Obesity is a major public health threat in the United States.1 It is linked to many chronic diseases, including diabetes, coronary heart disease, stroke, hypertension, and certain types of cancer, and is expected to account for $147 billion in annual costs.2 According to the Centers for Disease Control and Prevention, 34.9% of all Mississippians are classified as obese—the single highest obesity rate in the nation.3,4 Obesity is caused by various factors including unhealthy eating5 and inactivity behaviors6; these behaviors can be influenced by characteristics of the environment (eg, aesthetics of a neighborhood, grocery store access).7–9
Laws, regulations, and guidelines as public health policy can profoundly impact health behavior and status.10 Numerous policy interventions have been identified as effective in promoting physical activity, healthy eating, and obesity prevention.11–22 In addition to nationally implemented policies, mounting evidence shows that implementation at the local level is critical to achieving changes in risk factors and health outcomes.10,23–25 A 2009 Institute of Medicine report describes the importance of local government action in obesity prevention and lays out policy recommendations likely to directly affect children.24
When making decisions about supporting an issue, policy makers often prioritize constituents' needs or opinions above other factors.26 Further, the Institute of Medicine report, Preventing Childhood Obesity: Health in the Balance,27 emphasized that policy changes occur more quickly “when there is strong social consensus behind them.”28,29 It is therefore important to provide timely data showing public support of evidence-based approaches. Obtaining information on public health topics, however, requires a large and diverse sample of respondents to provide such information. Most public health data collection assesses factors, such as health status and socioeconomic and demographic data, but misses the information that may be of particular importance to influence policy maker priorities. There is a lack of public opinion data, particularly at the state and local levels, to help local-level decision makers tailor policies that would most efficiently and effectively impact obesity rates. The lack of data available to advocates also hampers efforts to communicate constituent concerns to policy makers. Peer-reviewed studies reporting on such perceptions are needed to help shape such communication efforts. Putting these studies through peer review and making them available in the scientific literature (not just the popular press literature) enhances the opportunity for researchers and policy makers to make use of these findings. This work aims to fill that gap by reporting the opinions of a random sample of residents from 10 counties in Mississippi.
The overall goal of this study was to determine the public support for policies related to nutrition and physical activity through administration of a phone survey in 10 counties in Mississippi. The study protocol has been reviewed and approved by the Washington University Institutional Review Board.
Telephone interviews with adult respondents were conducted using a random sample of 75% landline and 25% cell phone numbers from residents living in the following 10 counties in Mississippi: Holmes, Humphreys, Jefferson, Tunica, Wilkinson, Harrison, Hancock, Rankin, Tishomingo, and Prentiss. The first 5 counties represent those with the highest adult obesity prevalence in the state and the last 5 counties represent those with the lowest adult obesity prevalence based on County Health Rankings. The County Health Rankings are published online at www.countyhealthrankings.org by the University of Wisconsin Population Health Institute and the Robert Wood Johnson Foundation. The Rankings assess the overall health of nearly every county in all 50 states, using a standard way to measure how healthy people are and how long they live.30 At the time the study was designed, these rankings were based on Centers for Disease Control and Prevention data from 2006 to 2008.31 The data were collected from May through September 2011 by the Survey Research Laboratory, Social Science Research Center at Mississippi State University. The household telephone numbers were selected using random-digit-dialing sampling procedures. The sample included households with unlisted numbers. Quotas for the total number of completed surveys to be obtained from each county were predetermined according to county population size. Within-household random sampling was conducted by asking to speak with a household member who is at least 18 years of age and ... [randomly] ... “will be having the next birthday” [or] ... “has had the last birthday.” Telephone numbers were dialed a maximum of 8 times before being retired. Calls were placed throughout Mondays to Fridays from 5 PM to 9 PM, Saturdays from 10 AM to 6 PM, and Sundays from 1 PM to 9 PM, without randomization for when the calls were placed. A total of 81 000 telephone numbers were purchased; 36 182 were never used and 21 138 were not home or were not working phone numbers. Of the remaining 24 480 numbers (which includes answering machines and busy signals as well as ineligibilities and refusals), 2800 individuals responded (response rate 11%).
