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Risk Factors for Obesity: A Critique
Major Risk Factors for Obesity: A Critique of the Research Literature
Major Risk Factors for Obesity: A Critique of the Research Literature
The World Health Organization (WHO, 2013) estimated that close to 1.4 million adults were overweight in 2008 and of these 500 million were obese. For adults over the age of 20 this implies that 35 and 11% of the global adult population were overweight and obese, respectively. The definition of overweight is a body mass index (BMI) of 25 or higher, while obesity is defined as a BMI of 30 or higher. While obesity does not directly result in the death of anyone, it is the fifth leading mortality risk globally and is responsible for 2.8 million deaths annually. This is due to obesity representing a significant risk factor for serious comorbid conditions, including diabetes and cardiovascular disease. Accordingly, nearly 20% of U.S. healthcare spending is associated with obesity and obesity-related comorbidities (Ladabaum, Mannalithara, Myer, & Singh, 2014).
Aside from the devastating health consequences of obesity, the number of adults and children suffering from this condition has doubled (WHO, 2013) and tripled (Sample, Carroll, Barksdale, & Jessup, 2013), respectively, since the 1980s. The emergence of the adult and childhood obesity epidemic has motivated researchers and healthcare policymakers to search for common risk factors that can be addressed through policy changes at the national, local, and organizational levels. This report examines the findings of recent research as a way to understand how nursing interventions could improve the health outcomes of people at risk or suffering from obesity.
One of the primary concerns of health policymakers is the role of racial, ethnic, and economic disparities in determining the prevalence of obesity and associated comorbid conditions (Gaskin et al., 2013). The Patient Protection and Affordable Care Act of 2010 is expected to provide some relief; however, this legislation alone cannot eliminate health disparities (Leong & Roberts, 2013). What is needed is a greater understanding of the factors that contribute to health disparities so that interventions can be implemented, especially in light of the growing racial and ethnic diversity in the United States (Cooper, 2012).
Among the factors increasingly recognized as contributing to health disparities is the relationship between obesity and the neighborhood built environment. Most studies to date have employed a cross-sectional design, but Michael and colleagues (2014) sought to improve the quality of the research by examining the impact of a 14-year neighborhood improvement project on resident health indicators for the City of Portland, Oregon, using a retrospective cohort study design. In order to control for the demographic variables of race and ethnicity, only Caucasian, non-Hispanic older women with a mean age of 72.6 (± 5.5 years) were included in the study (N = 2,003). The primary independent variables were bus density, distance to transit, intersection density, and distance to commercial area, which collectively contributed to a walkability score. In addition, the availability of parks and green space, in addition to neighborhood socioeconomic status (nSES), were examined. The dependent variable was BMI. Improvements in neighborhood walkability and parks were not predictive of BMI scores, but the demographic variables of age, comorbidity, mobility disability, tobacco use, and nSES were. In addition, education and a history of manual labor were predictive of baseline BMI. While there was a non-significant decline in BMI over the period in relation to walkability and parks, this slight change could not be distinguished from naturally-occurring reductions in BMIs due to aging and increased frailty.
The study by Michael and colleagues (2014) revealed that improvements in the neighborhood built environment probably plays a minor role in determining obesity prevalence compared to SES. They found that higher SES was correlated with a healthier BMI at baseline and overtime. These findings support those of Lim and Harris (2014), who examined the data obtained through three distinct surveys of New York City neighborhoods (N ? 10,000 adults over the age of 18). Among the independent variables found to be predictive of obesity prevalence were race/ethnicity, age, gender, native- or foreign-born, education, SES, neighborhood walkability, and neighborhood diversity (p < .001). Among these variables, neighborhood diversity and neighborhood poverty were explored in greater depth. When compared to non-Hispanic Whites and after controlling for all known and suspected confounding variables, being African-American or Hispanic increased the risk of obesity by 60 and 30%, respectively. The inter-individual obesity prevalence due to neighborhood variables was almost entirely explained by the percentage of African-American residents, while neighborhood walkability contributed only a small percentage. The authors concluded that SES explains some of the association between obesity and the independent variable 'African-American neighborhood percentage,' but the greatest contributor to this finding was deemed to be structural differences in the built environment, such as the density of fast food establishments.
