Study Design: This is an experimental study design that involves a randomized control trial. This is because it involves simple random sampling of subjects to study a certain trait (obesity).
Study Units are patients visiting the physician.
The outcome of Interest: Risk factors for Obesity
Can causation be determined-At face value it is difficult to determine causation for obesity. From data analyzed from a sufficiently large sample size, this can be well determined. The outcome of interest can be caused by different factors that vary from one individual to the other.
Prevalence of Obesity
According to Rosner (2015),Prevalence of Obesity=number of cases/population *100=1311/5056=0.2593:
25.93% prevalence
95% Confidence level for prevalence
The confidence interval formula is;
Considering (prevalence) =0.2593; C.I =0.2593 1.96
C.I =0.2593 1.96(0.006163)
C.I =0.2593 0.01208
Confidence interval is [0.2472 and 0.2714]
The confidence interval of prevalence lies bewteen 24.72% to 27.14%.
Table 1: Obesity Risk Factors
| Variables | Description | Obese | Not Obese | Overall |
| sex | 1 – male | 549 | 1382 | 1931 |
| 0 – female | 762 | 2363 | 3125 | |
| Total | 1311 | 3745 | 5056 | |
| smoke | 0 – never | 925 | 2636 | 3561 |
| 1 – former | 229 | 662 | 891 | |
| 2 – current | 157 | 447 | 604 | |
| Total | 1311 | 3745 | 5056 | |
| Drinking Status | 0 – no | 756 | 2209 | 2965 |
| 1 – yes | 555 | 1536 | 2091 | |
| Total | 1311 | 3745 | 5056 | |
| Coffee Intake | 0 | 108 | 425 | 533 |
| 1 | 413 | 1019 | 1432 | |
| 2 | 523 | 1554 | 2077 | |
| 3 | 204 | 651 | 855 | |
| 4 | 63 | 96 | 159 | |
| Total | 1311 | 3745 | 5056 | |
| Calories | 897-1896 | 995 | 3034 | 4029 |
| 1897-2896 | 307 | 698 | 1005 | |
| 2897-3896 | 9 | 11 | 20 | |
| 3897-4896 | 2 | 2 | ||
| Total | 1311 | 3745 | 5056 | |
| Age | 23-37 | 43 | 306 | 349 |
| 38-52 | 1073 | 2709 | 3782 | |
| 53-67 | 195 | 729 | 924 | |
| 68-82 | 1 | 1 | ||
| Total | 1311 | 3745 | 5056 | |
| Glucose | 51-100 | 304 | 710 | 1014 |
| 101-150 | 934 | 2695 | 3629 | |
| 151-200 | 73 | 338 | 411 | |
| 201-250 | 2 | 2 | ||
| Total | 1311 | 3745 | 5056 | |
| Race | 1 – African-American | 265 | 643 | 908 |
| 2 – Hispanic | 413 | 1261 | 1674 | |
| 3 – White | 599 | 1749 | 2348 | |
| 4 – Other | 34 | 92 | 126 | |
| Total | 1311 | 3745 | 5056 |
Determining if the variables above are risk factors for obesity.
The variables involved in the above table are grouped. Most
are nominal and possess qualitative characteristics. To test if the above
variables are associated with obesity, statistical analysis was carried out
using chi-squared method for the observation of the values. The alpha value is
considered to be 0.05.
a. The null and alternative hypothesis will be written as follows:
1. Null hypothesis (H0): There is no association between respondent sex and obesity.
2. The alternative hypothesis (H1): There is an association between respondent sex and obesity.
Table 2: Sex Chi-Square Test
| Value | of | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | |
| Pearson Chi-Square | 10.177a | 1 | .001 | |
| Continuity Correctionb | 9.967 | 1 | .002 | |
| Likelihood Ratio | 10.105 | 1 | .001 | |
| Fisher’s Exact Test | .002 | |||
| Linear-by-Linear Association | 10.175 | 1 | .001 | |
| N of Valid Cases | 5056 |
From the table above, the Chi-square value is 10.18 and its associated P value is 0.002. Considering that the p-value is less than the alpha value of .05, there exists a statistically significant difference between gender distribution and the risk of obesity. Therefore, the null hypothesis is rejected and the conclusion made that the sex of an individual is a variable that is associated with obesity.
b. The null and alternative hypothesis will be written as follows:
3. Null hypothesis (H0): There is no association between respondent smoking behavior and obesity.
4. The alternative hypothesis (H1): There is an association between respondent smoking behavior and obesity.
Table 3: Smoking Chi-Square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | .029a | 2 | .985 |
| Likelihood Ratio | 0.029 | 2 | .985 |
| Linear-by-Linear Association | 0.003 | 1 | .953 |
| N of Valid Cases | 5056 |
From the table above, the Chi-square value is 0.029 and its associated P value is 0.985. Considering that the p-value is greater than the alpha value of .05, there exists no statistically significant difference between the smoking behavior and risk of obesity. Therefore, the null hypothesis is accepted and the conclusion made that smoking behavior is not a risk factor of obesity.
