Title Page
By Paul Tanner Olsen
A master’s project submitted to the faculty of
The University of Utah
in partial fulfillment of the requirements for the degree of
Master of Science
Department of Economics
The University of Utah
May 2017
Copyright © 2017 by Paul Tanner Olsen
All Rights Reserved
Complications from the secondary symptoms of diabetes generate more than half of the costs associated with diabetes, including inpatient and emergency medical services. But their onset can be largely delayed until later in life if patients maintain their A1C levels through proper diet, regular exercise, and access to adequate and affordable healthcare and medications. Patients who receive quarterly A1C tests, as well as annual tests for neuropathy and retinopathy, are more likely to better maintain their diabetes. By having personal information about the status of their disease periodically, patients can better adjust diet and exercise, and their physician can recalibrate medications as needed.
Private insurance and Medicare are more likely to provide these tests, as opposed to Medicaid or Tricare/Champs VA. Private insurance was positively correlated with receiving neuropathy and retinopathy tests for both Type 1 and Type 2 diabetics, and Medicare was positively correlated with A1C tests for Type 2 diabetics in a regression statistics study using 2009 Medical Expenditure Panel Survey data. However, there is room for improvement. By providing a few consistent and inexpensive tests, diabetes costs can be reduced for individuals, healthcare providers and the economy.
The impoverished have had less access to healthcare, which makes them more vulnerable to develop diabetes, as well as secondary symptoms of the disease. As Congress works to make improvements on the Patient Protection and Affordable Care Act of 2010, they should preserve and expand Medicare and reduce the costs of prescriptions for all. With fewer barriers to entry, patients will be more likely to receive care and take medications as prescribed to manage the disease, and thus reduce costs overall.
This thesis is a long time coming after years of researching the 2009 MEPS data. I want to thank my family for their continued support that has made this project possible. I also thank my wife Emily for her editing and formatting contributions.
As a Type 1 diabetic myself, I have a special interest in this topic. I wish to thank endocrinologist James Chamberlain, M.D. and Amy Glenn, N.P., who provided a review of my criteria for distinguishing between Type 1 and Type 2 diabetics. I am also grateful to my thesis committee for their time and attention to this thesis topic.
Abstract
Table of Contents
List of Tables
List of Figures
1. Introduction
1.1. Link Between Poverty and Diabetes.
1.2. Type 1 Diabetes Is Unique.
1.3. The Impact of Secondary Symptoms.
1.4. Tests for Secondary Symptoms.
1.5. Affordable Medications.
1.6. Adjustments to Current Legislation.
1.7. Prior to the Affordable Care Act.
1.8. Statement of the Problem.
1.9. Hypotheses Statements.
2. Materials & Methods.
2.1 Criteria for Distinguishing Type 1 from Type 2 Diabetics.
2.2 Variables.
2.3 Descriptive Statistics.
2.4 Design of Regression Sets.
3. Results.
3.1 First Regression Set.
3.2 Second Regression Set.
3.3. Third Regression Set.
3.4 Fourth Regression Set.
3.5 Fifth Regression Set.
3.6 Evaluation of Hypotheses.
4. Discussion and Conclusion.
4.1. Discussion.
4.2. Limitations and Topics for Further Study.
4.3. Conclusion.
References.
Appendix A: Numerical Values of Figures in Descriptive Statistics Data.
Appendix B: Regression Results – T-scores and Coefficients.
Table 2.1. Criteria for Distinguishing Type 1 from Type 2 Diabetics.
Table 2.2. Number of Total and Total (Weighted) Observations in the Study.
Table 2.3. Number of Observations per Age Group.
Table 2.4. Demographic Variables.
Table 2.5. Variables that Are Indicators of Socioeconomic Status.
Table 2.6. Additional Variables.
Table 2.7. 2010 Race Data (U.S. Census)*.
Table 2.8. Percentage of diabetes cases diagnosed in 2000, by race/ethnicity.
Table 2.9. Commonly Reported Priority Conditions.
Table 2.10. Design of the First Regression Set.
Table 2.11. Design of Second Regression Set.
