Analysis of Obesity as a Clinical Problem - Essay Example

? Statistical Analysis of Obesity as a Clinical Problem group number member s Sample and Rationale Thewell being index (WBI) is a comprehensive measure of people’s quality of life that incorporates a number of different factors based on socio-economic conditions (Doll, Petersen, & Stewart-Brown, 2012). Research indicates that obesity is becomingly increasingly common especially in the developed world. Contemporary research indicates that obesity is affecting people not only in terms of their health but also in terms of their overall performance in society (Hernandez, 2011). The current study aims to look at obesity and chronic obesity causing conditions since these are emerging as major reasons for negatively affecting the well being of people. Data for the study has been acquired from Gallup-Healthway’s Well Being Index ® website from a survey conducted and compiled in 2009. Data was also acquired from the State Health Facts website for targeted states only. The data acquired from Gallup-Healthway was used to demarcate five states that formed the upper most and lowest tiers of obesity prevalence in the United States. The states of West Virginia, Mississippi and Kentucky exhibit the highest obesity rates while the states of Hawaii and Colorado displayed the lowest obesity rates (Mendes & McGeeney, 2012). The states were chosen in this order also because West Virginia, Mississippi and Kentucky are on the lowest rung of the WBI while Colorado and Hawaii are near the top of the WBI list. In addition to these statistics, three other variables were also used that include the population in poverty, the amount of uninsured kids and the amount of obese kids. Statistical Analysis Descriptive statistics were tabulated for the acquired data (shown in Appendix A). Gallup uses defined metrics in order to survey well being which can be listed as the Composite, Life Evaluation Index (LEI), Emotional Health Index (EHI), Work Environment Index (WEI), Physical Health Index (PHI), Healthy Behavior Index (HBI) and Basic Access Index (BAI) (Gallup-Healthways, 2009). The other variables used include the Population in Poverty (POP IN POV), uninsured kids and obese kids (State Health Facts, 2012). Results for the descriptive statistics are presented in the table shown below. Table 1 Descriptive Statistics WBI (Rank) State Health Fact (%) Descriptive Statistics for WBI and State Health Facts for the Nation Overall and the Five States Selected Descriptive Statistic COMPOSITE LEI EHI WEI PHI HBI BAI POP IN POV UNINSURED KIDS OBESE KIDS Mean 65.03 44.73 78.2 48.82 75.33 62.27 80.78 23.5 7.83 34.05 Median 64.95 44.55 78.5 48.9 75.7 62.15 81.1 23.5 8.5 33.55 Range 9.7 15.9 8.5 8.3 9.5 10.1 7.1 12 9 17.2 Standard Deviation 3.51 5.83 2.93 2.67 3.66 3.88 2.89 4.04 3.31 6.36 Standard Error 1.43 2.38 1.2 1.09 1.5 1.58 1.18 1.65 1.35 2.6 The mean and median for the Gallup data remain fairly close to each other for all reported metrics. In contrast, the data acquired from State Health shows some skewness for uninsured kids with the mean being 7.83 while the median is 8.5. The range for most variables being analyzed stays under 10 except for LEI (15.9), population in poverty (12) and obese kids (17.2). these variables could be expected to display larger standard deviations as well since the range of data is greater. In terms of the standard deviation, the highest value is displayed by obese kids (6.36) followed by LEI (5.83) while other variables display standard deviations of around 4. The standard error tabulation reveals similar results with LEI exhibiting a standard error of 2.38 and obese kids displaying a standard error of 2.6. In contrast, the standard error for population in poverty is 1.65 while other variables display standard errors of less than 1.6. Based on these results it could be safely assumed that the data acquired displays a near uniform distribution except for LEI and obese kids that tend to exhibit some skewness. Composite and domain scores by state as well as the national average are presented in the figure below. In terms of the composite index, Hawaii figures highest followed by Colorado while West Virginia figures lowest. A look at the overall range of statistical variables reveals that Hawaii is at the top of the list for all metrics tabulated by Gallup. Colorado tends to shadow Hawaii closely in most cases. However, the State Health Facts