County Behavioral Risk Factors’ Effect on Diabetes Prevalence Statistics Project; Research on...

7/30/2019 County Behavioral Risk Factors Effect on Diabetes Prevalence Statistics Project; Research on diabetes in Arkansas

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County Behavioral Risk Factors Effect on Diabetes Prevalence

Introduction

Diabetes is among the most terrible diseases that affects humans. It is incurable, and

although it can be controlled, the treatment is expensive and a burden for the whole patients

family. Victims of diabetes typically become dependent on shots or other medication, and they

have to change the way they live for the rest of their lives. According to the Centers for Disease

Control and Prevention (CDC), a total of 25.8 million people, or 8.3% of the U.S. population

have diabetes (2011). This debilitating disease is also the seventh leading cause of death in the

United States. It is considered one of the fastest growing diseases in America, as it affects

nearly 2 million new patients each year. Diagnosis and treatment of diabetes costs the United

States Health Care System $218 billion dollars annually, including treatment and patient

disability payments (ADA). Other than treatment and disability payments by the US

government, diabetes also keeps patients from working, which further hinders the economy by

about $40 billion dollars in 2006 (ADH). The mortality rate of diabetes and its expense to the

U.S. government make it important to understand what diabetes is, what causes it, and what

kinds of activities relate with it.

Arkansas has a prevalence of diabetes in adults of 10.1%, which is relatively high

compared to the United States average of 8.3 (CDC, 2011). A study in Arkansas showed

diabetes is slightly more common in males (10%) than females (9%) and in Blacks (12%) than

Whites (9%) (ADH, 2008). Among a study comparing 33 states, Arkansas ranked in the top third

for highest incidence of diabetes (MMWR, 2008).


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Understanding Diabetes and some Related Factors

First, it is important to understand the nature of diabetes. According to CDC (2011),

diabetes is a group of diseases marked by high levels of glucose in the blood. This occurs when

there is a deficient production of insulin, a hormone produced by the pancreas, or when the

body cannot use its own insulin as well as it should. This hormone helps glucose get into the

cells of our bodies. Diabetes ultimately generates complications such as heart disease,

blindness, kidney failure, and lower-extremity pain and amputations.

A holistic approach to determine leading causes of diabetes may provide insight to

decrease diabetes prevalence, which would in turn decrease the cost of treatment and

diagnosis of diabetes for United States Health Care. Though it is well known that several factors

may be associated with diabetes, analyzing the aspects of ones health that studies support

most frequently relating to diabetes may provide insight for preventative care. These factors

alone or in conjunction with each other may also be used as predictors for diabetes prevalence.

This research paper analyzes the incidence of diabetes in the state of Arkansas by separating

data by counties. Data concerning health-related activities, such as high blood cholesterol,

smoking, obesity, physical activity, lack of a personal doctor, and lack of health insurance from

each Arkansas county (retrieved from the Arkansas Department of Health county health

estimates) will be used in our analysis to conduct a regression analysis. The above health

factors will be used as dependent variables in a regression analysis to determine their

relationship with the percentages of diabetes prevalence in each of those counties. This


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assessment will ultimately provide insight to the risk factors associated with diabetes to allow

its predictability. The information from this analysis can also help define what activities should

and should not be encouraged and provide information concerning the distribution of health-

related resources among counties in Arkansas (ADH, 2011).

For the linear regression analysis in this study, each of the variables is expressed in

percent frequency of the condition found in each county in Arkansas. The dependent variable