Table 2.12. Dependent and Independent Variables for Third Regression Set.
Table 2.13. Dependent and Independent Variables Used for Fourth Regression Set.
Table 2.14. Dependent and Independent Variables Used for Fifth Regression Set.
Table 3.1. Correlation Coefficients for Type 2 Diabetes – First Regression Set.
Table 3.2. Correlation Coefficients in Nephropathy & Retinopathy – Second Regression Set.
Table 3.3. Correlation Coefficients for Other Secondary Symptoms – Second Regression Set.
Table 3.4. Secondary Symptoms & Years with Diabetes – Third Regression Set.
Table 3.5. Years with Diabetes & Age – Third Regression Set.
Table 3.6. Insurance Type and Receiving Recommended Tests – Fourth Regression Set.
Table 3.7. Priority Conditions and Receiving Recommended Tests – Fourth Regression Set.
Table 3.8. Private Insurance and Secondary Symptoms – Fifth Regression Set.
Table 3.9. Demographics and Secondary Symptoms – Fifth Regression Set.
Figure 2.1. Execution of Criteria to Distinguish Type 1 from Type 2 Diabetics.
Figure 2.2. Number of Years with Diabetes.
Figure 2.3. Gender.
Figure 2.4. Married.
Figure 2.5.1. Race: Total Age Groups.
Figure 2.5.2. Race: Age 18-24.
Figure 2.5.3. Race: Age 25-40.
Figure 2.5.4. Race: Age 41-50.
Figure 2.5.5. Race: Age 51-64.
Figure 2.5.6. Race: Age ≥ 65.
Figure 2.6. Decimal Value of the Poverty Line*.
Figure 2.7. Years of Education.
Figure 2.8. Unable to Get Care/Financial Reason Unable.
Figure 2.9. Delayed Getting Care/Financial Reason Delayed.
Figure 2.10. Unable to Get Prescriptions/Financial Reason Unable.
Figure 2.11. Delayed in Getting Prescriptions/Financial Reason Delayed.
Figure 2.12. On Food Stamps.
Figure 2.13. Monthly Monetary Value of Food Stamps.
Figure 2.14. Family Size.
Figure 2.15. Ever insured in 2009/Uninsured in all of 2009.
Figure 2.16.1. Insurance Type – Tricare/Champ VA.
Figure 2.16.2. Insurance Type – Medicaid.
Figure 2.16.3. Insurance Type – Medicare.
Figure 2.16.4. Insurance Type – Private Insurance.
Figure 2.17. Percentage of Diabetics Treated for Priority Codes.
Figure 2.18. Percentage of Diabetics Who Received Clinical Tests for Secondary Symptoms.
Politicians, economists and medical executives have worked for years to provide an affordable and cost-effective healthcare system for all Americans. President Obama was the first U.S. President, with the help of a Democratic Congress, to pass a comprehensive healthcare bill called the Patient Protection and Affordable Care Act of 2010 (ACA), also known as Obamacare. As President Trump and a Republican Congress pursue revamps to the legislation, I offer them my research regarding the healthcare needs associated with a significant population, Type 1 and Type 2 diabetics, who constitute 29.1 million (9.3%) of the U.S. population. (CDC 2014).
In the United States, we spend $3 trillion, or 20 percent of the GDP, on healthcare, which is 2 or 3 times more than what other countries spend without getting better results (Solman 2017). The average medical expenditures among U.S. diabetic patients are 2.3 times higher than for people without diabetes (CDC 2014). Of diabetic patients, those who experience secondary symptoms of the disease incur the greatest expense – not only economically but in quality of life. With proper maintenance, these symptoms can be significantly delayed until later in life, resulting in fewer direct and indirect costs over time (Minshall et al. 2005).
Dall et al. (2009) reported that in 2007, of the $116.3 billion annual medical and pharmaceutical costs associated with diabetes in the United States, $58.3 billion (50.1%) are attributed to hospital inpatient costs. Another $7 billion (6%) can be attributed to diabetes-related emergency room visits and hospital outpatient costs. Inpatient and emergency room costs for diabetics involve medical treatment and procedures that are largely in response to uncontrolled diabetes.
