: Substance use and abuse among rural youth represents a significant public health concern throughout the United States. Unfortunately, many rural communities have a limited health education workforce; therefore, conducting appropriate needs assessment strategies in youth drug use is difficult. This study details a social and epidemiological needs assessment in two rural counties that experienced high rates of youth drug use. The assessment revealed unique insights in to the social ecological influences of drug use among youth in the rural communities.
Article Type: Case study
Subject: Epidemiology (Case studies)
Epidemiology (Social aspects)
Teenagers (Case studies)
Teenagers (Social aspects)
Youth (Case studies)
Youth (Social aspects)
Drugs and youth (Case studies)
Drugs and youth (Prevention)
Drugs and youth (Social aspects)
Mental health (Case studies)
Mental health (Social aspects)
Psychiatric services (Case studies)
Psychiatric services (Social aspects)
Authors: Williams, Ronald D., Jr.
Barnes, Jeremy T.
Leoni, Edward
Pub Date: 03/22/2011
Publication: Name: American Journal of Health Studies Publisher: American Journal of Health Studies Audience: Professional Format: Magazine/Journal Subject: Health Copyright: COPYRIGHT 2011 American Journal of Health Studies ISSN: 1090-0500
Issue: Date: Spring, 2011 Source Volume: 26 Source Issue: 2
Topic: Event Code: 290 Public affairs
Product: Product Code: E121930 Youth; 8000186 Mental Health Care; 9105250 Mental Health Programs NAICS Code: 62142 Outpatient Mental Health and Substance Abuse Centers; 92312 Administration of Public Health Programs
Accession Number: 308742262
Full Text: Substance use and abuse among youth represents a significant public health concern throughout the United States. The 2007 National Survey on Drug Use and Health (NSDUH) indicated that approximately 19.9 million Americans aged twelve or older have used illicit drugs in the past month representing 8.0% of the twelve and older population (U.S. Substance Abuse and Mental Health Services Administration, 2008). The 2007 NSDUH also revealed that the illicit drug use rate among youth age 12-17 was 9.5%. Despite national and local prevention efforts, rates of youth substance use have increased. The 2009 NSDUH revealed that approximately 21.8 million Americans aged twelve or older have used illicit drugs in the past month representing 8.7% of the twelve and older population (U.S. Substance Abuse and Mental Health Services Administration, 2010). The illicit drug use rate among youth age 12-17 increased to 10.0%.

Comparable trends exist among youth with the use of non-illicit substances. Youth aged 12-17 reported steady or increased rates of cigarette use, smokeless tobacco use, non-medical prescription drug use, alcohol use, and binge drinking (U.S. Substance Abuse and Mental Health Services Administration, 2010). Similarly, Pemberton, Colliver, Robbins, and Gfroerer (2008) reported that 53.9% of individuals aged 12-20 used alcohol in their lifetime, 28.3% used alcohol within the past month, and 19.0 % engaged in binge drinking in the past month. The use and abuse of alcohol and other drugs is both directly and indirectly related to various physical, social, and mental problems among youth. Such problems include unsafe sex, driving under the influence, auto accidents, violence, overdose, addiction, heart and respiratory diseases, and impaired cognitive functioning (Baskin-Sommers & Sommers, 2006; Benotsch, Koester, Luckman, Martin, & Cejka, 2011; Brown, et al., 2009; Hall, Room, & Bondy, 1998).

While many communities face the problem of youth substance abuse, rural areas across the United States are experiencing these issues to a greater degree. Van Gundy (2006) revealed that rural youth are more likely to consume alcohol than non-rural youth (37.0% compared to 34.0%). Wright and Sathe (2005) reported that rural states in the West, Southwest, and Midwest tend to show the highest rates of alcohol abuse. In addition to alcohol, youth age 12-17 in rural areas also show higher usage rates of cocaine, hallucinogens, heroin, and marijuana (Van Gundy, 2006). Additionally, rural areas are less likely to have appropriate substance abuse treatment options (No Place to Hide, 2002).

