Abstract (Centers for Disease Control, 2014). This problem

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Last updated: September 28, 2019

 AbstractThisresearch will focus on whether socioeconomic factors, specifically educationand income, increase or decrease obesity rates. We will use a bar chart andregression model using data from the Centers for Disease Control to show therelationships between obesity, income, and education levels.

The data wascollected from 2011-2016. We also used data from the U.S. Census Bureau todetermine the top ten states with the highest and lowest income and educationlevels, in 2016, to compare obesity rates. Research results found that stateswith the highest socioeconomic status had lower obesity rates than states withlower socioeconomic status. This was documented using various income andeducation levels.  KeywordsObesity,Income, Education, Rates, Socioeconomic IntroductionAccordingto the Centers for Disease Control (2016), obesity is defined as weight that ishigher than what is considered as a healthy weight for a given height. A bodymass index equal to or greater than 30 is considered to be obese.

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Body MassIndex (BMI) is a tool used for screening overweight or obesity. The formula tocalculate BMI is kg/m2, your weight (in kilograms) divided by yourheight (in meters) squared. Obesityis a growing problem in the United States. “In 2007-2008 more than one-third ofUnited States adults were obese” (Centers for Disease Control, 2014).

Thisproblem is only continuing to rise, with education and income playing a majorrole.Theobjective of this report is to prove that education and income levels affectobesity rates in the United States. HypothesisUsingdata collected from the Centers of Disease Control and the Bureau of LaborStatistics, the following is the final hypothesis: The states with higher socioeconomic status, as it relatesto education and income, have lower obesity rates than states with lowersocioeconomic status. LiteratureReviewObesity and Education Ourresearch of the correlation between education level and obesity had varyingconclusions.Publishedin the Journal of Obesity, vol 2013,”Black-White Disparities in Overweight and Obesity Trends by EducationalAttainment in the United States, 1997-2008″ analyzed data nationally of 174,228US-born adults. It found that obesity trends and racial disparities were moreprominent among individuals with higher educational levels, compared to theircounterparts with lower educational levels. The article stated “differentlevels of education, suggested as the single most important social influence onhealth, likely contribute to those obesity disparities and explanations for thepositive association between educational attainment and health are wellestablished” (Jackson, et al.

2013). More education is usually is associatedwith healthier behaviors such as not smoking, physical activity and drinking inmoderation. Because lower education generally relates to lower income, accessto resources can also attribute to poor health and physical conditioning. Obesityand educational attainment have increased dramatically since 1970. “FourDecades of Obesity Trends Among Non-Hispanic Whites and Blacks in the UnitedStates: Analyzing the Influences Educational Inequalities in Obesity andPopulation Improvements in Education” discusses a study that analyzed theinfluences of educational inequalities in obesity and population improvementsin education on national obesity trends between 1970-2010. This study looked atadults aged 25-74.

During this time period, the obesity rate nationallyincreased from 15.7% to 38.8%. “There were increases in obesity probabilitiesof non-college graduates compared to college graduates” (Yen 2016). Accordingto the study, obesity did not differ by education among black males and largestamong white females. Due to this difference, the study’s conclusion is obesityreductions associated with educational improvements were somewhat limited.

 Otherstudies have concluded the same as above, but are more definitive. “TheChanging Relationship Between Obesity and Educational Status” published in Gender Issues, Spring 2005, used annualcross-sectional survey data of non-institutionalized adults. Their conclusionwas that “the increased educational level of the adult population has notresulted in a decline in obesity” (Himes, et al. 2005).

This study made thepoint while the obesity rate has risen over the past 30 years, so haseducational attainment. In 2000, 84% of all adults age 25 and older hadcompleted high school compared to 50% in 1970. In the same time frame, theobesity rate has risen 15%. Education has been linked to better health,healthier lifestyles and lower incidence of chronic disease. “All else equal,an increase in educational level of the population should have led to a declinein obesity. However, the national patterns of obesity prevalence showotherwise” (Himes, et al. 2005).