1.1 Link Between Poverty and Diabetes
The metabolic disease diabetes mellitus is an expensive illness, especially for the impoverished. Unfortunately, there is a link between poverty and the onset of Type 2 diabetes. Although both Type 1 and Type 2 diabetics come from all socioeconomic groups, Type 2 diabetics are more likely to be poor in the United States, where there is a link between poverty and the consumption of cheaper sugary foods as a regular substitute to fruits and vegetables (Levine 2011), which leads to the disease over time. This phenomenon does not occur internationally, where impoverished populations have less access to sugary foods and rely on food staples such as corn, rice and beans. In addition to a poor diet, Type 2 diabetics are also less active, and genetics and other factors also play a role. The impoverished have had less access to healthcare and less education that would assist them in adjusting their diets to better meet their health needs.
1.2 Type 1 Diabetes Is Unique
It is unknown what causes Type 1 diabetes, which makes up 5-10% of all diabetics (CDC 2014). One current theory as to the cause of Type 1 diabetes is that certain identified genes cause an autoimmune response to turn on when a patient experiences a significant stress event, and this can occur at any time in their life (Atkinson, et al. 2014). Different human leukocyte antigens (HLA) class II haplotypes have been linked to Type 1 diabetes that develops in childhood (HLA DRB1*04–DQB1*0302, but also HLA DRB1*03–DQB1*0201) versus latent autoimmune diabetes (HLA- DQB1*0201/0302 [DR3/DR4]) whose onset is later in life (Cerna 2007). Others suggest that the condition is triggered by a virus or environmental factors (Eringsmark Regnéll & Lernmark 2013). Based on what researchers presently know about Type 1 diabetes, the condition develops regardless of patient lifestyle or health habits.
Type 1 diabetics, who are often diagnosed as children or young adults, have traditionally lived with the disease for a longer portion of their lives than Type 2 diabetics, who commonly experience the onset of the disease later in life. In recent years, a fraction of Type 2 diabetics has been diagnosed in the prime of life as a result of the obesity epidemic. Conversely, more cases of Type 1 diabetes with an onset later in life are being diagnosed as researchers learn more about the disease (Tao, et al. 2010). Over the course of their lifetimes, Type 1 diabetics will spend more out of pocket for medical expenses than Type 2 diabetics. As a result, it is important to track expenses of both diseases separately from each other.
Both Type 1 and Type 2 diabetes are on the rise. In the last two decades, the incidence of the disease has more than tripled, from 8 million in 1995 to 29.1 million in 2014 (CDC 2015-1). Tracking the disease and finding ways to cut costs will reduce its impact on patients with disease, healthcare providers and the economy.
1.3 The Impact of Secondary Symptoms
As the disease advances, both Type 1 and Type 2 diabetics will present with secondary symptoms, such as neuropathy, which can lead to the amputation of lower limbs, and retinopathy, which causes blindness. These conditions occur when elevated glucose levels in the circulatory system destroy capillaries over time. The prevalence of cardiovascular disease, another secondary symptom in diabetic patients, is similar to nondiabetic individuals who are 10 to 20 years older, both in cardiovascular events and in measures of atherosclerosis, or hardening and narrowing of the arteries (Polak et al. 2011). Renal failure, stroke, high cholesterol and high blood pressure are also conditions associated with diabetes. Out-of-pocket expenses associated with ambulatory care, emergency treatment, inpatient surgery, ICU stays, and hospice recovery will exasperate household budgets (Secrest et al. 2011), and the inability to work will further necessitate the need for patients to rely on disability insurance and social security. By delaying the onset of secondary symptoms until later in life, patients can provide for their families longer, purchase long term disability insurance, and prepare for the increased medical expenses as part of their retirement savings.
Access to affordable healthcare is essential to diabetics, who need regular doctor’s visits to properly monitor the disease. When co-pays, lab expenses and prescriptions become cost prohibitive to patients, they are less likely to go to the doctor and take their medications properly.