Such high rates of alcohol and other drug use among rural youth have led to increased awareness and prevention efforts in these areas. At the 2001 National Rural Health Research Center Director's Meeting, substance abuse was identified as a rural health priority. Additionally, the Rural Healthy People 2010 Project determined the top priorities in rural areas with substance abuse ranking as the seventh priority in rural public health (Gamm, Hutchinson, Bellamy, & Dabney, 2002). Recognizing that substance use among rural youth is a major concern has led some states to increase local prevention funding.

In 2009, the National Advisory Committee on Rural Health and Human Services indicated that lack of preventive health services is a major issue affecting healthy development for at-risk rural youth. The Committee indicated multiple factors influencing this lack of prevention including a lack of prevention-focused funding and an underdeveloped workforce. While the increase in prevention funding is needed, professionals must realize that rural areas may be the least likely to have a workforce trained in prevention science. Due to this possible lack of expertise, rural health professionals with expertise in tertiary treatment can be hesitant to pursue the development and implementation of primary prevention programs targeting youth substance use. One such situation occurred in the state of Missouri when a regional drug court received unsolicited funds designed to be used for youth substance abuse prevention efforts. Having no expertise in prevention, the funded organization approached local prevention professionals to assist in assess the community needs related to youth drug use. The purpose of this article is to provide a guide to health educators and prevention professionals who may find themselves in a similar situation. This study describes a comprehensive social and epidemiological assessment of rural youth substance use designed within the framework of the social ecology model of health behavior.


Regarding influences on health, social ecology suggests that behavior is affected by multiple levels of influence (McLeroy, Bibeau, Steckler, & Glanz, 1988). This model suggests that behavior is determined by a complex interplay of personal, social, and environmental contributing factors. McLeroy and colleagues (1988) outlined the following areas that influence behavior: intrapersonal factors, interpersonal factors, institutional or organizational factors, community factors, and public policy. Explanation of the levels of influence is provided in Table 1. Ecological perspectives been used throughout health science research to explore various risky behaviors among youth including substance use, violence, and early sexual practices (Ennett, et al., 2008; Kumpfer & Turner, 1991; Mason, Mennis, Coatsworth, Valente, Lawrence, & Pate, 2009; Mason, Valente, Coatsworth, Mennis, Lawrence, & Zelenak, 2010; Riner & Saywell; 2002; Stephens, 2001; Williams, Perko, Belcher, LeaverDunn, Usdan, & Leeper, 2006; Williams, Perko, Usdan, Leeper, Belcher, & Leaver-Dunn, 2008).


The state of Missouri is approximately 27.0% rural (Van Gundy, 2006). Historically, youth in Missouri have shown high rates of alcohol and other substance use, particularly among adolescents. According to the 2006 Missouri Student Survey, Missouri students are higher than the national average in 30-day alcohol use, 30-day tobacco use, and 30day marijuana use (Evans, Sale, Karamehic, Breejen, Witworth, & McCudden, 2006). The survey also indicated that counties in the Southeastern region of the state are above average in youth substance use rates. Although multiple drug prevention programs have been implemented in the area, results have yielded very little success in the reduction of usage rates and related problems. The Missouri Department of Mental Health Division of Alcohol & Drug Abuse Prevention provided unsolicited funding to the region's Judicial Circuit of Missouri Drug Court Programs for the sole purpose of substance use prevention among youth in two rural, southeast Missouri counties. However, administrators of the drug court, whose primary responsibility is judicial and treatment affairs, possessed little to no expertise in prevention.

The Southeast Regional Support Center (SRSC), a local substance use prevention center, was initially contacted by an administrator for the Judicial Circuit of Missouri Drug Court Programs, to assist in the development of an evaluation for the previous three-year grant period. In discussions with drug court officials, it was revealed that the funding had not been used during the grant period. As an evaluation of unused funds is impractical, drug court officials and SRSC prevention professionals determined the most effective use of funds would be to conduct a needs assessment with intentions to develop interventions to reduce substance use and related behaviors among youth in the area. The needs assessment purpose was to determine quality of life issues as they relate to risk and protective factors in substance abuse prevention. After subsequent communication, both parties reached an agreement and plans were finalized for the SRSC to design and implement the needs assessment strategy. The expected outcome of this assessment was to make research-based recommendations on how the funding could most effectively be used for substance abuse prevention.