(Y) is the presence of diabetes for that county and is defined by the percentage residents of

that county answering yes to having been told by a doctor that you have diabetes. The first of

six independent variables is prevalence of high blood cholesterol (X1). For this study, this

variable is defined as the percentages of residents of that county answering yes to having been

told by a doctor that your blood cholesterol is high. The second independent variable (X2) is

the frequency of smokers in each county. It is defined as the percentage of those who

answered yes to having smoked at least 100 cigarettes in your entire life and smoking

cigarettes every day or some days. The third independent variable (X3) is the frequency of

obesity. Residents were asked their weight and height without shoes, and their BMI (weight in

pounds divided by the squared height in inches) was calculated. Those with BMI greater than 30

are considered obese. The fourth independent variable (X4) is the percentage of residents of

each county that regularly exercises moderately. This is operationally defined as those who

partake in 30 minutes of moderate physical activity for 5 or more days a week. The fifth

independent variable (X5) is the percentage of people in each county that do not have a

personal doctor. This was calculated by a simply response to whether or not each participant

considered him or herself to have a personal doctor. The sixth independent variable (X6) is the


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percentage of residents who do not have health insurance. This was determined by whether or

not each resident answered no to having no kind of health care coverage.

Considering these variables, this regression analysis hypothesizes that high blood

cholesterol, smoking, obesity, lack of a personal doctor, and lack of health insurance will all be

positively related to higher prevalence rates of diabetes. Also, this study hypothesizes that

moderate physical activity will negatively associate with prevalence rates of diabetes. The null

hypothesis is that these variables do not have a correlation with diabetes, so that their

coefficients in the regression analysis will be zero.

Literature Survey

Though studies support numerous contributing factors to diabetes, high blood

cholesterol, smoking, obesity, lack of physical activity, and lack of a personal doctor are all

among the most highly researched health-related contributors to diabetes. Though Type 1 and

Type 2 diabetes may have different risk factors, due to the nature of the data for this analysis,

the two subtypes of diabetes will simply be considered under the broad category of diabetes. A

study by Mokdad et al. (2003) determined that obesity and diabetes were not only significantly

correlated but that a person with obesity is seven times more likely (p < .05) to have diabetes.

High blood cholesterol has also been supported to as a risk factor for diabetes (Perry et al.,

1995; Ravid, Brosh, Ravid-Safran, Levy, & Rachmani, 1998) Another study showed that the most

influential health risk factor was body mass index (BMI) (Perry et al., 1995). Perry and his

colleagues determined that men who had a BMI in the top fifth percentile (greater than 27.9)

were more likely to develop diabetes than men in the bottom fifth percentile by 11-fold (p


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.00001). Note, however, that this study was only concerning men. This study also supported

that men who participated in moderate physical activity were less likely to develop diabetes (p

< .05) (Perry et al., 1995). Whether smoking was a risk factor was trending but not significant (p

= .06) (Perry et al., 1995). Other studies (Ravid et al., 1998) support that smoking is significantly

correlated with diabetes and may possibly be a risk factor.

There are several possible explanations for the increased frequency of diabetes in

Arkansas. Arkansas ranks low in several aspects across the country. The states per capita

income is $30,060, which is ranked 48th

only above West Virginia ($29,537) and Mississippi

($28,845), respectively (U.S. Census Bureau, 2007). Low socioeconomic status has been shown

to positively correlate with diabetes (Ravid et al., 1998). Arkansas also has higher levels of

increased blood cholesterol (38.7%) compared to the United States as a whole (37.5%) as well

as higher rates of obesity (31.9%) compared to 26.5% in the U.S. As described above, having

higher levels of blood cholesterol or obesity are also positively correlated with diabetes.

Arkansas ranks 50th

(out of the 50 states and Washington DC) for percentage of adult residents

who have a college degree with a frequency of 18.8% followed only by West Virginia (Education

Statistics, 2004). Having a college degree is significantly correlated with not having diabetes

(ADH, 2008). Adults without a high school degree have a chance of 14% of having diabetes

while that percentage drops to 6% in those with a college degree (ADH, 2008).