1.4 Tests for Secondary Symptoms
It is recommended that patients receive quarterly hemoglobin A1C tests, that identify a three-month average of plasma glucose concentration. A person with a fully functioning pancreas will have an A1C level below 5.7% (Mayo Clinic 2017-1). An acceptable target for a diabetic is less than 7%. An elevated A1C is a warning sign for patients to make changes to diet, exercise, and how frequently they are testing their blood sugar. If blood glucose levels are not monitored as recommended, the onset of secondary symptoms can accelerate.
Other tests that the American Diabetes Association recommends on an annual basis are a foot exam to check for signs of neuropathy and a dilated eye exam to measure the presence of retinopathy. Neuropathy tests may be performed by a family physician or endocrinologist, and retinopathy tests may be given by an optometrist or ophthalmologist. Some states, including Utah, require that diabetics provide the results of the retinopathy test to maintain an active driver’s license. Diabetics should also receive blood chemistry tests, such as a cholesterol test.
In addition to affordable doctor visits and monthly premiums, diabetics need access to affordable medications and supplies, which also make up a significant percentage of diabetes costs. Dall et al. (2009) reports that $27.7 billion (15.9%) was spent in 2007 on outpatient medications and supplies for diabetics. Although expensive, they help diabetics maintain healthy A1C levels, which will reduce overall expenses and ultimately help them live a longer and better quality life.
The United States has the highest drug prices in the world. There is presently no price control mechanism for drug prices in the United States. When one drug manufacturer puts a price at a new high level, other drug manufactures follow suite. Drug advertising, which is only allowed in one other country in the world (New Zealand), has increased 62% since 2012. In other countries such as Japan, prices must go down in the medical industry as technology gets older, but in the United States, that is not happening. For example, the MRI became available in the 1960s, but the cost for them is still in the thousands, where in Japan the cost is about $150 (Solman 2017).
The out-of-pocket costs for certain insulins have skyrocketed in the last few years, but market pressures may drive prices back down to affordable levels. For example, the cost of Sanofi SA’s Lantus insulin increased 90% in last five years (Loftus 2016-1). In response to the public outcry, competitors Eli Lilly and Boehringer Ingelheim GmbH have announced that they will soon begin to offer lower-cost versions of Lantus, which has prompted CVS Health Corp. to stop paying for Sanofi’s name brand product.
Medicare has faced several policy challenges this year. The Obama administration proposed a policy change in 2016 designed to curb Medicare spending on prescription drugs by requiring doctors to prescribe less expensive medications when possible, but the policy was never finalized (Loftus 2016-2). The Trump Administration has expressed its disapproval of pharmaceutical prices.
1.6 Adjustments to Current Legislation
The Trump Administration, after a failed bill in the House to repeal the Affordable Care Act, intends to pursue changes to the legislation through a Congressional act or through policy (Armour 2017). It has proposed to end the healthcare mandate that required Americans to enroll in an insurance policy, which would lead to an increase in premiums of an estimated 15-20% (Mathews 2017). It has also proposed to repeal the Medicaid expansion starting in 2020. More than 30 states have participated in the expansion of the joint federal and state insurance program designed for children, low-income individuals and those with disabilities, and the program has grown 16-25% since 2014 through the Affordable Care Act. Governors whose states have been participating oppose the rollback (Grant 2017).
The Trump administration has an opportunity to improve upon Obama’s legislation with the benefit of hindsight. If policy changes can provide diabetics with more focused care, affordable premiums, doctor visits and medications, it will move us closer to the goal of reducing the immediate and long-term costs of the disease.
1.7 Prior to the Affordable Care Act
This thesis utilizes the 2009 Medical Expenditure Panel Survey (MEPS; Agency for Healthcare Research and Quality 2011), a study of self-reported and doctor-reported medical data, to study the economic and medical situations of diabetics. The year 2009 provides an interesting snapshot of patients’ access to healthcare prior to the Affordable Care Act and allows us to assess in which areas we have excelled and where we still need improvement. I will compare insurance types to identify which is the most affordable and provides the most effective coverage to diabetic patients.