According to the article, race may play afactor in the discrepancy. The United States has become more diverse over thedecades. African-Americans and Hispanics generally have an higher BMI. Thegrowth of the African-American and especially Hispanic population in the UnitedStates over 30 years is a factor when it comes to the obesity rate. Therefore,when looking at the United States as a whole, the correlation of educationlevel and obesity is hard to measure. Asstated earlier, There are different ways to observe and measure therelationship between education and obesity.

Our research shows differentstudies had conclusions from varying viewpoints. Therefore, the literaturereview is inconclusive when discussing education’s effect on obesity.  Obesityand IncomeObesity in the United States has beenincreasingly cited as a major health issue in recent decades, resulting indiseases such as heart disease, diabetes, depression etc. While many developedcountries have experienced similar increases, obesity rates in the United States are the highest in theworld. Obesity has continued to grow within the United States.

Two of everythree American men are considered to be overweight or obese, but the rates forwomen are far higher. The United States contains one of the highest percentageof obese people in the world. (“Most obese countries”. Reuters.Retrieved 24 September 2014)People from alleconomic backgrounds often eat for social, cultural, and emotional reasons, notjust for hunger according to the article, “Obesity in America: It’s Getting Worse,2004, January.” While obesity levels have been rising for all, some groups aremore affected than others. Some researches show that socioeconomic statusrelated to obesity changed.

According to the hypothesis, one can say that inlower-income states, people with less socioeconomic status were more likely tobe obese. Conversely, in high-income states, those with higher socioeconomicstatus were less likely to be obese.Various studies haveshown that overweight people are seen as less conscientious, less agreeable,less emotionally stable, less productive, lazy, lacking in self-discipline, andeven dishonest.

  People are also seen assloppy, ugly, socially unattractive, and sexually unskilled. According to, The price of obesity: How your salary depends on yourweight,  there will be no surprise to saythat obese people are paid less than the other, who has thinner body and higherincome.According to the information above from the article, aperson can make a list about how the money affect poor people’s eating habits.Poor families have limited food budgets and choices, and most of time stretch supplies toward the end of the month:?     Theyprobably choose high-fat foods such as sugars, cereals, potatoes and processedmeat products because these foods are more affordable and last longer thanfresh vegetables and fruits and lean meats and fish.

?     Poorfamilies most likely live in disadvantaged neighborhoods where healthy foodsare hard to find.?     Economicinsecurity, such as trouble paying bills or rent, leads to stress, and peopleoften cope by eating high-fat, sugary foods. On the other hand, what makes higher socioeconomic status inhigh-income states beneficial for staying thin? Conversely from these bulletpoints, higher income will give you more freedom instead of thinking about atthe end of the month, rent or bills. People, who has higher income more likelyto do, activities such as reading, attending cultural events, and going to themovies were associated just as much as exercise was with a lower BMI.It may be that in lower-income states, less socioeconomicstatus leads to consuming high-calorie food and avoiding physically toughtasks. People who participated in activities such as watching TV, attendingsporting events, and shopping had higher BMI, but in higher-income states,individuals with higher socioeconomic status may respond with healthy eatingand regular exercise.In light of the information above, we can see that obesityand income have a negative correlation.

When income increases, obesity decreases;vice versa, when income decreases, obesity increases.DataAnalysisThedata is taken from Center for disease control and prevention (BRFSS). This datais from 2011 to 2016.

Data was collected for 20 states. According to US CensusBureau for 2016, top ten states with higher and lower income are taken. This isto identify how overall higher or lower socioeconomic status will changeobesity level. Thisgraph represent average obesity level for higher income states. Where X axis is10 highest income states and Y axis range BMI values from 0 to 35. With BMIgreater than 30 is considered obesity.

Here in the 6 years span for ten states,the average BMI is seen below 30. However the maximum is 31.4 for Alaska in2016 and minimum is 21.

8 in Hawaii during 2011 and 2013. This shows thatobesity level was lower with higher income states.    Thisgraph represent ten states with lower income from 2011 to 2016.

