Essay Instructions: a. What is your search strategy?
b. Why is this problem important to address?
c Conduct an on-site community assessment with the group facilitator. Describe how community assessment was conducted ? walking survey etc include when (dates) of walking/windshield survey, interviews (& where) etc.
b. Provide a description of the data obtained and the person(s) interviewed who provided the information. You do not have to provide a verbatim script of the interview but rather the content you receive from the interviewee should be embedded in the Community Assessment section where you feel it makes sense. The goal of the interview is to obtain information from the interviewee that you would not be able to find in journal articles, information on the web, etc.
c. What are your other (literature and electronic such as the NYC Community Health Profiles) data sources?
d. If you were to actually conduct a comprehensive community assessment, what data would you want to obtain directly in the community (as opposed to internet, literature, etc.)? What other steps would you would undertake to locate important data about your community? Who else would be your key informants in the community? What questions would you pose to these key informants?
e. Summarize the findings from your data sources that identifies a gap or need to be addressed via a population-focused health intervention project.
the population chosen is a group of college students age 18- 30 at the college of Hostos Community College. I should be adressing that testing BMI knowledge and student's self perceptions of their weigh will help us discover if in fact students are in denial of their overweight/obese state. Or perhaps Americans have began to view obesity as acceptable, and no longer view themselves as they truly are. I discovered last week that 9 out of 10 students she randomly approached believed that they were at a "normal" weight and 1 thought she was overweight. The one student who thought she was overweight was at a normal BMI and the other 9 were overweight. Denial and/or knowledge deficits may play a huge roll in the obesity crisis. please include that there is a wait control program of 6 weeks at the college available 2 times a year. last semester they saw 6 000 students. talked about everything that may help to loose weight . gum may be used when classes are not in session. there is a college fair 2 times a year were the truck comes around and takes people blood pressure, talks about healthy habits and encourages students to Flow Up with their doctors. the down fall of what little they offer according to the Alejandrina Pena RN of Hostos Community college i interviewed the is no kind of Follow Up after recommendations are made to seek medical care or a wellness group. if the students have or don't have insurance or just don't have a doctor they are told to go to the hospital, particularly HHC (New York City Health and Hospitals Corporation). Russel Levine, MS Wellness Specialist at Hostos Community college i interviewed also stated that there is no kind of follow up. they are offering a comprehensive seven week program to guide and support you through a personal journey to better health and weight management, the goal is to help develop a healthy relationship with food, physical activity, and weight in an environment that helps students achieve your academic goals. they give out please address the Mock survey as well. thank you. if any questions please call 3
one of the articles found to this assignment may be helpful :
obesity | VOLUME 19 NUMBER 2 | February 2011 453
nature publishing group short communications
Epidemiology
Young adulthood is a unique developmental period and a time
for excess weight gain (1). During the transition to adulthood,
independence and autonomy increases, and long-term diet
and physical activity patterns may be established. (See Nelson
et al. (1) for a recent review of this literature.) Historically,
however, young adult research has largely focused on students
attending traditional 4-year colleges (1). Little research is available
to quantify weight-related characteristics among young
adults in other settings, such 2-year community and technical
colleges.
These institutions represent more racially diverse and
economically disadvantaged groups than other postsecondary
institutions (2,3), and thus may be an important setting for
intervention.
Overall, effective young adult health promotion strategies
are needed. To inform obesity-related intervention and policy
efforts in postsecondary settings, a better understanding of
weight-related behaviors in these settings is needed. Therefore,
the objective of this study was to examine differences in the
prevalence of overweight/obesity and weight-related behaviors
(e.g., diet, physical activity) among students attending 2-year
vs. 4-year postsecondary institutions, using data drawn from a
large state-wide surveillance system.
Methods
Data were collected in 2007 and 2008. The University of Minnesota's
Boynton Health Service developed a sampling frame including 27
Minnesota college/university campuses (14 two-year college campuses,
13 four-year campuses). These included all campuses surveyed in 2007,
and those surveyed in 2008 that were not included in 2007. Study protocols
were approved by The University of Minnesota Institutional
Review Board.