1.8 Statement of the Problem
In comparing private insurance, Medicaid, Medicare, and Tricare/Champ VA among diabetic patients, which type is more likely to:
1.9 Hypotheses Statements
Improving patients’ access to healthcare, despite their socioeconomic status, is one important method that can assist in delaying the onset of the secondary symptoms of diabetes which are costly for individuals, medical providers and the national economy. The following is a further description and the null and alternative hypotheses for the each of the three research questions.
H0: A patient’s insurance type has no impact on whether they receive the recommended tests.
H1: In analyzing private insurance, Medicaid, Medicare and Tricare/ Champ VA, at least one type of insurance will be statistically significant in facilitating the recommended tests.
H0: A patient’s insurance type does not impact the likelihood of secondary symptoms.
H1: In evaluating private insurance, Tricare/ Champ VA, Medicaid and Medicare, at least one type of insurance analyzed will reduce the likelihood of secondary symptoms.
In this section, I will present the criteria to distinguish between Type 1 and Type 2 diabetics. I will include definitions and descriptive statistics of the variables included in the study, and I will also provide the design of the four sets of regressions that I performed.
2.1 Criteria for Distinguishing Type 1 from Type 2 Diabetics
Using a set of approved criteria to distinguish between Type 1 and Type 2 diabetes has been very important to my study, as I started getting correlation when I separated the data by the two main diabetes types. The lack of correlation when the groups are combined and the overall correlation when their data is run separately against economic and medical variables confirms that the circumstances of Type 1 and Type 2 diabetics are different from each other.
To diagnose patients as diabetic, doctors typically use an A1C test or a combination of two glucose tests (measuring glucose in the plasma and administering an oral test; Seley 2009). A score of 6.5 or more indicates the effects of diabetes. To distinguish between Type 1 and Type 2 diabetes, doctors have traditionally considered age and BMI (body mass index; Fourlanos et. al 2006 and Utzschneider et. al 2004). However, new research suggests that a diagnosis based on BMI is proving to be an inaccurate diagnosis in some cases. Some patients originally diagnosed as Type 2 are now being recognized as Type 1 and vice versa.
For example, researchers have discovered that some adults originally diagnosed as Type 2 diabetic have characteristics of Type 1 diabetes (Golden et al. 2010), and children can be misdiagnosed as well (Brunk, et al. 2011). Researchers involved in a study of obese teenagers by the American Diabetes Association indicated the importance of performing lab tests to measure autoantibodies and C-peptide levels that will either identify or rule out Type 1 diabetes. The results of such lab tests are not available for patients in the MEPS data set, but I can distinguish between Type 1 and Type 2 diabetics with reasonable certainty using the available data.
ICD-9 diagnostic codes were used by doctors in 2009 for medical billing purposes. For example, a code of 250.00 indicates Type 2 or undefined, 250.01 indicates Type 1 diabetes without complication, and 250.02 is Type 2 diabetes without complication – uncontrolled. Other decimals, for example, indicate complications such as ketoacidosis, or renal, ophthalmic or neuropathic manifestations (ICD9Data.com 2017). In the 2009 MEPS Study, ICD-9 codes were provided in HC128, a separate spreadsheet from the main HC129 spreadsheet. The study includes 3-digit codes such as 250, but, unfortunately, specifying decimals attached to the codes were not included for privacy reasons. Instead, I relied on the self-reported as diabetic column to distinguish between diabetics and nondiabetics, but I utilized the 3-digit ICD-9 column as a check. When patient IDs were compared in each, the columns matched up and confirmed themselves.
By utilizing information available in the dataset, I was able to determine with reasonable certainty whether identified diabetic patients were Type 1 or Type 2 diabetic. In addition, I compared my results to national statistics, which estimate that Type 1 diabetics make up 5-10% of all diabetics. My results assigned between 6-7% of diabetics in the dataset as Type 1 diabetic.
Table 2.1 lists the seven criteria that are presented in the order in which they were applied, with the first criteria given higher importance than the last. I have also included a description of each. Figure 2.1 is a flow chart that illustrates the execution of the criteria upon the dataset in the order of importance.