The SRSC formulated a field research team to collect and analyze qualitative, quantitative, and proxy data within the target population. This research team consisted of a group of field researchers and field recorders. Senior members of the research team included University faculty with expertise in research methods, substance abuse prevention, behavior change, and statistical analysis. A cadre of six university students served as recorders and worked under the direct supervision of the senior members of the field research team. Prior to beginning the needs assessment, each member of the field research team participated in data collection training sessions. Two sessions were held with each lasting approximately three hours. Training consisted of an introduction to the research plan, models of behavior, methods of data collection, interviewing ability, as well as other skills necessary to appropriately and accurately complete research duties.


The social assessment phase within the target population began by identifying community stakeholders and gatekeepers. The SRSC requested and received from a drug court administrator a list of thirteen key community stakeholders that may have insight into the quality-of-life issues facing members of their respective communities. The field research team requested and conducted interviews with each identified stakeholder. In the process of interviewing, three additional stakeholders were identified and interviewed for a total of sixteen interviews. Stakeholders included various community members and leaders including school principals, teachers, law enforcement, business owners, school counselors, and professionals working in social service organizations. Interviews lasted approximately thirty minutes each yielding a total of 503 minutes of interview time.

Additionally, interviews were conducted with area youth within the local schools through a non-probability convenience sample (n = 203). Researchers were allowed to area sample at the school campuses during lunch periods. Students were approached, informed of the purpose of the study, ensured confidentiality, and asked to participate in a brief interview. Interviews with youth lasted approximately fifteen minutes each yielding a total interview time of 2,880 minutes. The sample was consisted of more females (64%; n = 130) than males (36%; n = 73); however, the racial demographics were highly representative of the community. The youth sample was 79.3% Caucasian (n = 161) and 16.8% African American (n = 34), with 3.9% (n = 8) representing other racial categories. The U.S. Census Bureau (2009) indicates that the communities in this study are 82.8% Caucasian and 15.5% African American.

Interview questions were framed through the five social ecological levels of influence, since determining multi-level factors that influence youth substance use allowed for a more thorough assessment. Interviews consisted of the following questions with additional questions that arose from discussion:

1. Can you describe the life of youth in this county? What are the main issues affecting health and quality of life among youth?

2. Intrapersonal--How do youth attitudes and beliefs affect their use or non-use of drugs?

3. Interpersonal--What role do family relationships play in the youth drug use or non-use? What role do peer relationships play in the youth drug use or non-use?

4. Institutional--Are you aware of any local school rules, formal or informal, that may impact youth drug use or non-use? If so, what impact do these rules have?

5. Community--How does this community impact youth drug use or non-use? Are there any social beliefs about drug use that may impact youth?

6. Policy--Are you aware of any local or state laws or policies that impact youth drug use or non-use? If so, what impact do these rules have?

To analyze participant responses, a generalized framework approach was used including data familiarization, frame identification, coding, and interpretation (Pope, Ziebland, & Mays, 2000). The three primary researchers of this project individually reviewed interview responses and coded data into frames based on the five levels of social ecological levels of influence. Although interview questions were specifically asked for each level, participant responses did overlap into other levels, therefore data coding was necessary. Independent data reviews and multiple coding by each researcher were conducted to increase inter-rater reliability (Barbour, 2001). A series of meetings were then held by the field research team in order to discuss interview results, organize findings, and collate data results. In these meetings, common coded responses among participants were discussed. The results of the data analysis relied heavily upon the common responses provided by participants, thereby allowing the researchers to infer with confidence the issues facing the communities in this study. All sixteen adult stakeholders identified drug use as an issue affecting youth, while the majority of youth interviewed identified it as well. The needs assessment results section below provides examples of common responses in each social ecological frame.

In addition to the interview data, state proxy data and local school surveys were used to determine drug use rates and related health outcomes. As part a school health program being offered in the two counties of interest, three local schools administered the California Student Survey (CSS). A cluster sample of students (n = 649) from three area middle schools completed the CSS; however, the researchers were only allowed access to aggregate data. Because of this, inferential statistics were unavailable. Results of both the social and epidemiological diagnoses were correlated to complete the needs assessment.