A series of 10 questions used a 4-point scale to assess how strongly respondents would “favor or oppose” new laws or policies (referred to as “healthy eating and activity policies”). An additional 4 questions asked whether respondents would support the use of local government funds to build and maintain (a) walking trails, (b) swimming pools, (c) recreation centers, and (d) bicycle paths (referred to as “local funding”). The instrument is available for download as supplementary material.
Support for each policy was examined on a univariate basis and then stratified by a number of demographic characteristics (ie, age, race, marital status, education, income, presence of children in the home, gender, high/low obesity county, and body mass index [BMI]). The Mantel-Haenszel χ2 values across levels of these characteristics were determined. Summary scores were computed by summing the responses to these questions for a possible score of 10 to 40 for support of healthy eating and activity policies (where 10 is strongly supporting each policy and 40 is strongly opposing all) and 4 to 8 for local funding policies (where 4 is answering “yes” to each policy and 8 is answering “no” to all). These scores were dichotomized to distinguish those supporting and those opposing policies (10-24 vs 25-40 for healthy eating and activity policies and 4-5 vs 6-8 for local funding policies). Logistic regression models were used to explore the association between supporting/opposing the policies and demographic characteristics (ie, gender, race/ethnicity, age, income, presence of children in the home, level of obesity in the county, individual weight status, and education). These models were run a second time with adjustment for all other demographic characteristics.
The final sample contained 2800 individuals, roughly half (1401) of whom were from the 5 counties with the lowest obesity rates in Mississippi. The sample was 71% female (2000). Fifty-one percent of the sample (1432) was white, 45% (1263) was black or African American, and nearly 4% (105) was some other race. Additional demographic characteristics of the sample can be found in Table 1, with additional demographic information on Supplemental Digital Content Table 1 available at: http://links.lww.com/JPHMP/A19.
Among the total sample, support for healthy eating and activity policies was highest for the following: requiring at least 30 minutes of physical activity or physical education classes every day for children in kindergarten through 12th grade (93%) and lowest for the following: taxing soda and soft drinks and using the money for public education campaigns to fight obesity in children (65%). Support for local funding policies was highest for recreation centers (86%) and lowest for swimming pools (74%) (Table 2).
The levels of support for each policy, stratified by race, obesity level of the county, and gender, are shown in Table 2. Support for nearly every policy was greater among African Americans, females, and those living in counties with higher levels of obesity. In general, the trends across BMI, education, income, and age were not linear (data not shown), in that support did not increase or decrease consistently across categories.
For the sum of all 10 healthy eating and activity policies, summary scores ranged from 10 to 40, with a mean of 17.1 (SD = 6.0) and for local funding the sum of the 4 items ranged from 4 to 8, with a mean of 4.7 (SD = 1.3). Females were significantly more likely than males to support both types of policies (odds ratio [OR] = 1.69, 95% CI: 1.28-2.24 and OR = 1.30, 95% CI: 1.06-1.60) (Table 3). When this model was adjusted for all demographic factors listed in the methods, the association was no longer significant for the healthy eating and activity policies (adjusted OR = 1.42, 95% CI: 0.98-2.06) but remained borderline significant for the local funding policies (adjusted OR = 1.39, 95% CI: 1.04-1.85). Compared with whites, blacks/African Americans were significantly more likely to support both healthy eating and activity policies (adjusted OR = 4.18, 95% CI: 2.48-7.05) and local funding policies (adjusted OR = 2.84, 95% CI: 1.95-4.15). Age was associated with support for local funding for all age categories compared with those 65 years and older, but this was significant after adjustment only for the age groups 25 to 34 years, 35 to 44 years, and 45 to 54 years.
