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Aspects of Obesity Risk Factors

Introduction

Obesity has become one of the most pressing concerns in recent years. Most studies attribute the rising cases of obesity to economic development in both low and high income economies (Ameye and Swinnen, 2019). These studies show that obesity is more prevalent in high-income economies, and is more prevalent among the middle class population in developing economies. In addition, studies also show that women are more prone to obesity especially in low and middle income economies. However, this gender gap declines in high income countries. Nonetheless, significant gender-based difference in the proportion of obese population in both high income and developing economies.

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Obesity occurs when a person’s calorific intake exceeds the normal body requirements. Clinically, a person is considered obese when the body mass index exceeds 30 (World Health Organization, 2021). Studies that examine the relationship between drugs and alcohol use disorders and obesity find a small but significant relationship between them. Recently, Almandoz, et al. (2021) finds a strong and positive correlation between substance abuse and obesity particularly in areas that had strict lockdowns to prevent the spread of COVID-19. However, more scientific studies indicate that opioid consumption destabilizes brain reward and motivational circuits (Elman, et al., 2020). However, none of the existing studies offers a comprehensive model to establish the causative link between obesity and drug and alcohol abuse.

Study Question

What is the relationship between obesity and gender, exposure to drugs, and type of neighborhood among children aged 0-17 years?

Method

This research uses a quantitative research design. It uses secondary data to determine the predisposition to obesity for children aged 0-17 years living in the United States. It used the odds ratio and cross-tabulations to determine risk of children exposed to the identified risj compared to those that have had no such exposure. It hypothesizes that children that live in neighborhoods that have high socioeconomic status, limited physical amenities, and ineffective social support systems are more prone to becoming obese (Mohammed et al., 2019). In contrast, Mohammed et al. attributes the high prevalence of obesity to the low social engagement, lack of exercise, and lack of emotional support, which predisposes the residents to gaining weight.

Sampling and Data Collection

This research relied on secondary data from the National Survey of Children’s Health (NSCH) to answer the research questions. The NSCH study sampled 48,686 children living in the United States and aged between 0-17 years. The sample represented the total population of children in the US, which is estimated at 74 million (Child Stat, 2021). The sample accounted for 6.5% of the total population.

Variables

The dependent variable was the odds of becoming obese, while the independent variables were the children’s external conditions including neighborhood characteristics, gender, and exposure to drugs. Obesity was defined as having a body mass index exceeding 30. In addition, exposure to drugs was indicated as at least one instance in a child’s life that they had used hard drugs or alcohol. These variables were obtained directly from the coded data in the NSCH survey dataset. The children were asked to state whether they had been exposed to drugs in their childhood and whether they considered their neighborhood supportive or not. The results were coded in SPSS software.

Analysis

The research used a quantitative research design to determine the odds of children becoming obese based on exposure to drugs and alcohol, and the social support systems in their neighborhoods. The research recoded the obesity variable into a dichotomous outcome to facilitate the calculation of the odds ratio. This odds ratio indicated the chances of developing obesity based on exposure to drugs and alcohol, and living in a supportive neighborhood. A cross-tabulation of these variables was performed to provide the risk estimate for each variable.

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Results

An initial cross-tabulation of the Obese variable against the ACE-Drug-16 variable. This analysis assessed the odds ratio of children that have had negative experiences with drugs and alcohol becoming obese. Table 1 shows the output from this analysis.

Table 1: Cross-tabulation of Obesity and negative experiences of alcohol and drugs in childhood

Adverse childhood experience: lived with anyone who had a problem with alcohol or drug * Obes Crosstabulation
Obes Total
Nobse Obese
Adverse childhood experience: lived with anyone who had a problem with alcohol or drug Experienced the adverse childhood experience Count 3852 484 4336
% within Adverse childhood experience: lived with anyone who had a problem with alcohol or drug 88.8% 11.2% 100.0%
No adverse childhood experiences Count 41791 2559 44350
% within Adverse childhood experience: lived with anyone who had a problem with alcohol or drug 94.2% 5.8% 100.0%
Total Count 45643 3043 48686
% within Adverse childhood experience: lived with anyone who had a problem with alcohol or drug 93.7% 6.3% 100.0%
Risk Estimate
Value 95% Confidence Interval
Lower Upper
Odds Ratio for Adverse childhood experience: lived with anyone who had a problem with alcohol or drug (Experienced the adverse childhood experience / No adverse childhood experiences) .487 .440 .540
For cohort Obes = Nobse .943 .933 .953
For cohort Obes = Obese 1.935 1.764 2.121
N of Valid Cases 48686

Table 1 shows that children that had been exposed to drugs and alcohol had an 11.2% chance of becoming obese. In contrast, children that had never had such experiences had a 5.8% chance of becoming obese. The odds ratio shows that children that had negative experiences with drugs and alcohol were 48.7% more likely to become obese compared to those that had never experienced drugs and alcohol problems.

The second cross-tabulation examined the influence of the children’s neighborhood in developing diabetes. Table 2 shows the odds of developing diabetes based on the social support of the neighborhood.