When asked about attitudes and beliefs about drugs, adult participants frequently stated that youth were participating in drug behaviors because the perceived risk was low. Adults felt that the reason for this low perceived risk of drug use was due to having been around drug use by adults and viewing their future as limited to the careers of their parents. Interestingly, many youth reported a similar perception. Both adult and youth participants referred to an attitude among youth that researchers called the myopic mindframe. This refers to the belief that life will always take place with the borders of the rural community. Many youth participants (even those as young as middle school age) reported that they already knew that they would follow the same occupational path as one of their parents. This isn't inherently negative; however, it does limit youth's perceptions of pursuing higher education or other opportunities outside of the local community. Although, college attendance and career development are available to youth in the area, it was clear that many share a very limited foresight about life after high school. According to the U.S. Census Bureau (2009), 21.6% of Missouri state residents complete a Bachelor's degree or higher. In the two counties assessed in this study, those percentages are significantly lower: 9.6% and 10.6%. Even rates of high school completion are lower in the studied areas. According to the census, the Missouri state high school graduation rate is 81.3% compared to 61.1% and 72.9% in the respective counties in this study. Through interviews with youth in the communities, it was discerned that many youth do not view college or vocational training as a viable post-high school option. Common reasons for this included: do not want to leave the area; area does not have jobs for professionals; do not see the benefit in college; can make money by other means (including sale of drugs); will do/live/work the same as parents. It is possible that the myopic mindframe increases the likelihood of drug use as some youth do not value education or personal growth. School bonding and connectedness has been indicated as a primary protective factor against drug use and abuse (Arthur, Hawkins, Pollard, Catalano, & Baglioni, 2002; Chatterji, 2006; Hawkins, Catalano, & Miller, 1992; Risk and protective, 2002; Sales, Sambrano, Springer, & Turner, 2003; School connectedness, 2010).


The most consistent theme emerging from the interpersonal question was the lack of positive parental involvement in the lives of youth. Researchers were told on numerous occasions that many youth in the area have little to no parental supervision and/ or control. The phrases "kids raising themselves" and "kids raising kids" dominated responses of both adults and youth participants. According to the CSS data, 33.2% of middle school children in the area reported having no adult supervision for at least one hour after school during three or more days of the week.

Another potential interpersonal influence on youth drug use may be negative role-modeling of adults. Many youth participants indicated that their parents/guardians used alcohol, tobacco, or other drugs. It was even noted that some suggested parental provision of alcohol and other drugs. This was corroborated through responses from adult stakeholders who suggested that many local parents take pride in hosing alcohol parties for youth. CSS data supported this finding by indicating that 53.1% of youth know many/most adults who use cigarettes; 55.7% use alcohol; 12.3% use marijuana, 7.8% cocaine/crack, and 6.3% use methamphetamine.


The assessment yielded very few common themes related to institutional influences. Since the behavior of interest was youth drug use, questions were framed using the school as the primary institution with regulatory control. Although some youth participants did state that they were aware of drug use on school grounds, this was not a common subject. The one consistent response from both adults and youth was that schools may be able to provide safe environments during after-school hours through the addition of more recreational activities; however, adult stakeholders were quick to identify the lack of resources as a significant barrier.


Cultural acceptance and continuance of negative drug use behaviors was identified as a primary influencing factor. It was consistently reported by the sample that risky behaviors were part of life or part of the culture in the area. Comments such as "kids being kids" and "that's just what they do around here" were repeated multiple times in reference to risky behaviors such as youth alcohol use, illicit drug use, and related behaviors such as unsafe sexual practices. Surprisingly, even some adult stakeholders were accepting of alcohol and drug use as a rite of passage for youth. This level of acceptance may reflect the permissiveness and apathetic nature of many local adults, which enables and encourages youth participation in risky behaviors. Adults allowing behaviors to continue because it is "part of life around here" perpetuates the social norm of risky behaviors.

A second common theme emerged in the community level of influence. The lack of recreational opportunities and employment, both immediate and future, were noted repeatedly by both adults and youth participants. A common response among youth was that drug and alcohol use was a product of boredom, which supports previous research on youth in other areas (McIntosh, MacDonald, & McKegany, 2003; McIntosh, MacDonald, & McKegany, 2006; McIntosh, MacDonald, & McKegany, 2008). The lack of viable employment and recreation may lead to participation in risky behaviors. Some community members reported that the selling of drugs was an easy way to make money when there were few jobs in the area.