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Data Analysis

The state maps below show a general correlation between several health factors and

diabetes even before a regression analysis. On these maps, the percentage of residents with

that particular health characteristic goes up as the darkness of green increases. The darkest

shade of green represents a county that has 13.3% to 17.9% of residents with that particular

health characteristic. In nearly all of the maps, it is clear that the counties towards the outside

have higher percentages of the factors especially concerning no personal doctor and lack of

health insurance. With some exceptions, the north east and south west areas of the state seem

0 5 10 15 20 25 30 35 40 45

Diabetes

High Blood Cholesterol

Smoking

Obesity

Partakes in Moderate Activity

Lack of a Personal Doctor

Lack of Health Insurance

Percentage

Relative Frequencies of

Risk Factors in Arkansas


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to show the highest percentage of the factors. Diabetes, lack of health insurance, and obesity

seem to show the clearest presence of high frequency in the north east and south west. It also

seems evident from the graph that the counties that have higher rates of residents with regular

physical exercise are in the North West and south each corners of the state. This supports our

initial hypothesis that physical activity will be negatively related to diabetes prevalence. This

provides insight to our linear regression. Though some factors, especially percentage of

residents who smoke, do not match with the darker counties on the diabetes map, many

factors, such obesity show more synchronicity with diabetes. Some factors, such as high blood

cholesterol, are not as clear though they may show significance with the linear regression

analysis. Further analysis with the regression analysis will provide more exact comparisons

between the factors.


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The following graphs represent the percentage of adults (18 years or older) in Arkansas, with

certain health-related characteristics.

A simple analysis of the map above shows that North East and South West Arkansas

have the greatest percentage of people with diabetes. Diabetes in these areas seems uniformly

above 13.3%. All counties in the North West and South East corners Arkansas show under a

8.9% diabetes rate.


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The map above shows that the highest percentage of people with high cholesterol is

distributed between Northern Eastern and South Eastern counties of Arkansas. These counties

have above a 46.3% rate of high blood cholesterol. Counties in Western Arkansas show the

lowest percentages of high blood cholesterol.


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The map above shows a greater percentage of people who smoke in the Eastern and

Western parts of Arkansas. Unlike the other maps, this map shows few states, which are found

in the Central and Southern areas of Arkansas, have low percentages. Overall, the North

Eastern part of the state has the lowest prevalence of smoking.


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The graph above shows a larger percentage (above 38.1%) of people who are

obese in the Eastern counties of Arkansas; however, there are three counties in the west that

have a high percentage of obesity as well. Counties in the North West and throughout the

Central area of the state show lower percentages of residents with obesity.


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This map illustrates that the highest percentage of residents who meet the

physical activity standard in Arkansas is primarily in the North Eastern and North Central areas

as well as Southern Arkansas. Medium percentages are present within the Central areas of the

state, and the lowest percentages are seen in the South Western and far Eastern counties.


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This map shows that the highest percentage of people without a personal doctor is in the

Western counties of Arkansas. Three counties in Eastern Arkansas also show the highest level,

which is defined as a percentage of residents without a personal doctor between 20.7% and

40.6%.


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This map shows that the counties with the highest percentage of not having health

insurance is greatly dispersed throughout the state. Counties in the East and West seem to,

overall, show higher percentages of not having health insurance. Most clearly, the map shows

that counties in Central Arkansas have the lowest percentage of people without health

insurance.

Regression Analysis

A first regression analysis of the data used the six previously explained risk factors for

diabetes. For this analysis, a multiple regression model for diabetes percentage was created as

, such that =prevalence of high blood

cholesterol, =prevalence of smoking, =prevalence of obesity, =prevalence of lack of


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physical activity, =prevalence of lack of personal doctor, and =prevalence of lack of health

insurance at a 95 percent confidence level. The analysis showed that these variables account

for 49.9% of the variability (F (6,74) = 11.29,p = .000) in the prevalence of diabetes. Using the

adjusted multiple coefficient of determination for 6 dependent variables, these risk factors

account for 45.4% of the variability in diabetes prevalence. However, the analysis showed a

non-significant relationship between the dependent variable (the percentage of people with

diabetes in each county) and three of the risk factors, including high blood cholesterol (p-value

= 0.1640), lack of personal doctor (p-value = 0.1208), and lack of health insurance (p-value =

0.9827). Because thep-values for these three factors were not significant, the null hypothesis

for these variables must be accepted; therefore, for these three factors , , ,

. This means that according to this first regression model, high blood cholesterol, lack of a

personal doctor, and lack of health insurance do not contribute in predicting the prevalence of

diabetes.