Random samples of students, including undergraduate and graduate
students (where applicable), were drawn from institutional enrollment
lists. Students received multiple invitations to participate via
postcards and emails. Participants were offered small monetary incentives
and opportunities to win several large prizes. Full study details
1Division of Epidemiology & Community Health, University of Minnesota, Minneapolis, Minnesota, USA; 2Department of Kinesiology & Health Education, University of
Texas at Austin, Austin, Texas, USA; 3Boynton Health Service, University of Minnesota, Minneapolis, Minnesota, USA. Correspondence: Melissa N. Laska
()
Received 17 August 2010; accepted 6 September 2010; published online 21 October 2010. doi:10.1038/oby.2010.262
The Differential Prevalence of Obesity
and Related Behaviors in Two- vs. Four-Year
Colleges
Melissa N. Laska1, Keryn E. Pasch2, Katherine Lust3, Mary Story1 and Ed Ehlinger3
The objective of this study was to determine whether obesity prevalence and weight-related behaviors (e.g., diet,
physical activity) differ among students enrolled in 2-year community/technical colleges and those attending 4-year
colleges/universities. This information could inform the development of intervention strategies. Through an existing
surveillance system of Minnesota postsecondary cation institutions, survey data were collected from 16,539
students from 27 campuses (14 two-year college campuses, 13 four-year college/university campuses; 2007?2008),
including self-reported physical activity, media use, dietary patterns, weight control behaviors, height, and weight.
Unadjusted analyses indicated that students enrolled in 2-year colleges, particularly females, had a higher prevalence
of overweight/obesity, lower levels of physical activity, more television viewing, higher intakes of soda, fast food, and
diet pills compared to students attending 4-year colleges (P < 0.05). Females attending 4-year colleges were more
likely to engage in certain unhealthy weight control behaviors (taking diet pills, binge eating, self-induced vomiting)
compared to females attending 2-year institutions. Among male students there were fewer differences between
2-year and 4-year colleges. Controlling for sociodemographic factors (e.g., race/ethnicity, age), most disparities in
prevalence estimates remained, though many were attenuated. Overall, few young adults engage in weight-related
behaviors consistent with national recommendations. Two-year college students may represent a particularly at-risk
group. Disparities between 2- and 4-year college students exist beyond the sociodemographic differences in these
populations. Effective weight-related interventions are needed for young adults, particularly females attending 2-year
colleges and all males attending postsecondary institutions.
Obesity (2011) 19, 453?456. doi:10.1038/oby.2010.262
454 VOLUME 19 NUMBER 2 | february 2011 | www.obesityjournal.org
short communications
Epidemiology
are available elsewhere (4,5). Overall, 16,656 students completed
surveys (overall response rate: 37.1%; 2-year colleges: 30.4%, 4-year
colleges: 40.7%).
Measures
Gender, age, race/ethnicity, relationship status, number of dependent
children, weekly hours worked for pay, credit card debt, living situation
and health insurance coverage were self-reported. Student status was
defined as currently enrolled in a 2-year community/technical college
or 4-year college.
BMI. BMI (kg/m2) was calculated using self-reported height and
weight. Cut-points of 25 and 30 kg/m2 defined overweight and obesity,
respectively (6). To identify outliers, we examined the log-transformed
BMI distribution. Values <25th percentile minus three times the interquartile
range, and >75th percentile plus three times the interquartile
range, were examined; some values were biologically implausible and
excluded (n = 68) (7).
Physical activity and media. Participants reported the weekly frequency
of vigorous- and moderate-intensity physical activity, strengthening
exercises, television viewing and video/computer gaming (8).
Dietary intake. Diet-related items included adapted YRBSS survey
items (e.g., fruit/vegetable, soda, diet soda consumption) (9).
Participants
reported past week breakfast frequency (8). Two questions
assessed usual frequency of eating at fast food and other restaurants.
Weight control. Participants reported currently trying to lose weight,
and past year frequency of engaging in unhealthy behaviors (e.g., using
laxatives, diet pills) (10).
Analyses
Cross-sectional, gender-stratified mixed-effects regression analyses
were conducted to assess associations between 2-year vs. 4-year college
enrollment and weight-related behaviors. Schools were specified as the
nested random effect in the model (11,12).