c. The null and alternative hypothesis will be written as follows:
5. Null hypothesis (H0): There is no association between respondent drinking behavior and obesity.
6. The alternative hypothesis (H1): There is an association between respondent drinking behavior and obesity.
Table 4: Drinking Chi-Square Test
| Value | df | Asymp. Sig. (2-sided) | Exact Sig. (2-sided) | |
| Pearson Chi-Square | .697a | 1 | .404 | |
| Continuity Correctionb | 0.644 | 1 | .422 | |
| Likelihood Ratio | 0.696 | 1 | .404 | |
| Fisher’s Exact Test | .415 | |||
| Linear-by-Linear Association | 0.697 | 1 | .404 | |
| N of Valid Cases | 5056 |
From the table above, the Chi-square value is 0.697 and its associated P value is 0.415. Considering that the p-value is greater than the alpha value of .05, there exists no statistically significant difference between the drinking behavior and risk of obesity. Therefore, the null hypothesis is accepted and the conclusion made that drinking behavior cannot be associated with obesity.
d. The null and alternative hypothesis will be written as follows:
7. Null hypothesis (H0): There is no association between coffee uptake and obesity.
8. The alternative hypothesis (H1): There is an association between coffee uptake and obesity
Table 5 Coffee Chi-Square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | 33.268a | 4 | .000 |
| Likelihood Ratio | 32.305 | 4 | .000 |
| Linear-by-Linear Association | 2.196 | 1 | .138 |
| N of Valid Cases | 5056 |
The results above show a Chi-square value is 33.27 and its associated P value is 0.00. Considering that the p-value is less than the alpha value of .05, there exists a statistically significant difference between the coffee uptake and the risk of obesity. Therefore, the null hypothesis is rejected and the conclusion made that coffee uptake (number of cups) is a variable that is associated with obesity.
e. The null and alternative hypothesis will be written as follows:
9. Null hypothesis (H0): There is no association between race and obesity.
10. The alternative hypothesis (H1): There is an association between race and obesity
Table 6: Race Chi-Square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | 6.677a | 3 | .083 |
| Likelihood Ratio | 6.565 | 3 | .087 |
| Linear-by-Linear Association | 2.199 | 1 | .138 |
| N of Valid Cases | 5056 |
The analyzed results above show a Chi-square value is 6.68 and its associated P value is 0.083. Considering that the p-value is greater than the alpha value of .05, there exists no statistically significant difference between an individual’s race and risk of obesity. Therefore, the null hypothesis is accepted and the conclusion made that race cannot be associated with obesity. However, this result is significant at 90% confidence level.
f. The null and alternative hypothesis will be written as follows:
11. Null hypothesis (H0): There is no association between age and obesity.
12. The alternative hypothesis (H1): There is an association between age and obesity
Table 7: Age Chi-Square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | 93.149a | 43 | .000 |
| Likelihood Ratio | 108.195 | 43 | .000 |
| Linear-by-Linear Association | 1.377 | 1 | .241 |
| N of Valid Cases | 5056 |
The results above show a Chi-square value is 93.15 and its associated P value is 0.000. Considering that the p-value is less than the alpha value of .05, there exists a statistically significant difference between the age of an individual and the risk of obesity. Therefore, the null hypothesis is rejected and the conclusion made that the age of an individual is a variable that is associated with obesity.
g. The null and alternative hypothesis will be written as follows:
13. Null hypothesis (H0): There is no association between glucose and obesity.
14. The alternative hypothesis (H1): There is an association between glucose levels and obesity
Table 8: Glucose Chi-square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | 167.344a | 143 | .080 |
| Likelihood Ratio | 191.142 | 143 | .004 |
| Linear-by-Linear Association | 40.745 | 1 | .000 |
| N of Valid Cases | 5056 |
The analyzed results above show a Chi-square value is 167.34 and its associated P value is 0.08. Considering that the p-value is greater than the alpha value of .05, there exists no statistically significant difference between an individual’s glucose levels and risk of obesity. Therefore, the null hypothesis is accepted and the conclusion made that glucose cannot be associated with obesity. However, this result is significant at 90% confidence level.
h. The null and alternative hypothesis will be written as follows:
13. Null hypothesis (H0): There is no association between calories and obesity.
14. The alternative hypothesis (H1): There is an association between calories levels and obesity
Table 8: Calories Chi-square Test
| Value | df | Asymp. Sig. (2-sided) | |
| Pearson Chi-Square | 147.35a | 143 | .000 |
| Likelihood Ratio | 138.14 | 143 | .004 |
| Linear-by-Linear Association | 60.84 | 1 | .000 |
| N of Valid Cases | 5056 |
The results above show a Chi-square value is 147.2=35 and its associated P value is 0.00. Considering that the p-value is less than the alpha value of .05, there exists a statistically significant difference between the calories levels and the risk of obesity. Therefore, the null hypothesis is rejected and the conclusion made that calories levels are associated with obesity.
In conclusion, the variables sex, coffee uptake, age and calories levels were found to be highly associated with obesity condition. Therefore, these are risk factors that require to be observed to ensure one is not overweight.
References
Rosner, B., 2015. Fundamentals of biostatistics. Nelson Education.