The primary environmental influence of note was the availability of substances to youth in the area. Many participants, both adult and youth, reported that participation in risky behaviors such as drug use was relatively easy for people in the area. Some reasons for this included easy access to alcohol and drugs, availability of substances, and policy or law avoidance. CSS data supported the theme of easy access to substances with 37.3% of youth reporting that alcohol was easy to obtain and 20.6% reporting that marijuana was easy to obtain. When asked how the received access to alcohol, 26.6% of youth obtained it from parties, 25.6% obtained it at home, 23.1% obtained it from other youth, and 20.7% obtained it from adults at a friend's home. Although laws prohibit alcohol and other drug use, both adult and youth interview participants reported that law enforcement generally ignore such use "as long as there are no major problems."

Another interesting result learned from youth interviews was that of substance use for law avoidance. In 2005, the state of Missouri instituted a minor-in-possession (MIP) law which mandated stricter penalties for alcohol consumption and/or intoxication for youth under age 21. A large majority of youth participants indicated that to avoid MIP penalties, many youth are replacing alcohol use with the recreational use of prescription drugs. Although CSS data did not provide a comparison, the 2010 Missouri Student Survey reported that youth in the southeast region of Missouri showed higher rates of 30-day prescription drug use (8.1% vs. 6.3%-6.7%) than other regions in the state (Evans, Sale, Breejen, & Dupue, 2010).


As a result of this study, a concept diagram of rural youth social ecological influences has been developed (Figure 1). Indicators suggest that youth drug use, particularly in rural areas, continues to increase potentially impacting personal and community health outcomes for years. This study demonstrates the process of completing a social and epidemiological assessment in rural community where resources are limited and youth drug rates are high. Using the social ecological model to frame the assessments allows health educators to identify particular influential constructs of behavior. Although drug education programs exist in the community, there seems to be a prevailing thought of community acceptance of drug use. Rural youth perceive that drug use is an acceptable norm among other youth, as well as adults in the community. The norm seems to perpetuate from the adult generation to younger children leading to an increased view of acceptance. Myopic views of life coupled with the perceived boredom in rural areas contribute to the perception of drug use as an alternative to healthy recreation. The limited outlook on education and career possibilities is also an influential factor in drug use among rural youth.


Health educators are in a unique position to assist those communities in assessing the influential factors related to drug behaviors among youth. The diagnosis of behavioral influences assists in the development of appropriate prevention and intervention strategies. It is recommended that health educators working in rural settings utilize the social ecology model to assist in the assessment process.


This study is limited in its application to rural communities outside of those examined; however, the process may provide valuable insights to health educators working to assess rural communities with limited resources. Sampling procedures provide another limitation. Although a non-probability sample was used for youth interviews, this process reflects an actual indication of the limited access that health educators may have while assessing rural communities, specifically schools. While the researchers would have preferred access to raw data from the CSS instrument, only aggregate data was available. This limitation reflects the lack of assessment and analysis expertise among local schools in the rural communities of study. Raw data was not available because school officials did not perceive it to be important. Although these limitations exist, they are notable in that they could represent real barriers to assessment design for rural health educators. This study highlights how these barriers can be overcome to create a social and epidemiological assessment of drug use among rural youth.


Arthur, M. W., Hawkins, J. D., Pollard, J., Catalano, R. F., & Baglioni, A. J. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities That Care Youth Survey. Evaluation Review, 26, 575-601.

Barbour, R. S. (2001). Checklists for improving rigour in qualitative research: A case of the tail wagging the dog? British Medical Journal, 322, 1115-1117.

Baskin-Sommers, A. & Sommers, I. (2006). The co-occurrence of substance use and high-risk behaviors. Journal of Adolescent Health, 38, 609-611.

Benotsch, E. G., Koester, S., Luckman, D., Martin, A., & Cejka, A. (2011). Non-medical use of prescription drugs and sexual risk behavior in young adults. Addictive Behaviors, 36, 152-155.