acquired variables reveal that Colorado has a favorable leading position for population in poverty and the amount of obese kids while Hawaii dominates the amount of uninsured kids. In contrast, West Virginia trails at the lowest end for most Gallup metrics. In contrast, Mississippi tops the amount of people living in poverty, the number of uninsured kids and obese kids. The relatively lower position of Hawaii for population in poverty and the relatively high position of Mississippi reveal that population under poverty may not be strongly related to WBI overall. Moreover, Mississippi displays the greatest number of people in poverty, number of obese kids and the greatest number of uninsured kids while it tends to score well on the Gallup statistics compared to West Virginia. This indicates that the Gallup statistics alone are not reliable enough to tabulate the WBI outcome since inconsistencies exist in both lineages of data. Figure 1 - Comparison of tabulated variables from state to state and with national means ANOVA was carried out in order to determine how the metrics of well being tended to differ along with the other variables used in the study with respect to the geographic placement of people. Statistics for the five states were included for the ANOVA but the national means were not used. The results of the ANOVA are shown in the table below. The results clearly reveal that there is no significant difference (F = 12.02, p = 4.34e-14) in the well being metrics and other variables with respect to geographic placement. The variance between groups was significantly larger (37,674.46) when compared to the variance within the groups (13,316.11) showing greater coherence of data within groups when compared to between groups. The ANOVA revealed that the critical F-value was 1.79 while the F-value was 12.02. The null hypothesis stands rejected given that the p value is much lower than 5% (p = 4.34e-14) providing a rejection confidence of 95%. This tends to indicate that domains of well-being as measured by the indexes of the well being index do not tend to differ as a function of where people live. Table 2 ANOVA (single factor) Source of Variation SS df MS F P-value F crit Between Groups 37674.46 16 2354.654 12.02427 4.34E-14 1.794556 Within Groups 13316.11 68 195.8252 Total 50990.58 84 Correlation between variables was also investigated to determine how variables were linked up with each other in the wider context. The analysis for correlation included all indexes from Gallup’s WBI and the State Health Facts acquired variables. The results of this analysis are shown in the table provided below. The correlation between indexes acquired from the Gallup surveys showed a high degree of correlation with the lowest correlation strength being between BAI and HBI at 0.82. In contrast, the highest correlation existed between PHI and LEI at 0.99 which shows a very strong relationship between these indexes. The variables gained from the State Health Facts data showed weak and even negative correlation to the basic Gallup indexes for WBI. The results indicate that LEI is weakly negatively correlated to population in poverty and with uninsured kids at -0.15 while LEI is more moderately negatively correlated to obese kids. This implies weak and only causal relationships between these sets of variables. On the other hand, population in poverty displays a weak relationship with EHI at 0.24 while uninsured kids (-0.24) and obese kids (-0.45) are weakly negatively correlated to EHI. The connection between WEI and population in poverty is very meager (0.06) leading to a nonexistent relationship between both variables. In contrast, WEI is weakly negatively correlated to both uninsured kids (-0.12) and obese kids (-0.44). PHI denotes weakly negative correlation to both population in poverty (-0.09) and uninsured kids (-0.11) while it displays a moderately negative correlation to obese kids (-0.61). HBI shows a similar trend since it is only weakly correlated to both population in poverty (-0.16) and uninsured kids (-0.25) while it is more moderately negatively correlated to obese kids (-0.67). In contrast, BAI displays moderately negative relationships with both population under poverty (-0.40) and uninsured kids (-0.38) while it has a strong negative correlation to obese kids (-0.89). On the other hand, uninsured kids was related to population in poverty moderately positively (0.34) while obese kids was