Participants were excluded if missing gender (n = 6) or age (n = 85),
and/or having reported an age that was likely erroneous (n = 11). Transgendered
individuals were also excluded (n = 15). The final sample size
was 16,539. The missingness of key outcome variables ranged from 0.3%
(television viewing) to 1.3% (BMI). Observations with missing values
were excluded from individual analyses; thus sample sizes varied slightly
between models. Analyses were conducted using SAS v.9.1 (SAS Institute,
Cary, NC, 2001).
Results
Overall, 34.7% of participants were male, and 86.0% was white,
2.6% African-American or black, 5.2% Asian/Pacific Islander,
1.1% Latino/Hispanic, 0.8% American Indian, and 3.0% other/
mixed race (1.5% were missing data on race). Respondents
from 2-year colleges (compared to those from 4-year colleges)
were more likely to be married/have a domestic partner (28.3%
vs. 16.6%, respectively), older (27.0 ? 0.8 vs. 24.3 ? 0.8 years,
interquartile range: 20?31 years vs. 20?25 years), have one
dependent child (12.7% vs. 5.3%) or two or more dependent
children (17.9% vs. 8.0%), work 20?39 h/week (35.0% vs.
22.0%) or ≥40 h/week (21.3% vs. 14.9%) for pay, have more
credit card debt ($1,327 vs. $876), and were less likely to be
Asian/Pacific Islander (2.6% vs. 4.9%), live in a residence hall,
fraternity or sorority (1.2% vs. 28.1%), and have health insurance
(84.1% vs. 89.2%).
Across all types of students, on average, many did not meet
national health recommendations (Table 1). In unadjusted
analyses, females attending 2-year colleges had a higher
prevalence of overweight and obesity, lower levels of physical
activity, and more television viewing, soda consumption,
fast food consumption,
and diet pill use, compared to 4-year
college
students
(P < 0.05). Females attending 4-year colleges
were more likely to engage in some unhealthy weight control
behaviors
(laxative use, binge eating, induced vomiting) and
video gaming. There were fewer differences among males; in
unadjusted analyses, males attending 2-year colleges reported
less strenuous
activity and more soda and fast food, compared
to males attending 4-year colleges (P < 0.05).
Adjusted models were conducted to assess these differences
independent of age, race/ethnicity, relationship status, dependent
children, hours/week worked for pay, credit card debt, living
situation, and health insurance (Table 1). Despite adjusting for
these covariates, most relationships remained significant; those
that were no longer significant were: strength training, diet soda
consumption, and taking
diet pills among females only, as well
as fast food consumption among both males and females.
Discussion
Our findings highlight differences in weight status, physical
activity, media use, dietary intake, and weight control behaviors
between students attending 2-year vs. 4-year colleges,
particularly
women. In general, females attending 2-year colleges
exhibited
less healthful dietary and physical activity patterns than those
attending 4-year colleges. There were fewer differences among
males, although males attending 2-year colleges
were less likely
to engage in strenuous physical activity and more likely to consume
soda and fast food. Interestingly, a majority of these differences
between 2- and 4-year students were evident even after
controlling for numerous
sociodemographic factors.
Unadjusted estimates are important in assessing which
young adult subgroups are engaging in the least healthful
weight practices and are most in need of health promotion
efforts. In contrast, adjusted estimates allow us to estimate
these disparities independent of various sociodemographic
factors that may confound this relationship. Although 2-year
college students
clearly exhibit less healthful lifestyle patterns,
it is important to understand whether this difference is because
2-year colleges better represent racial minorities and low-
income
groups, who are at greater risk for obesity, or whether
there are other factors at work in lives of these young adults that
are not wholly explained by sociodemographic characteristics.
Given that many of the associations between student status
and weight behaviors remained significant in adjusted models,
these findings suggest that there are other lifestyle factors that
need further exploration in future research. Little research to
date has explored the modifiable determinants and contextual
characteristics that influence young adult weight status; such
work may be critical in understanding how we can effectively
intervene
with these groups.