Brown, S. A., McGue, M., Maggs, J., Shulenberg, J., Hingson, R., Swartzwelder, S., et al. (2009).Underage alcohol use: Summary of developmental processes and mechanisms: Ages 16-20. Alcohol Research & Health, 32, 41-52.

Chatterji, P. (2006). Illicit drug use and educational attainment. Health Economics, 15, 489-511.

Ennett, S. T., Foshee, V. A., Bauman, K. E., Hussong, A., Cai, L., Reyes, H., et al. (2008). The social ecology of adolescent alcohol misuse. Child Development, 79, 1777-1791.

Evans, C. J., Sale, L., Breejen, K. M., & Dupue, S. (2010). Missouri Student Survey 2010. Retrieved January 31, 2011 from

Evans, C., Sale, E., Karamehic, A., Breejen, K., Witworth, A., & McCudden, S. (2006). 2006 Missouri Student Survey. Retrieved August 15, 2008 from MSS%20Final%20Rpt%20Printer%2001-23-07.pdf.

Gamm, L., Hutchison, L., Bellamy, G., & Dabney, B. (2002). Rural Healthy People 2010: Identifying rural health priorities and models for practice. Journal of Rural Health, 18, 9-14.

Hall, W., Room, R., & Bondy, S. (1998). Comparing the health and psychological effects Of alcohol, cannabis, nicotine and opiate use. In W. Corrigall, W. Hall, H. Kalant, & R. Smart (Eds.), Cannabis and health. Toronto: Addiction Research Foundation.

Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64-105.

Kumpfer, K. & Turner, C. (1991). The Social Ecology Model of adolescent substance abuse: Implications for prevention. Substance Use and Misuse, 25, 435-463.

Mason, M. J., Mennis, J., Coatsworth, J. D., Valente,T. W., Lawrence, F., & Pate, P. (2009). The relationship of place to substance use and perceptions of risk and safety in urban adolescents. Journal of Environmental Psychology, 29, 485-492.

Mason, M. J., Valente,T. W., Coatsworth, J. D., Mennis, J., Lawrence, F., & Zelenak, P. (2010). Place-based social network quality and correlates of substance use among urban adolescents. Journal of Adolescence, 33, 419-427.

McIntosh, J., MacDonald, F., & McKeganey, N. (2003). The initial use of drugs in a sample of pre-teenage schoolchildren: The role of choice, pressure and influence. Drugs: Education, Prevention and Policy, 10, 147-158.

McIntosh, J., MacDonald, F., & McKeganey, N. (2006). Why do children experiment with illegal drugs? The declining role of peer pressure with increasing age. Addiction Research and Theory, 14, 275-287.

McIntosh, J., MacDonald, F., & McKeganey, N. (2008). Pre-teen children's experiences with alcohol. Children and Society, 22, 3-15.

McLeroy, K., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education Quarterly, 15, 351-377.

National Advisory Committee on Rural Health and Human Services. (2009). The 2009 Report to the Secretary: Rural Health and Human Service Issues. Retrieved February 28, 2011 from http://www.hrsa. gov/advisorycommittees/rural/2009secreport.pdf.

National Rural Health Research Center Director's Meeting. (2001). Research Opportunities for Rural Health Research Centers and State Offices of Rural Health. Washington, D.C.

No Place to Hide: Substance Abuse in Mid-Size Cities and Rural America. (2000). New York Columbia University, National Center on Addiction and Substance Abuse.

Pemberton, M., Colliver, J., Robbins, T., & Gfroerer, J. (2008). Underage Alcohol Use: Findings from the 2002-2006 National Surveys on Drug Use and Health. Rockville, MD: Substance Abuse and Mental Health Services Administration, Of-fice of Applied Studies. Retrieved August 11, 2008 from http://

Pope, C., Ziebland, S., & Mays, N. (2000). Analysing qualitative data. British Medical Journal, 320, 114-116.

Riner, M., & Saywell, R. (2002). Development of the social ecology model of adolescent interpersonal violence prevention (SEMAIVP). Journal of School Health, 72, 65-70.

Risk and protective factors in substance abuse prevention. (2002). NIDA Notes, 16. Retrieved January 31, 2011 from

Sales, E., Sambrano, S., Springer, F. J., & Turner, C. (2003). Risk, protection, and substance use in adolescents: A multi-site model. Journal of Drug Education, 33, 91-105.