connected more strongly (0.69). Uninsured kids was related to obese kids only moderately at 0.5. Figure 2 - Correlation matrix scatter plot Table 3 Correlation Matrix for WBI and State Health Facts for the Nation Overall and the Five States Selected     LEI EHI WEI PHI HBI BAI POP IN POV UNINSURED KIDS OBESE KIDS LEI                   EHI 0.93     WEI 0.93 0.93     PHI 0.99 0.94 0.90     HBI 0.96 0.92 0.83 0.98     BAI 0.86 0.74 0.79 0.83 0.82     POP IN POV -0.15 0.15 0.06 -0.09 -0.16 -0.40     UNINSURED KIDS -0.15 -0.24 -0.12 -0.11 -0.25 -0.38 0.34     OBESE KIDS -0.64 -0.45 -0.44 -0.61 -0.67 -0.89 0.69 0.50   Conclusions The results of this investigation make it clear that people’s location across the United States is not a function of various indexes used to tabulate the well being index. In addition, other variables such as population in poverty, uninsured kids and obese kids are also not strongly tied up to the geographic location of such population samples. This leads to the conclusion that geography alone is not to blame for the overall well being index of an area but other factors such as social setup, economic well being and other similar factors may also play a role in determining the well being index. The correlation plot and matrix clearly revealed that statistics gathered from Gallup were strongly correlated to each other while statistics gathered from State Health Facts were moderately correlated to each other. In terms of correlation between groups, these factors were weakly or negatively correlated to each other. This implies that population in poverty, uninsured kids and obese kids alone are not able to incorporate causes for obesity into the well being index. It is suggested that further work in this regard should incorporate other factors that compensate for socio-economic factors to provide a more comprehensive model for relating obesity and well being. Bibliography Doll, H. A., Petersen, S. E., & Stewart-Brown, S. L. (2012). Obesity and Physical and Emotional Well-Being: Associations between Body Mass Index, Chronic Illness, and the Physical and Mental Components of the SF-36 Questionnaire. North American Association for the Study of Obesity 8(2) , 160-170. Gallup-Healthways. (2012). 2009 WBI Aggregate by State. Retrieved October 6, 2012, from Gallup-Healthways: http://wbi.meyouhealth.com/files/2009-wbi-aggregate-by-state.zip Gallup-Healthways. (2009). Gallup-Healthways Well Being Index: Methodology Report for Indexes. Retrieved October 6, 2012, from Gallup-Healthways: http://wbi.meyouhealth.com/files/GallupHealthwaysWBI-Methodology.pdf Gallup-Healthways. (2012). Home. Retrieved October 6, 2012, from Gallup-Healthways: http://www.well-beingindex.com/ Hernandez, D. J. (2011). Declining Fortunes of Children in Middle-Class Families: Economic Inequality and Child Well-Being in the 21st Century. FCD Child and Youth Well-Being Index (CWI) Policy Brief 2011. New York: Education Resources Information Center. Mendes, E., & McGeeney, K. (2012, August 16). In U.S., Majority Overweight or Obese in All 50 States. Retrieved October 6, 2012, from Gallup: http://www.gallup.com/poll/156707/majority-overweight-obese-states.aspx?ref=more State Health Facts. (2012). Home. Retrieved October 6, 2012, from State Health Facts: http://www.statehealthfacts.org/ Appendix A Well-Being Indexes (WBI) and State Health Facts for the Nation Overall and the Five States Selected WBI (Rank) State Health Fact (%) STATE COMPOSITE LEI EHI WEI PHI HBI BAI POP IN POV UNINSURED KIDS OBESE KIDS Hawaii 70.2 (1) 52.7 (1) 83.1 (1) 52.7 (5) 80.1 (1) 67.8 (2) 84.4 (9) 24 3 28.5 Colorado 67.3 (7) 49.4 (6) 78.6 (25) 49.7 (21) 78.1 (7) 65.2 (11) 83 (20) 17 9 27.2 Mississippi 64 (43) 43.3 (36) 78.4 (29) 48.7 (28) 74.8 (44) 61.2 (38) 77.3 (50) 29 12 44.4 Kentucky 62.3(49) 40.4 (47) 75.8 (49) 48.3 (32) 71.8 (49) 57.7 (50) 80 (39) 23 8 37.1 West Virginia 60.5 (50) 36.8 (50) 74.6 (50) 44.4 (47) 70.6 (50) 58.6 (47) 77.8 (48) 22 5 35.5 NATION 65.9 45.8 78.7 49.1 76.6 63.1 82.2 26 10 31.6 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Composite 5 324.3 64.86 15.193 5 150 30 575 LEI 5 222.6 44.52 42.197 5 140 28 530.5 EHI 5 390.5 78.1 10.72 5 154 30.8 406.2 Wei 5 243.8 48.76 8.908 5 133 26.6 236.3 PHI 5 375.4 75.08 16.307 5 151 30.2 581.7 HBI 5 310.5 62.1 18.63 5 148 29.6 474.3 BAI 5 402.5 80.5 9.81 5 166 33.2 323.7 Population in Poverty 5 115 23 18.5 Uninsured Kids 5 37 7.4 12.3 Obese Kids 5 172.7 34.54 48.763 Read More