Recently, we also explored disparities in young adult dietary
patterns and found results similar to that of our current study
(13). Findings from our previous study indicated that 18?23-
year-old study participants who were not college students
or
were enrolled in 2-year colleges had poorer dietary intakes
obesity | VOLUME 19 NUMBER 2 | February 2011 455
short communications
Epidemiology
than those attending 4-year colleges. Our current study
augments
this work by exploring a more comprehensive range
of weight-related behaviors and outcomes. Although there has
been recent scientific interest in cational disparities among
other young adult health behaviors (14,15), to our knowledge
there are no recent studies that have examined a wide range of
weight-related behaviors across these populations.
Although this study is among the first of its kind and includes
data from a large, diverse sample, it has several weaknesses.
Our sample was drawn from one geographic region, which may
limit generalizability. Self-reported measures are also subject to
error and reporting bias. Our survey response rate was 37.1%
(30.4% among 2-year colleges; 40.7 among 4-year colleges);
thus our study may be subject to selection bias. Logistical challenges
with diverse young adult populations make it difficult
to achieve higher response rates; nonetheless, this remains a
concern. Other limitations include a lack of data on access
to physical activity facilities, multiple dimensions of socioeconomic
status (beyond weekly hours worked for pay and
credit card debt), and available leisure time. Finally, although
postsecondary institutional settings may provide an important
framework for the delivery of health promotion interventions,
Table 1 Prevalence estimates for weight-related behaviors and health outcomes among students attending 2- and 4-year
Minnesota postsecondary institutions (2007?2008)
Females (n = 10,804) Males (n = 5,735)
2-year:
n = 3,866,
%
4-year:
n = 6,938,
%
P value
(crude)
P value
(adjusted)a
2-year:
n = 1,713,
%
4-year:
n = 4,022,
%
P value
(crude)
P value
(adjusted)a
Weight status
Overweight
(BMI ≥ 25 kg/m2)
46.1 37.4 0.002 0.05 55.1 51.2 0.26 0.80
Obese (BMI ≥ 30 kg/m2) 22.4 15.2 0.0004 0.01 20.9 17.5 0.16 0.34
Physical activity and sedentary behaviors
No strenuous exercise in
the past week
39.8 31.1 0.0006 0.01 30.7 23.8 0.002 0.02
<2 h/week of moderateintensity
physical activity
66.5 60.3 0.001 0.02 62.7 59.0 0.13 0.49
No strengthening
exercises in the past week
42.4 36.9 0.001 0.15 34.8 32.1 0.18 0.80
≥2 h/day of TV viewing 52.9 46.9 0.03 0.04 56.4 51.4 0.11 0.23
≥2 h/day of Video gaming 21.0 33.8 <0.0001 0.02 40.4 46.6 0.08 0.99
Dietary behaviors
<5 Servings/day of fruits or
vegetables
87.7 86.2 0.17 0.44 90.0 88.3 0.11 0.97
≥1 Regular soda/day 18.4 12.1 0.0007 0.0008 34.4 26.5 0.002 0.0001
≥1 Diet soda/day 20.8 17.8 0.05 0.97 16.1 13.6 0.14 0.84
Eating breakfast <5 days/
week
55.2 54.3 0.78 0.14 64.3 67.2 0.35 0.62
Eating fast food several
times per week or more
16.0 11.4 0.006 0.06 26.6 21.0 0.002 0.06
Eating at other restaurants
several times per week or
more
10.6 10.4 0.87 0.19 13.1 13.7 0.75 0.08
Weight intentions and weight control
Trying to lose weight 68.9 67.5 0.31 0.84 42.1 41.6 0.80 0.64
Using laxatives to control
weightb
3.1 2.4 0.08 0.46 1.4 1.2 0.78 0.40
Taking diet pillsb 7.8 5.7 0.009 0.18 2.5 3.4 0.15 0.07
Binge eatingb 15.0 17.7 0.004 0.002 9.7 10.4 0.44 0.15
Inducing vomiting to
control weightb
3.3 4.5 0.003 0.01 0.9 1.1 0.47 0.87
Note: Sample sizes for individual analyses vary slightly due to a small degree of missing data.
aModels adjusted for age, race/ethnicity, relationship status, number of dependent children, weekly hours worked for pay, credit card debt, living situation (i.e., living
on-campus or off-campus), and health insurance (yes/no). bParticipants reported engaging in any of these unhealthy weight control behaviors a few times per year
or more.
