Socioeconomic Factors and Prevalence of Diabetes
Incidents of diabetes have been rising among US residents. The late-onset diabetes is associated with changes in lifestyle, and more so lack of physical exercise and obesity. These risk factors vary by region, with most urban centers being the worst affected by the pandemic. In most cases, high education attainment results in rising incomes that, in turn, predispose people to a sedentary lifestyle (US Center for Disease Control and Prevention (CDC), 2017). An analysis of these socioeconomic factors by region indicates that obesity and poverty rate are the most significant determinants of diabetes prevalence, and this discovery should inform the pricing of health insurance in the respective states.
Regional Prevalence and Risk Factors
The Western region of the United States has an average prevalence of 25.83%. The median age of the 13 states is 36.73 (American FactFinder (AFF), 2018), which is one of the youngest in the country. A correlation matrix for this area shows that obesity has the highest correlation with the prevalence of diabetes. The poverty rate, or the percentage of households that earn less than $25,926 per day (US Census Bureau, 2019), also showed a strong correlation with the prevalence of diabetes as seen in Table 1. Don't use plagiarised sources.Get your custom essay just from $11/page
Table 1.
Correlation Matrix for the Western Region
Median income
High school or higher(%)
Obese Population (%)
Poverty Rate(%)
Diabetes (%)
Age_Adj prev
Blood Pressure
Median income
1
High school or higher(%)
0.33
1.00
Obese Population (%)
-0.48
-0.07
1.00
Poverty Rate(%)
-0.67
-0.54
0.61
1.00
Diabetes (%)
-0.43
0.02
0.90
0.50
1.00
Age_Adj prev
0.14
0.10
0.03
-0.29
-0.07
1.00
Blood Pressure
-0.44
-0.17
0.62
0.64
0.56
-0.09
1.00
In contrast, the level of income showed a strong negative correlation with diabetes prevalence. Besides, the median age indicated a small negative correlation with diabetes prevalence. Therefore, the cost of healthcare insurance should be higher for the obese, and the poor should be given subsidies to afford coverage.
In the Midwestern region, the obesity rate had a strong influence on the prevalence of diabetes. However, it had a lower correlation than the western part. Similarly, the poverty rate had a positive but lower correlation compared to the western region as shown in Table 2.
Table 2
Correlation Matrix for the Midwestern Region
Median income
High school or higher(%)
Obese Population (%)
Poverty Rate(%)
Diabetes (%)
Age_Adj prev
Blood Pressure
Median income
1.00
High school or higher(%)
0.49
1.00
Obese Population (%)
-0.73
-0.40
1.00
Poverty Rate(%)
-0.59
-0.44
0.52
1.00
Diabetes (%)
-0.77
-0.50
0.73
0.36
1.00
Age_Adj prev
0.01
0.06
-0.06
-0.47
0.01
1.00
Blood Pressure
-0.64
-0.75
0.53
0.62
0.75
-0.28
1.00
In contrast, median income and the educational attainment exhibited a stronger negative correlation compared to the western region. Therefore, investing in education and creating better job opportunities would reduce the prevalence of diabetes in the region.
In the Southern region, obesity had the second highest correlation with the prevalence of diabetes. In addition, the poverty rate had the highest positive correlation with incidences of diabetes as shown in Table 3.
Table 3
Correlation Matrix for the Southern Region
Median income
High school or higher(%)
Obese Population (%)
Poverty Rate(%)
Diabetes (%)
Age_Adj prev
Blood Pressure
Median income
1.00
High school or higher(%)
0.72
1.00
Obese Population (%)
-0.67
-0.73
1.00
Poverty Rate(%)
-0.87
-0.75
0.67
1.00
Diabetes (%)
-0.68
-0.61
0.91
0.55
1.00
Age_Adj prev
-0.29
-0.37
0.38
0.34
0.20
1.00
Blood Pressure
-0.74
-0.54
0.83
0.63
0.86
0.37
1.00
The region had the second lowest median income and the second highest level of educational attainment. Therefore, insurance premiums for this section should have a higher premium based on the higher prevalence of obesity and high educational attainment.
In the Northeastern region, obesity exhibited the highest correlation with the percentage of the obese population. Unlike other areas, poverty had a low correlation, which indicates the lack of close variation between these variables as shown in Table 4.
Table 4.
Correlation matrix for the Northeastern region
Median income
High school or higher(%)
Obese Population (%)
Poverty Rate(%)
Diabetes (%)
Age_Adj prev
Blood Pressure
Median income
1
High school or higher(%)
0.05
1.00
Obese Population (%)
-0.65
0.19
1.00
Poverty Rate(%)
-0.64
-0.64
0.14
1.00
Diabetes (%)
-0.54
-0.03
0.94
0.10
1.00
Age_Adj prev
-0.40
-0.11
0.23
0.30
0.18
1.00
Blood Pressure
-0.56
-0.10
0.73
0.15
0.78
0.03
1
The level of median income had a negative correlation with the prevalence of diabetes, which was lower than that of other regions. Therefore, insurance policies should focus on obesity in pricing more than other factors.
Obesity and poverty rate are the most significant determinants of diabetes prevalence in all regions. Therefore, insurance firms should consider this finding as a significant factor in the pricing of health coverage. Further, the companies should regard poverty rates and educational attainment as major contributors to late-onset diabetes.