Those earning less than $15 000 were more likely to support both types of policies than those earning at least $75 000. However, the only association that was significant before and after adjustment was between those earning $15 000 to $24 999 and those earning at least $75 000 for supporting the healthy eating and activity policies (adjusted OR = 2.32, 95% CI: 1.19–4.52).
Before, but not after, adjustment, respondents living in the 5 counties with the highest levels of obesity were significantly more likely to support both types of policies than those in the counties with the lowest obesity prevalence. Respondents with at least 1 child in the home were significantly more likely to support policies related to local funding (OR = 1.94, 95% CI: 1.53-2.44). Similarly, obese individuals were more likely to support policies related to local funding than those with a normal BMI between 18.5 and 24.9 kg/m2. Neither of these associations was significant after adjustment. Only those in the lowest education category (less than high school) were significantly more likely than those with at least a college education to support healthy eating and activity policies; this association was no longer significant after adjustment.
Findings from this study indicate support among Mississippi residents for policies aimed at promoting healthy eating and activity in their communities, particularly among those living in counties with the highest levels of obesity. Other large-scale polls32–35 have shown similar levels of support. In late 2010, the California Endowment funded a poll of 1005 registered voters34 and found that levels of support for banning advertising were similar in Mississippi and California (66% and 64%, respectively).34 Public use of school facilities when school is not in session had 88% support in California34 and 80% support in Mississippi. On the contrary, Mississippi residents had higher support for banning the sale of soda in school (70% strongly or somewhat supported) than those in New York State (59% support)32 (April 2010 Quinnipiac University random-digit-dialing survey of 1381 New York State registered voters). Similarly, 65% of Mississippi respondents expressed support for taxing soda and soft drinks to fund public education campaigns for childhood obesity whereas only 56% of respondents in the California survey supported this policy.34,35 A slightly different version of this question (where the use of tax funds was not specified) was asked in a December 2009 Columbia Broadcasting System poll,33 which included a random-digit-dialing sample of 1048 adults nationwide; only 38% favored the policy. Finally, in the Quinnipac University poll,32 which suggested that the money raised from such taxes would be used to fund health care only, 48% supported the policy.
Support among Mississippians has remained relatively constant for some policies since a 2006 poll by the Center for Mississippi Health Policy.35 For example, support for requiring schools to send annual BMI scorecards to parents was 68% in 2011 and 66% in 2006; this was much higher than the national level of support (51%), at the time (2006). A 2006 survey found that support for eliminating vending machines in elementary schools was slightly greater in Mississippi35 (78.5%) than in the rest of the nation at that time (71%); in this 2011 survey, 74% of Mississippians supported the policy.
It is often difficult for researchers to conduct polls assessing policy perceptions due to financial and methodological constraints. Funding and research study timelines are often out of synch, with the need for information about timely issues.36 The aforementioned surveys were not published as part of the scientific literature. In the case of these surveys, dissemination through popular, rather than scientific press allows practitioners and policy makers the opportunity to use the knowledge gleaned from these investigations in a more timely fashion. While inclusion in the popular press is important, publication of these findings in the scientific literature aids in their use by researchers and policy makers. This type of publication allows for peer review, which adds credibility, and allows them to be searchable in databases such as PubMed.
Many of the trends across demographic characteristics seen in the Mississippi population were also seen in similar surveys conducted elsewhere. In Mississippi, support for taxing soda and soft drinks had greater support among African Americans (76%) than among whites or any other race (55% and 61%, respectively). Similarly, in California,34 the highest support was among African Americans and Latino/Hispanics (70% and 72%, respectively) compared with 50% and 53% for white non-Hispanics and Asian-American/others.