Because half of the risk factors did not significantly correlate with diabetes, second

regression analysis included only four independent variables: prevalence of smoking, obesity,

and residents who partake in regular moderate activity for each county. The second analysis

used the regression model , such that =prevalence of

smoking, =prevalence of obesity, =prevalence of lack of physical activity, and

=prevalence of lack of personal doctor. This regression was also calculated at a 95%

confidence level. Using this modified regression, the three variables accounted for 48.5% of the

variation (F (4,74) = 16.45,p = .000) in the prevalence rate of diabetes. Adjusted, these

variables account for 45.5% of the variability in diabetes prevalence.


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The modified regression analysis indicated that there is a negative relationship, such

that = -.176,p = .002, between the percentage of smokers and the percentage of people with

diabetes. Though the literature review and our original hypothesis suggested that smoking may

be positively related to diabetes, the initial assessment using the Arkansas risk factors maps

indicated that smoking and diabetes may be negatively related because for diabetes the North

East and South West had the highest percentage and for diabetes the opposite two corners had

higher rates of smoking. One possible explanation for this is that doctors heavily pressure

patients with diabetes to stop smoking. The nicotine in cigarettes causes patients with diabetes

to develop more blindness as well as more heart and kidney problems than patients who do not

have diabetes (Park, 2011). The heightened complications in diabetic patients who smoke leads

doctors to try and prevent them from smoking. For this reason, the relationship should be

interpreted as the more people with diabetes the less people who smoke and not the more

people who smoke the less people with diabetes.

The second independent variable of our study, percentage of each countys residents

with obesity, has a positive relationship, such that = .159,p = .000, with the percentage of

people with diabetes. The positive coefficient for the effect of obesity in the regression model

correlates with the hypothesis and the other studies in the literature review. Because this

regression only determines a correlation, it cannot be determined which of these health risks

causes the others or if some third variable causes both of them to be correlated. One

explanation is that being obese causes diabetes. Another explanation is that having diabetes

causes insulin problems, which causes obesity.


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The analysis also indicates a negative relationship, such that = -.235,p = .000,

between the percentage of people involved in moderate physical activities and diabetes. This

relationship matches with both the hypothesis and with the information provided in the

literature review. Because this regression does not imply a cause-and-effect relationship, there

are two possible explanations for the negative relationship between physical activity and

diabetes. One explanation is that the more physical activities a person does, the less likely he or

she will get diabetes. The second explanation could be that those with diabetes have more pain

and heart problems and cannot participate in as much physical activity.

The final risk factor, the percentage of people that lack a personal doctor, did not show

to be a predicting value for diabetes in this regression model though it may be trending towards

significance (p = .09). Though non-significance means accepting the null that = 0, for the

purposes of this analysis it may be important to explain the outcome. Though not significant,

the coefficient for indicates a trend towards a negative relationship between having a

personal doctor and having diabetes. This could be explained by a general understanding of

proper health in people who have a personal doctor. A doctor could provide information to

reduce certain risk factors that may cause diabetes to prevent diabetes before it develops. A

larger sample may have provided a significant influence for this risk factor on diabetes.

It is important to note why the coefficients for the independent variables that have

significance may seem small. The county data estimates each have a small standard deviation

between the counties and the mean of the prevalence for each risk factor for the whole state.

The average standard error of the mean for prevalence of diabetes, high blood cholesterol,


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smoking, obesity, lack of physical activity, lack of personal doctor, and lack of health insurance

are 0.6, 1.1, 1.0, 1.1, 1.2, 1.1, and 1.1, respectively. The low variations in the values may be

because the data was taken from one state. This state, though it clearly varies in each of its

areas, has similarities in lifestyles, which could make the variation in the variables small.