Thereby X axisis states and Y axis BMI range from 0 to 40. As we see in this lower incomestates BMI is mostly above 30 of average, which considers obesity level ishigher there. Where minimum was 26.

3 in New mexico during 2011 and maximum wasin west virginia in 2016 with 37.7. Furthermore, higher income states obesity level is lower on average andvice versa.Thegraph is taken from Center for Disease Control and Prevention. This graphrepresents the nation wide data for 2016 based on income level.

The incomelevel is divided into six parts and data not reported is also included. Theobesity is shown in y axis from 0 to 40. The income level less than $15,000 is35.4. The income level between $15,000 to $24,999 is 33.4 and 31.9 at $25,000to $34,999. From 35,000 to $49,999 is 32 and $50,000 to $74,999 is 31.

1. Incomethat is  greater than $75,000 is 25.4.Further, it shows with increase in income the obesity level decreases from 35.4to 31.1. Thisgraph is taken from Centers for Disease Control and Prevention. This comparesthe education level and it’s effect on obesity in 2016 for the U.

S. (nationwide).The X axis shows education level: less than high school, high school, somecollege or technical school and college graduate. The Y axis contains BMIlevels from 0 to 40.  Hereit shows the higher average BMI with lower education.

The less than high schoolBMI range from 34.5 to 36.5, high school graduate is 31.8 to 32.8, Some collegeor technical school is 30.

5 to 31.5 and college graduate is 21.9 to 22.

6. So,the median average was lower in college graduates at 22.3 whereas less thanhigh school was 35.5.

 Furthermore,a regression analysis is done based on income and education with six states.The higher income states are: Maryland, Alaska, and New Jersey; and lowerincome states are: Oklahoma, Tennessee, and New Mexico.The data is taken fromthe Centers for Disease Control and Prevention. Income is divided into 6levels: 1-less than $15,000, 2-$15,000 to $24,999, 3-$25,000-$34,999, 4-$35,000to $49,999, 5-$50,000 to $74,999 and 6-$75,000 and above. Education is dividedinto 4 levels: 1-less than high schools, 2-high school grad, 3-some college andtechnical school and 4-college graduate. Maryland Regression Analysis ofObesity based on Income and Education Level 2016 The multiple R is correlation coefficient, which says howstrong the linear relationship is. Where it has moderate relationship by incomelevel with 0.

57. R squared is coefficient of determination, which isstatistical measure to know how close the data is to regression line. Which is33% or 0.331 around the mean of the data by income level.

The regressionanalysis of Maryland state for 2016 based on education, where multiple R iscorrelation coefficient is 0.88 says strong relationship and Coefficient ofdetermination is 78%. AlaskaRegression Analysis of Obesity by Income and Education Level 2016Alaskaregression analysis based on income level, where correlation coefficient has noor negligible relationship and coefficient of determination is around 2%. Byeducation level, the correlation coefficient is 0.36 is moderate relation andcoefficient of determination is around 13%. New Jersey Regression Analysis ofObesity by Income and Education Level 2016Withnew jersey regression analysis by income level, there is strong relation ofcorrelation coefficient with 0.92.  The coefficientof determination is 84%.

The multiple r correlation coefficient is strongrelation with 0.95 and 90% is coefficient of determination by education level. Oklahoma Regression Analysis ofObesity by Income and Education Level 2016Theregression analysis by income level for oklahoma, 0.74 is multiple rcorrelation coefficient is strong relation and coefficient of determination is54%. By education level correlation coefficient is 0.83 shows strong relationand 69% of coefficient of determination.        ForTennessee regression analysis by income level, where correlation coefficient is0.

39 with moderate relationship. The R square is coefficient of determinationis 15%. By education level, the correlation coefficient has moderaterelationship and R square is coefficient of determination is 37%.   Tennessee Regression Analysis ofObesity by Income and Education Level 2016                    New Mexico Regression Analysis ofObesity by Income and Education Level 2016 Theobesity level in New Mexico by income level , the correlation coefficient has astrong relationship. The R square is coefficient of determination which is62%.