Table 2: Cross-tabulation of supportive neighborhood and obesity

Child live is a supportive neighborhood * Obes Crosstabulation
Obes Total
Nobse Obese
Child live is a supportive neighborhood Live in supportive neighborhoods Count 28118 1612 29730
% within Child live is a supportive neighborhood 94.6% 5.4% 100.0%
Do not live in supportive neighborhoods Count 17538 1418 18956
% within Child live is a supportive neighborhood 92.5% 7.5% 100.0%
Total Count 45656 3030 48686
% within Child live is a supportive neighborhood 93.8% 6.2% 100.0%
Risk Estimate
Value 95% Confidence Interval
Lower Upper
Odds Ratio for Child live is a supportive neighborhood (Live in supportive neighborhoods / Do not live in supportive neighborhoods) 1.410 1.310 1.518
For cohort Obes = Nobse 1.022 1.017 1.027
For cohort Obes = Obese .725 .677 .777
N of Valid Cases 48686

Table 2 shows that children that live in supportive neighbourhoods have a 5.4% chance of being obese. In contrast, children that live in non-supportive neighbourhoods have a 7.5% chance of becoming obese. In addition, children in non-supportive neighbourhoods are 1.41 times more likely to develop obesity compared to their counterparts in socially supportive societies. Therefore, the social support structures in society play a significant role in the child’s potential for becoming obese.

The third cross-tabulation used gender and obesity to determine if the children’s gender predisposed them to obesity. Table 3 shows the cross-tabulation results of gender and obesity.

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Table 3: Cross-tabulation of gender and obesity

Sex of the child * Obes Crosstabulation
Obes Total
Nobse Obese
Sex of the child Male Count 23863 1870 25733
% within Sex of the child 92.7% 7.3% 100.0%
Female Count 23248 1231 24479
% within Sex of the child 95.0% 5.0% 100.0%
Total Count 47111 3101 50212
% within Sex of the child 93.8% 6.2% 100.0%
3Risk Estimate
Value 95% Confidence Interval
Lower Upper
Odds Ratio for Sex of the child (Male / Female) .676 .627 .728
For cohort Obes = Nobse .976 .972 .981
For cohort Obes = Obese 1.445 1.348 1.549
N of Valid Cases 50212

From Table 3, boys were 7.3% had become obese compared to 5% of the sampled girls. The odds ratio shows that boys were more 67.6% more likely to become obese compared to women. Therefore, contrary to extant literature, boys are more prone to becoming obese compared to girls.

Discussion and Conclusion

Results from this exercise affirm the findings of most studies on the risk factors for diabetes. It finds that lack of social support, gender, and exposure to drugs and alcohol play a major role in influencing the causes of diabetes. However, contrary to the extant findings from published journals, this research finds that boys are more prone to becoming obese compared to girls. Consequently, additional research should be conducted to determine the reasons for the contradictory findings.

The finding that social support structures play an important role in children’s physical development offers significant implications for several stakeholders. These structures include emotional support from parents, development of an active lifestyle that reduces, participation in sports and other physically exerting leisure activities, and building of healthy social relationships with the peers (Rosário & Martins, 2020). Children that grow in societies that have healthy social support networks are also less likely to become depressed or predisposed to drug and alcohol abuse. Therefore, social support structures are effective means of controlling substance dependence and adverse lifestyles among children.

The study makes one contradictory finding on the relationship between gender and obesity. Extant studies find a stronger link between female gender and obesity (Ulijaszek, 2017). This contradiction might have been caused by the age differences of the sampled population in various studies. While most studies focus on the adult population, this study focuses on the children aged 0-17 years of age. In the early ages, hormonal differences in the male and female population could have caused variations in weight for boys and girls. However, as women develop into adults, their social settings and changes in their biological nature meant to support motherhood result in accumulation of fat. Hence, age differences could have been the driving factors behind the contradiction.

The findings have strong social and policy implications for many stakeholders. Firstly, physical planners should include adequate facilities to support physical activities among the residents. Secondly, the society should develop avenues for social interaction between parents and their children as well as among the children. These interventions will enable the children to feel accepted as members of the communities. These interactions will also create avenues to let out stressful situations without resorting to drugs and alcohol.

References

Almandoz, J.P., Xie, L., Schellinger, J.N., Mathew, S.N., Bismar, N., Ofori, A., Kukreja, S., Schneider, B., Vidot, D., and Messiah, S.E. (2021). Substance use, mental health and weight-related behaviors during the COVID-19 pandemic in people with obesity. Clinical Obesity, 11(2), 1-10.

Ameye, H., & Swinnen, J. (2019). Obesity, income and gender: The changing global relationship. Global Food Security, 23, 267-281.

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ChildStat. (2021). POP1 Child population: Number of children (in millions) ages 0–17 in the United States by age, 1950–2019 and projected 2020–2050. Web.

Elman, I., Howard, M., Borodovsky, J.T., Mysels, D., Rott, D., Borsook, D., & Albanese, M. (2020). Metabolic and addiction indices in patients on opioid agonist medication-assisted treatment: A comparison of buprenorphine and methadone. Scientific Reports, 10(5617), 1-12.

Mohammed, S.H., Habtewold, T.D., Birhanu, M.M., Sissay, T.A., Tegegne, B.S., Abuzerr, S., & Esmaillzadeh, A. (2019). Neighborhood socioeconomic status and overweight/obesity: a systematic review and meta-analysis of epidemiological studies. Biomedical Journal, 9(11), 1-12.

Rosário, R., & Martins, M.J. (2020). Understanding obesity: From its causes to impact on life. Bentham Science Publishers.

Ulijaszek, S.J. (2017). Models of obesity: From ecology to complexity in science and policy. Cambridge University Press.

US Centers for Disease Control. (2021). 2016-17 National Survey of Children’s Health. US CDC Website.

World Health Organisation. (2021). What are obesity and overweight? Web.

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