The conclusion reached by Lim and Harris (2014) is in agreement with the findings of Pruchno and colleagues (2014), who discovered that obesity was significantly predicted by the prevalence of neighborhood fast food retailers, convenience stores, bars, and small grocers (p < .001), but not by supermarkets. Since childhood overweight and obesity is a significant risk factor for adult obesity (Goldschmidt, Wilfley, Paluch, Roemmich, & Epstein, 2013), the impact of the built environment on child obesity rates is also of interest to researchers and health policymakers. Accordingly, a recent cross-sectional observational study found a significant correlation between adolescent (N = 706) perceived walking distance to food outlets and daily amounts of sugar-sweetened beverage (p < .01) and fast food (p < .05) consumed (Hearst, Pasch, & Laska, 2012). The most convenient source of unhealthy, high-calorie foods was convenience stores, followed by coffee shops, fast food restaurants, and supermarkets. A 10 minute walk was found to be too short to provide significant protection against increased consumption of sugar-sweetened beverages and fast foods.
The results of Hearst and colleagues (2012) suggest that the easy availability of supermarkets is not protective against an unhealthy diet for adolescents. In support of these findings, Shier and colleagues (2012) conducted an observational cohort study tracking BMI from the fifth to eighth grade (longitudinal) for a representative U.S. national sample (N = 6,260). The BMI data was compared against geographic data (cross-sectional), which provided information about various types of food outlets, including convenience stores, fast food restaurants, and supermarkets. Overall, the strongest predictor of a higher BMI by eighth grade was access to more than one type of food outlet (p = .005); however, access to food outlets was not predictive of BMI change from fifth to eighth grade. This was a nationally representative sample, so minorities represented less than 40% of the sample and the SES bell curve centered on lower middle class families (median annual income = $49,022). The relevant protective covariates were spending less time watching television and belonging to a higher income level.
Probably the weakest study reviewed above is Michael et al. (2014) because they limited their analysis to BMI only and did not include a comparison group. Since this was a retrospective study they could have gathered other health from patient files, including blood pressure and resting heart rate. In addition, limiting the cohort to elderly Caucasian women severely limited the relevance of the findings to the general population. Expanding the cohort to include all adults of any race or ethnicity may have revealed interesting findings, especially if a comparison group had been included. By comparison, the other three studies examined the interaction between independent and dependent variables across racial, ethnic, age, and SES lines and it became clear that race and SES is a strong predictor of obesity within the United States. These findings were in turn correlated with significant differences in the neighborhood built environment. The substantial size of the study samples increased the power of the studies, allowing the complexity of the interactions between independent and dependent variables to be revealed.
The above studies reveal the importance of race and SES in determining the prevalence of obesity within the U.S., which is in turn modified by the neighborhood built environment. Health care organizations can play an important role in reducing health disparities by strengthening the primary care system serving a community (Lee, Kiyu, Milman, & Jimenez, 2007). Developing effective interventions to reduce health disparities will be increasingly important as the diversity of American communities continue to increase (Cooper, 2012).
Given the above findings, implementing an intervention designed to increase access to primary care and to improve obesity patient education may have a significant impact on obesity prevalence. The efficacy of a cost-effective intervention, like the one implemented in Cook County for diabetes patients, (Vachon et al., 2007), could be investigated. A prospective, longitudinal, observational cohort study that examined changes in BMI over time could be a useful study design, while another could be a longitudinal, cross-sectional study that compared the changes in obesity prevalence between an intervention and control community. A combination of the two would be preferable though, one that included a comparison…[continue]
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