Abstract
This paper concerns obesity and the factors which affect it in low-income communities of the United States. The topic’s relevance comes from the condition’s prevalence and the health risks it poses. Five articles devoted to the topic were analyzed to determine the state of affairs and collect valuable information for further use. The first two studies analyze the link between family income and obesity, as low-income families are at an increased risk of the condition, with the second one emphasizing its reverse casualty. The third study focuses on severe obesity in Hispanic/Latin children and the factors which cause it. The fourth article presents an overview of the association between built environments and obesity, where the former impacts physical activity. The last one considers the family’s impact on treating obesity. In the end, some general recommendations are provided, and the conclusion about obesity causation and prevention in low-income families is drawn.
Introduction
Obesity is a condition caused by physical and social factors that imply other health risks. Throughout the years, its prevalence kept increasing, transforming into a major healthcare issue. Although all demographics seem to be affected by obesity, the less advantaged social population groups are at an increased risk. The means to combat the condition are well-known, but due to various reasons, including economic ones, it might be difficult for the low-income population to follow them. However, analyzing the up-to-date evidence focused on the socioeconomic group’s relation to obesity might assist in finding solutions to the issue.
Study Analysis
The first article on obesity discusses how family income affects a child’s fitness level. The study’s purpose was to assess the association between socioeconomic status and several values including body mass index and obesity among eight racial and ethnic groups (Jin & Jones-Smith, 2015). The data was collected through a physical fitness test taken by a big sample of Californian students ranging from fifth- to ninth-graders (Jin & Jones-Smith, 2015). The study was cross-sectional, and regression analysis was used as a method to determine the hypothesis’s validity about the inverse proportionality of family income to a child’s obesity (Jin & Jones-Smith, 2015). The dependent variables were physical fitness level, body mass index, and obesity, while family income was an independent one accompanied by those indicating sex, age, and race/ethnicity (Jin & Jones-Smith, 2015). Thus, a set of relationships was established between the two groups of variables.
The study’s results confirmed the original hypothesis regarding the link between family income and obesity. More than half of the children with the condition belong to low-income families, which were also associated with a low physical fitness level (Jin & Jones-Smith, 2015). The relationship is observed in all studied racial and ethnic groups regardless of a child’s sex, although differences in the association’s magnitude exist, as boys, for instance, are physically active income notwithstanding (Jin & Jones-Smith, 2015). The study did not consider private school students and the shades of income, which could have affected the overall picture (Jin & Jones-Smith, 2015). However, the sample size is considerable enough to conclude that children from low-income families are at an increased risk of obesity, which requires implementing new policies.
Another article also delves into the connection between income and obesity. It aimed to determine the nature of their assumed association and assess the causality between them (Kim & Knesebeck, 2018). The study’s design is a systematic review supplied by meta-analysis (Kim & Knesebeck, 2018). The methods consisted of a literature search through various databases, further condensed, following certain inclusion and exclusion criteria and random-effect models accompanied by the Newcastle-Ottawa Scale to ensure quality (Kim & Knesebeck, 2018). The searching phase considered such factors as population, intervention, outcome, and others while selecting the studies (Kim & Knesebeck, 2018). The results section highlighted the process, which eventually left 21 items to be analyzed (Kim & Knesebeck, 2018). The analysis focused on the hypotheses, which implied binary or more nuanced outcomes (Kim & Knesebeck, 2018). Thus, by considering and dissecting the most representative studies, the authors could obtain a picture of the relationship.
The article’s findings reveal that the supposed association between income and obesity exists, although it differs from the original expectations. The significance of social causation, implying that low income leads to obesity, was deemed negligible, while reverse casualty proved, which signifies that being obese affects one’s income, proved to be important (Kim & Knesebeck, 2018). It is connected to the fact that the former is an established theory, for which negative results would seem unwarranted, while the latter is understudied (Kim & Knesebeck, 2018). Although the study had limitations that could have prevented relevant articles from being analyzed, the results are not contradictory (Kim & Knesebeck, 2018). Thus, the relationship between the analyzed phenomena is evident and bilateral, as they affect each other.
An article, continuing the topic of obesity and family income’s association, considers minority ethnicities, namely Hispanic and Latino children. The study aimed to identify the factors that might lead to severe obesity within those demographic groups (Salahuddin et al., 2017). The design is cross-sectional; the data was collected by surveying parents of Texan children ranging from two to twelve years old with a body mass index equaling or surpassing 85% (Salahuddin et al., 2017). The study’s primary method was regression analysis that was used for each age group separately and considered such measures as outcomes, exposures, and covariates, some of which were missing (Salahuddin et al., 2017). A sensitivity analysis was additionally performed, and sociodemographic characteristics of those who provided the necessary data and chose not to were compared (Salahuddin et al., 2017). Thus, the study identified three age groups and the significant factors for severe obesity.
The results reveal that the condition is common among the surveyed demographic. Approximately a third of all participants had severe obesity, which increased in the older groups (Salahuddin et al., 2017). Most of the factors assumed by the study to be formative in obesity’s development, including material and behavioral ones, were insignificant, especially the latter (Salahuddin et al., 2017). Being a large-for-gestational-age child was identified as a relevant obesity predictor for the age group of 9 to 12, and maternal severe obesity was a significant factor for the youngest and the oldest groups (Salahuddin et al., 2017). The study’s limitations were linked to the missing data and the binary outcome (Salahuddin et al., 2017). Altogether, the study revealed the severe obesity factors that affect the Hispanic/Latino demographic.