Findings from this Mississippi study have a number of research and policy implications. This study identified several characteristics as being related to policy support. Future research efforts should investigate the use of such components to target social marketing campaigns, such as those aimed at building support for policy change. A recent Institute of Medicine report, Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation,37 recommends social marketing as a method of changing public opinion. An essential piece of this method is that these campaigns be highly targeted (in terms of audience and behavior).38
In addition to the research implications, this study has the potential to inform policy. Policy makers rely heavily on constituent needs and opinions in making crucial policy decisions.26 Practitioners can use this information to tailor communication about public health interventions, including policy approaches. Local health leaders can determine priorities for education and awareness. In addition, this information, showing clear support for policies encouraging healthy eating and physical activity, can be powerful in the hands of advocates working with policy makers to achieve change.26
An important use for these findings is to provide those looking to achieve policy change with information regarding population subgroups most likely to support (and those most likely to oppose) policy changes related to healthy eating and physical activity. This study found that African Americans and females were more likely to support healthy eating and activity policies and local funding policies. Similarly, those in younger age groups were more likely to support local funding than those in the oldest age group. These are individuals who may need to be specifically targeted, perhaps with different messages, in order to change their perception.
Study limitations include a cross-sectional design, which limits causal inferences. Second, although the study was designed to be representative of the counties sampled (the 5 with the highest and 5 with the lowest obesity levels in Mississippi), it is not representative of state or national samples, which may limit generalizability. The response rate for the sample was low; however, the individuals were sampled for variability (by selecting those in the counties with the highest and lowest levels of obesity), and the aim was not to try to estimate area-wide prevalence. Third, related to sampling, we had a low response rate and although an attempt was used to survey by cell phone as well as landline, there may have been bias in those choosing to participate in the survey.
These results paint a picture of the perceptions of residents concerning public health policies related to healthy eating and physical activity. Although patterns were found by demographics such as race and gender, overall support was quite high for all policies. In practice, individuals and groups that support such policies should be recruited as advocates. Those opposing the policies may be targeted with messages aimed at reducing their resistance. These findings can help move public opinion, a key driver of policy change.
1. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307(5):491–497.
2. Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood). 2009;28(5):w822–w831.
5. Seagle HM, Strain GW, Makris A, Reeves RS. Position of the American Dietetic Association: weight management. J Am Diet Assoc. 2009;109(2):330–346.
6. Committee PAGA. Physical Activity Guidelines Advisory Committee Report, 2008. Washington, DC: Department of Health and Human Services; 2008.
7. Chen SE, Florax RJ. Zoning for health: the obesity epidemic and opportunities for local policy intervention. J Nutr. 2010;140(6):1181–1184.
8. Casey AA, Elliott M, Glanz K, et al. Impact of the food environment and physical activity environment on behaviors and weight status in rural U.S. communities. Prev Med. 2008;47(6):600–604.
9. Centers for Disease Control and Prevention. Ten great public health achievements—United States, 1900–1999. MMWR Morb Mortal Wkly Rep. 1999;48(12):241–243.
10. Brownson RC, Chriqui JF, Stamatakis KA. Understanding evidence-based public health policy. Am J Public Health. 2009;99(9):1576–1583.
11. Anderson LM, Quinn TA, Glanz K, et al. The effectiveness of worksite nutrition and physical activity interventions for controlling employee overweight and obesity: a systematic review. Am J Prev Med. 2009;37(4):340–357.
12. Dobbins M, De Corby K, Robeson P, Husson H, Tirilis D. School-based physical activity programs for promoting physical activity and fitness in children and adolescents aged 6–18. Cochrane Database Syst Rev. 2009;(1):CD007651. doi:10.1002/14651858.CD007651.
13. Heath GW, Brownson RC, Kruger J, et al. The effectiveness of urban design and land use and transport policies and practices to increase physical activity: a systematic review. J Physical Activity and Health. 2006;3(suppl 1):S55–S76.
14. Birch LL, Parker L, Burns A eds; Institute of Medicine. Early Childhood Obesity Prevention Policies. Washington, DC: The National Academies Press; 2011.
15. Kahn EB, Ramsey LT, Brownson RC, et al. The effectiveness of interventions to increase physical activity. A systematic review (1,2). Am J Prev Med. 2002;22(4) suppl 1):73–107.
16. Katz DL. School-based interventions for health promotion and weight control: not just waiting on the world to change. Annu Rev Public Health. 2009;30:253–272.