School connectedness: Strategies for increasing protective factors among youth (2010). Reclaiming Children & Youth, 19, 21-24.

Stephens, J. W. (2001). The social ecology of the co-occurrence of substance use and early coitus among poor, urban black female adolescents. Substance Use and Misuse, 36, 421-446.

U.S. Census Bureau (2009). State and County QuickFacts. Retrieved May 19, 2009 from http://quickfacts.

U.S. Substance Abuse and Mental Health Services Administration. (2008). Results from the 2007 National Survey on Drug Use and Health: National Findings. (Office of Applied Studies, NSDUH Series H-34, HHS Publication No. SMA 08-4343 Findings). Rockville, MD.

U.S. Substance Abuse and Mental Health Services Administration. (2010). Results from the 2009 National Survey on Drug Use and Health: Volume I. Summary of National Findings (Office of Applied Studies, NSDUH Series H-38A, HHS Publication No. SMA 10-4586 Findings). Rockville, MD.

Van Gundy, K. (2006). Substance Abuse in Rural and Small Town America. Retrieved August 2, 2008 from SubstanceAbuse.pdf

Williams, R., Perko, M., Belcher, D., Leaver-Dunn, D., Usdan, S., & Leeper, J. (2006). Use of Social Ecology Model to address alcohol use among college athletes. American Journal of Health Studies, 21, 228-237.

Williams, R., Perko, M., Usdan, S., Leeper, J., Belcher, D., & Leaver-Dunn, D. (2008). Influences on alcohol use among NCAA Athletes: Application of the Social Ecology Model. American Journal of Health Studies, 23, 151-159.

Wright, D. & Sathe, N. (2005). State Estimates of Substance Use from the 2002-2003 National Surveys on Drug Use and Health. DHHS Publication No. SMA 05-3989, NSDUH Series H-26. Rockville, MD: Substance Abuse and Mental Health Services Administration, Of-fice of Applied Studies.

Ronald D. Williams, Jr., PhD, CHES, is affiliated with the Department of Food Science, Nutrition, and Health Promotion, Mississippi State University, Jeremy T. Barnes, PhD, is affiliated with the Department of Health, Human Performance, and Recreation, Southeast Missouri State University, Edward Leoni, ReD, is affiliated with the Department of Health, Human Performance, and Recreation, Southeast Missouri State University. Corresponding Author: Ronald D. Williams, Jr., PhD, CHES, Mississippi State University Department of Food Science, Nutrition, and Health Promotion, Box 9805, Mississippi State University, MS 39762, phone: 662-325-0401, Email:

Competency B: Collect health-related data Sub-competencies:

1. Use appropriate data-gathering instruments

2. Apply survey techniques to acquire health data

3. Conduct health-related needs assessments

4. Implement appropriate measures to assess capacity for improving
health status

Competency C: Distinguish between behaviors that foster and hinder
well-being Sub-competencies:

1. Identify diverse factors that influence health behaviors

2. Identify behaviors that tend to promote or comprise health

Competency F: Infer needs for health education from obtained data

1. Analyze needs assessment data

Table 1. Five levels of social ecological influence on behavior
(McLeroy, et al, 1988).

Intrapersonal Factors     Characteristics include personal
                          knowledge, attitudes, and beliefs
                          concerning particular behaviors;
                          Issues of personal skill and
Interpersonal Factors     Social networks including family,
                          friends, and work groups
Organizational/           Social practices with
  Institutional Factors   organizational characteristics
                          including the formal and informal
                          rules and regulations for operation
                          within the particular institution;
                          Organizational norms and changes
                          of those norms can affect behavior
                          of those individuals involved
Community Factors         Relationships among organizations,
                          institutions, and informal networks
                          within defined boundaries; Includes
                          the social standards or norms that
                          exist within the community.
Policy Factors            Policies and laws that are designed
                          to protect the health of a
                          community; Polices for health
                          protection include regulations for
                          healthy actions, disease
                          prevention, and disease control
Gale Copyright: Copyright 2011 Gale, Cengage Learning. All rights reserved.