The fourth study focuses on physical activity as a factor that connects to obesity. It aimed to analyze the complex relationship between the built environment and such variables as physical activity and body mass index while making the findings more accessible (Sallis et al., 2020). The study covered several countries and was cross-sectional, carefully approaching the selection of the neighborhoods and the participants (Sallis et al., 2020). Such methods as surveying, measurement with accelerometers, and geographic information systems were used (Sallis et al., 2020). The latter had some variables associated with it, including neighborhood buffers, park access, and the walkability index, which was calculated using intersection and net residential density and land-use mix (Sallis et al., 2020). Some measures were self-reported to compare objective data with the participants’ self-perception, which revealed high reliability (Sallis et al., 2020). The data analysis applied generalized additive mixed models and various functions to determine a person’s obesity status (Sallis et al., 2020). In the end, a complex relationship between objective built environment, physical activity, and obesity was established.
The study’s results demonstrate that environmental variables impact physical activity and obesity outcomes. Built environments can be activity-supportive, leading to a decreased risk of non-communicable diseases and obesity, or provide fewer opportunities to raise walkability (Sallis et al., 2020). The study’s limitations are related to the insufficient representation of middle- and low-income countries, although the findings could be particularly relevant for them (Sallis et al., 2020). However, the results also have implications for financially disadvantaged people from developed countries who could find it difficult to leave an environment that is not activity-supportive, putting them at an increased risk of obesity.
The last study is concerned with obesity prevention in families. Its purpose was to explore how parents and children influence each other’s habits that impact obesity, namely, physical activity and diet (Zhen-Duan et al., 2019). The study’s design adapts community-based participatory research and is rooted in social cognitive theory (Zhen-Duan et al., 2019). Eight low-income families were included in the study, as both adults and children from them could participate (Zhen-Duan et al., 2019). The former belonged to several age ranges and had to have one of the three food-related health conditions, while the latter’s age was limited to the range between ten and seventeen years old (Zhen-Duan et al., 2019). The primary method was qualitative interviewing with open-ended questions, and the children were interviewed separately to obtain an objective picture (Zhen-Duan et al., 2019). The data were analyzed thematically and summarized to determine the relationships within each family, and some parts of the transcript were coded for further comparison (Zhen-Duan et al., 2019). Ultimately, the meticulous research process led to obtaining valuable findings.
The results imply that obesity prevention in a family by performing physical activities and dieting is a collective effort. Collaborative and non-collaborative approaches to altering a family’s diet and attitude to physical activity were identified, with the latter consisting of barriers to changes for the most part (Zhen-Duan et al., 2019). Meanwhile, collaboration seems to drive positive changes, as family members will perceive them as sustainable when done collectively (Zhen-Duan et al., 2019). The findings are relevant for low-income families, as the participants were representative of the demographic, and the implications are that obesity prevention is achievable even with financial constraints.
Recommendations
Based on the studies discussed above, the following general recommendations regarding obesity causation and prevention can be provided:
- The government and the health care sector should implement new policies in light of the consistent evidence that family income and obesity are directly linked.
- Schools and families should collaborate to decrease obesity risks in children, as a half-hearted approach might be detrimental.
- Mothers from low-income families should be aware that their condition might be hereditary and prepared for the consequences.
- Municipal governments should consider improving fixed environments in low-income neighborhoods to make them more activity-supportive.
- Financial factors are not major forces in obesity prevention, so low-income families can be involved in it by collaborating with other family members.
- Although an individual can choose such an approach that would not impact the entire family, it is more beneficial to make it a collective effort to avoid miscommunication and further barriers.
Conclusion
To summarize everything, obesity is a health condition with an increasing prevalence that affects low-income communities in particular due to the inherent link between the two, but it is preventable. The analyzed studies are consistent in their findings that family income and obesity are connected, although the second study implies that the latter affects the former. Additionally, it is not the issue of how much a family earns but of the living environment and other conditions that serve as the consequences of being in a low-income community. Maternal factors might also be relevant, although some age groups remain unaffected by them. While it may take time for new policies based on the evidence to be implemented, families might find that their members’ habits also have a considerable impact on obesity development. With cooperation, they may decrease the risks of obesity for every member without much effort and financial investment, changing their attitude towards physical activity and adhering to a healthier diet.
References
Jin, Y., & Jones-Smith, J. C. (2015). Associations between family income and children’s physical fitness and obesity in California, 2010-2012. Preventing chronic disease, 12, E17.
Kim T. & Knesebeck, O. (2018). Income and obesity: What is the direction of the relationship? A systematic review and meta-analysis. BMJ Open, 8, e019862. Web.
Salahuddin, M., PĂ©rez, A., Ranjit, N., Kelder, S. H., Barlow, S. E., Pont, S. J., Butte, N. F., & Hoelscher, D. M. (2017). Predictors of severe obesity in low-income, predominantly Hispanic/Latino children: The Texas childhood obesity research demonstration study. Preventing chronic disease, 14, E141.
Sallis, J., Cerin, E., Kerr, J., Adams, M., Sugiyama, T., Christiansen, L., Schipperijn, J., Rachel Davey, R., Salvo, D., Frank, L., Bourdeaudhuij, I., Neville Owen, N. (2020). Built environment, physical activity, and obesity: findings from the International Physical Activity and Environment Network (IPEN) Adult study. Annual Review of Public Health, 41(1), 119-139.
Zhen-Duan, J., Engebretsen, B., & Laroche, H. H. (2019). Diet and physical activity changes among low-income families: perspectives of mothers and their children. International journal of qualitative studies on health and well-being, 14(1), 1658700.