17. Katz DL, O'Connell M, Njike VY, Yeh MC, Nawaz H. Strategies for the prevention and control of obesity in the school setting: systematic review and meta-analysis. Int J Obes (Lond). 2008;32(12):1780–1789.
18. NICE. Physical activity and the environment: review one: transport. In: NICE Public Health Collaborating Centre—Physical activity. London, UK: NICE; September 5, 2006. 2006.
19. NICE. Physical activity and the environment: review two: urban planning and design. In: NICE Public Health Collaborating Centre—Physical activity. London, UK: NICE; October 17, 2006. 2006.
20. NICE. Physical activity and the environment: review three: natural environment. In: NICE Public Health Collaborating Centre—Physical activity. London, UK: NICE; October 10, 2006. 2006.
21. NICE. Physical activity and the environment: review four: policy. In: NICE Public Health Collaborating Centre—Physical activity. London, UK: NICE; December 2006. 2006.
22. NICE. Physical activity and the environment: review five: building design. In: NICE Public Health Collaborating CentrePhysical activity. London, UK: NICE; January 2007. 2007.
23. Colditz GA, Samplin-Salgado M, Ryan CT, et al. Harvard report on cancer prevention, volume 5: fulfilling the potential for cancer prevention: policy approaches. Cancer Causes Control. 2002;13(3):199–212.
24. Institute of Medicine. Local Government Actions to Prevent Childhood Obesity. Washington, DC: The National Academies Press; 2009.
25. Zaza S, Briss PA, Harris KW, eds. The Guide to Community Preventive Services: What Works to Promote Health? New York: Oxford University Press; 2005.
26. Dodson EA, Stamatakis KA, Chalifour S, Haire-Joshu D, McBride T, Brownson RC. State legislators' work on public health-related issues: what influences priorities? J Public Health Manag Pract. 2013;19(1):25–29.
27. Institute of Medicine, Committee on Prevention of Obesity in Children and Youth. Preventing Childhood Obesity: Health in the Balance. Washington, DC: The National Academies Press; 2005.
28. Kersh R, Morone J. How the personal becomes political: prohibitions, public health, and obesity. Stud Am Polit Dev. 2002;16(2):162–175.
29. Economos CD, Brownson RC, DeAngelis MA, et al. What lessons have been learned from other attempts to guide social change? Nutr Rev. 2001;59(3, pt 2):S40–S56; discussion S57–S65.
30. Healthiest and Least Healthy Counties Ranked in Every State 2012 County Health Rankings Show What Influences How Healthy Residents Are, How Long They Live. Madison, WI: Robert Wood Johnson Foundation; 2012.
32. New Yorkers Oppose Fat-tax 2–1, Quinnipiac University Poll Finds; But Split on Tax if Money is Promised for Health Care. Hamden, CT: Quinnipiac University Polling Institute; 2010.
33. Where America Stands: Fighting Obesity, December 17-22, 2009. January 7, 2010, 2011.
34. DiCamillo M, Field M. Increasing Concern Among California Voters About Childhood Obesity. Support for Policies Aimed at Encouraging Greater Physical Activity and Healthier Eating Among Children, Release #2367. San Francisco, CA: Field Research Corporation; 2011.
35. Kolbo JR, Harbaugh B, Anderson-Lewis C, Zhang L, Lee J, Sasser T. What Do Mississippians Think About Childhood Obesity? Findings From the 2006 Mississippi Public Perception of Childhood Obesity Survey. The Center for Mississippi Health Policy; 2006.
36. Green LW. Making research relevant: if it is an evidence-based practice, where's the practice-based evidence? Fam Pract. 2008;25(suppl 1):i20–i24.
37. Institute of Medicine. Accelerating Progress in Obesity Prevention: Solving the Weight of the Nation. Washington, DC: The National Academies Press; 2012.
38. Wakefield MA, Loken B, Hornik RC. Use of mass media campaigns to change health behaviour. Lancet. 2010;376(9748):1261–1271.