Conclusion

This analysis suggests that three important health factors may contribute to predicting

diabetes rates: prevalence of smoking, prevalence of obesity, and prevalence of physical

activity. Because it does not seem logical to imply that encouraging people to smoke will reduce

their chance of developing diabetes, the other two factors may be more important. The factor

that had the greatest correlation was physical activity. This implies that promoting physical

activities will help reducing the percentage of people with diabetes. Further, encouraging a

healthy diet and creating campaigns that promote healthy lifestyle could possibly decrease the

number people with obesity, which correlates positively with diabetes. Information from this

analysis can be used to educate Arkansas about the potential risk factors related to diabetes.

Also, this study can be used as a basis for distributing resources and investments in the

prevention of diabetes in the state of Arkansas. Programs to help decrease obesity and increase

physical exercise will ultimately save money in treatments for people with this disease. After

implementing certain programs to reduce diabetes, a future study could determine if diabetes

rates decreased among certain counties more than others. Furthermore, a more accurate

analysis could be obtained using more participants and including participants from the whole

country. A more holistic study could include factors, such as family genetics, per capita income


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for each county, types of food typically eaten by residents, or age of onset of diabetes. Because

diabetes rates have been increasing in Arkansas since 2000 and patients with diabetes have

twice the risk of death than those without diabetes (ADH, 2001), it is important to understand

diabetes and associated risk factors.


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References

American Diabetes Association (ADA). 2011. Diabetes Statistics. Retrieved from

http://www.diabetes.org/diabetes-basics/diabetes-statistics/

Arkansas Department of Health (ADH). 2011. County Data Estimates. Retrieved from

http://www.healthy.arkansas.gov/programsServices/healthStatistics/Brfss/Pages/defaul

t.aspx.

Arkansas Department of Health (ADH). (2008). Diabetes in among adults in Arkansas. Retrieved

from

http://www.healthy.arkansas.gov/programsServices/healthStatistics/Brfss/Documents/

publications/FactSheets/diabetes_AR2008.pdf.

Centers for Disease Control and Prevention (CDC). (2011). Retrieved from

http://www.cdc.gov/diabetes/

Education statistics- Bachelors degree or higher by percentage (most recent) by state. (2004).

StateMaster.com. Retrieved from

http://www.statemaster.com/graph/edu_bac_deg_or_hig_by_per-bachelor-s-degree-

higher-percentage.

MMWR. (2008). State-specific incidence of diabetes among adults: Participating states, 1995 -

1997 and 2005 2007. Retrieved from

http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5743a2.htm


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Mokdad, A. H., Ford, E. S., Bowman, B. A., Dietz, W. H., Vinicor, F., Bales, V. S., & Marks, J. S.

(2003). Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001.

Journal of the American Medical Association, 289, 76-79.

Park, A. (2011). Why is smoking especially bad if you have diabetes?TimeMagazine.com.

Retrieved from http://healthland.time.com/2011/03/27/why-smoking-is-a-bad-idea-for-

diabetics/.

Perry, I J., Wannamethee, S. G., Walker, M. K., Thomson, A. G., Whincup ,P. H., & Shaper, A. G.

(1995). Prospective study of risk factors for development of non-insulin depending

diabetes in middle aged British men. British Medical Journal, 310, 560-564.

Ravid, M., Brosh, D., Ravid-Safran, D., Levy, Z., & Rachmani R. (1998). Main risk factors for

nephropathy in Type 2 diabetes mellitus are plasma cholesterol levels, mean blood

pressure, and hyperglycemia.Achieves of Internal Medicine, 159 (9), 998-1004.

United States Census Bureau. (2007). Personal income per capita in current dollars, 2007.

Retrieved from http://www.census.gov/statab/ranks/rank29.html

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