The correlation coefficient by education level is 0.77, which means thereis a strong relationship and coefficient of determination is 60%. ResultsAstatistical analysis to demonstrate the relationship between obesity, income,and education was completed. Based on this analysis, a determination on if theresults accept or reject the hypothesis was able to be made. The hypothesisstates that states with higher socioeconomic status, as it relates to educationand income, have lower obesity rates than states with lower socioeconomicstatus.  Themajority of the evidence found proved the hypothesis to be true. As income andeducation levels increased, obesity rates decreased. Education levels showedsignificant differences in obesity rates.

Based on the model, the moreeducation achieved, nationwide, the lower obesity rates were. Also, the moreincome in the household, nationwide, the lower obesity rates were. However,there was some evidence found that were exceptions to the hypothesis. In one ofthe higher income states, Alaska, obesity rates were significantly higher thanthe rest in the year 2016; just as high as those in the lower income states. InNew Mexico, a lower income state, there were significantly lower obesity ratesfor all six years.  Aregression analysis of the top 3 higher income states: Maryland, Alaska, andNew Jersey; and top 3 lower income states: Oklahoma, Tennessee, and New Mexicowere conducted.

The findings implicate for most of the above states, there is asignificant relationship between obesity, income, and education. There is anexception to this; Tennessee showed no significant relationship and fit to themodel than the other states in the model. It isobvious that education and income levels have a significant impact on obesity.We assume that those in higher income areas, have more money and resources tomaintain a healthier lifestyle than those in lower income areas.

Thereare some cases where the hypothesis isn’t supported by the evidence but overallthere is enough evidence to support it. Therefore, we can accept thehypothesis. FutureDirectionsIt isvital to continue research and develop more accurate models to representobesity and what causes it. In doing so, a way to decrease obesity can bepinpointed.

. Different ways to improve the regression analysis will be given,since there were some instances where the hypothesis wasn’t supported by theevidence. Also, limitations of the study and how to improve future research toeliminate any sources of error will also be given. Improve Future Regression ModelsOnesuggestion is to use one-on-one regression. This involves collecting the dataand then composing scatter plots for each independent variables plotted againstthe dependent variable.

A straight line( linear) will form if the variables arein direct proportion of each other and when one variable changes the other willbe affected. Also, instead of regressing each of the independent variablesagainst the dependent variable; a multiple regression can be used where each ofthe independent variables are regressed as a group against the dependentvariable. LimitationsThereare some constraints that have been found when it comes to using register-baseddata. When dealing with income that is registered based, majority of the timeit is self reported.

Self-reported income is subject to reporting bias andcan’t be used to determine if there is a direct correlation between obesity andincome. In future studies, the fact that many report themselves as havinghigher or lower income should be taking into consideration. Also, when it comesto income, people who were not gainfully employed should be excluded from thestudy. This includes unemployed individuals who are receiving benefits,individuals who receive pensions or sickness benefits and also maternityallowances during a given year. Thelast constraint is using self-reported BMI. Self-reports underestimate theprevalence of obesity and obese individuals tend to underreport their BMI.

 Add Another SES VariableInfuture research, the SES category of occupational status can be added asanother independent variable. This category will include upper-white collaremployees, lower- white collar employees, manual workers, farmers, andself-employed individuals including entrepreneurs. This study sought toinvestigate only two variables when it came to socioeconomic status. Adding anothervariable will provide a more clearer picture of which individuals are obese andwhy.

 ConclusionAs theprevalence of obesity is rising in the United States, obesity was studied todetermine if SES had any direct correlation or causation to the matter. Indoing so, the hypothesis of higher SES individuals being less obese than lowerSES individuals was analyzed. The findings suggest that as income and educationlevels increased, obesity rates decreased. The more education achieved,nationwide, the lower obesity rates were.

As more information and data areadded to the regression models and from using the future directions provided, amore in depth understanding on obesity as it relates to SES status should begained. Furthermore, it will provide insight on why obesity is so prevalent andmaybe determine different ways it can be decreased. Overall, obesity is a veryimportant factor nationwide.     

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