456 VOLUME 19 NUMBER 2 | february 2011 | www.obesityjournal.org
short communications
Epidemiology
it is important to note that a significant proportion of high-risk
youth at this age may not enroll in colleges and universities.
Additional research is needed to understand lifestyle factors
among young adults not attending college.
Overall, these findings suggest important differences in
obesity
and weight behaviors between 2-year and 4-year college
students, particularly females. In part, these disparities may be
attributed to factors beyond basic sociodemographic differences
in these populations. Despite the disparities, however,
few young people overall are engaging in healthy lifestyles.
Effective health promotion efforts are needed for all young
adults.
Acknowledgments
Funding for data collection was provided by BlueCross BlueShield of
Minnesota, the University of Minnesota, and Minnesota State Colleges
and Universities, as well as other participating colleges and universities.
Additional salary support for the analysis of these data was provided by the
National Cancer Institute (award #K07CA126837; PI: M.N.L.). The content
of this manuscript is solely the responsibility of the authors and does not
necessarily represent the official views of the National Cancer Institute or
the National Institutes of Health.
Disclosure
The authors declared no conflict of interest.
? 2010 The Obesity Society
REFERENCES
1. Nelson MC, Story M, Larson NI, Neumark-Sztainer D, Lytle LA. Emerging
adulthood and college-aged youth: an overlooked age for weight-related
behavior change. Obesity (Silver Spring) 2008;16:2205?2211.
2. U.S. Department of Education, National Center for Educaion Statistics,
2003?04 National Postsecondary Student Aid Study (NPSAS:04).
3. Adelman C. Moving into Town-and Moving on: The Community College
in the Lives of Traditional-age Students. U.S. Department of Education:
Washington, DC, 2005.
4. VanKim NA, Laska MN, Ehlinger E, Lust K, Story M. Understanding young
adult physical activity, alcohol and tobacco use in community colleges
and 4-year post-secondary institutions: A cross-sectional analysis of
epidemiological surveillance data. BMC Public Health 2010;10:208.
5. Widome R, Laska MN, Gulden A, Fu SS, Lust K. Health risk behaviors
among veterans of the Afghanistan and Iraq wars attending college: do they
differ from that of their non-veteran peers? Am J Health Promot, in press.
6. Physical Status: The use and interpretation of anthropometry. WHO
Technical Report Series 854. 1995. World Heath Organization: Geneva,
Switzerland
7. Iglewica B, Hoaglin D. How to detect and handle outliers. In: Mykytka E,
(ed). The ASQ Basic References in Quality Control: Statistical Techniques.
American Society for Auality, Statistics Division: Milwaukee, WI, 1993.
8. Nelson M, Lust K, Story M, Ehlinger E. Associations between credit card
debt, stress, and leading health risk behaviors among college students.
Am J Health Promot 2008;22:400?407.
9. Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance
System. (2009).
10. Laska MN, Pasch KE, Lust K, Story M, Ehlinger E. Latent class analysis
of lifestyle characteristics and health risk behaviors among college youth.
Prev Sci 2009;10:376?386.
11. Murray D. Design and Analysis of Group-Randomized Trials. Oxford
University Press: New York, 1998.
12. Raudenbush S, Bryk A. Hierarchical Linear Models: Applications and Data
Analysis Methods. Sage Publications: Thousand Oaks, 2002.
13. Nelson MC, Larson NI, Barr-Anderson D, Neumark-Sztainer D, Story M.
Disparities in dietary intake, meal patterning, and home food environments
among young adult nonstudents and 2- and 4-year college students. Am J
Public Health 2009;99:1216?1219.
14. Solberg LI, Asche SE, Boyle R, McCarty MC, Thoele MJ. Smoking
and cessation behaviors among young adults of various cational
backgrounds. Am J Public Health 2007;97:1421?1426.
15. Paschall MJ, Freisthler B. Does heavy drinking affect academic performance
in college? Findings from a prospective study of high achievers. J Stud
Alcohol 2003;64:515?519.
Thank you
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