The MEPS survey is self-reported, and often, patients did not fill out all the data fields requested, or the data was otherwise not provided. If patients did not include enough information or the information was conflicting to the point that the data was inconclusive, the patient was removed from my study. The following are a few examples of missing data and how I would classify them using the criteria:
Example 1: A patient has indicated that they are diabetic, they are taking insulin, but they did not indicate what year they were diagnosed. However, their BMI is less than 27.5, so the patient was classified as Type 1 diabetic.
Example 2: A patient has indicated that they are diabetic, but they did not indicate whether they are taking oral medications or insulin. However, they were diagnosed over the age of 55 and their BMI is greater than 27.5. There is a chance that they could be GADA Type 1 diabetic, which often presents similar characteristics to Type 2, but the instance of GADA Type 1 diabetes is rare, so for the purposes of this study I classified them as Type 2 diabetic.
Example 3: A patient self-reports themselves as diabetic, current age of 50, and age of diagnosis as 49, but does not provide any BMI information and does not indicate whether they are taking oral medication or insulin. The patient is likely Type 2, but without sufficient information, the patient is excluded from my study.
A self-reported dataset, the 2009 MEPS study includes more than 36,000 observations (patients; see Table 2.2). Each observation was weighted to represent the United States’ population. About
one-third of the patients could not be included in the study because of a lack of self-reported data that prevented them from being compared accurately with the others, but the slimmed down data set still represented the intended population.
About 29.1 million individuals, or 9.3% of the population, have diabetes in the United States (American Diabetes Association 2016). 9.5% of observations in the self-reported MEPS study, which included individuals 18 and older, indicated that they had diabetes. Among the weighted numbers, only 6.41% identified themselves as diabetic. In the study, no children under the age of 18 identified themselves as diabetic, but we know that there are children in the United States who have diabetes.
To improve overall accuracy, I removed from the dataset all subjects under the age of 18, including nondiabetics (13,387 observations). Doing so adjusted the total percentages among the weighted numbers, and this action explains the discrepancy in how the weighted numbers compare to national averages. For purposes of analysis in the descriptive statistics section, I separated the observations into five age groups, as shown in Table 2.3.
Notice that there are fewer Type 1 diabetics in the Age ≥ 65 category, and this is because many have traditionally died before making it to retirement. This statistic is changing as improved insulins and glucose meters help patients to better monitor their diabetes More patients are able to utilize an insulin
pump and sensor, the latest models of which work together as an artificial pancreas and greatly improve the quality of life of patients.
Demographic Variables. In all four of the regression sets, I ran certain demographic statistics as independent variables in every analysis that was performed. Those variables are listed with their descriptions in Table 2.4. I performed the regression analyses in the statistics software STATA, which requires that nominal data be in binary format – in ones and zeroes – and I formatted the MEPS data accordingly. For example, I separated the race data into separate columns for each of six racial groups, with the seventh, Caucasian, serving as the null value.
Variables included in the MEPS data that are indicators of socioeconomic status (see Table 2.5) were tested in separate computations alongside the demographics listed above. Impoverished individuals may have limited access to healthcare services for a variety of reasons. It may be as simple as a lack of transportation. Others may not be aware of what is required to maintain a healthy A1C level. Some poor dietary habits can be rooted in a lack of education or the inability (or refusal) to consume fruits and vegetables. Others are the result of food deserts, when residential units in the inner-city that are located more than a mile from the nearest grocery store (Levine 2011).
Table 2.6 includes descriptions of variables that indicate a patient’s access to health insurance, and includes insurance type, which is utilized in the fourth regression set of diabetics only (including
private insurance, Medicaid, Medicare, and Tricare/ Champ VA). Tricare is insurance for uniformed service members, retirees and their families, and Champ VA is insurance for disabled military veterans. People with Type 1 diabetes are disqualified from entering the military, but if the onset of the condition occurs to an active member of the military they aren’t automatically sent home (Hieronymus and
Rickerson 2012). Medicare covers people over the age of 65 as well as those who are disabled or who have end-stage renal disease. Diabetics who are on dialysis have end-stage renal disease (Mayo Clinic 2017-2) and can be under the age of 65.
The fourth regression set also uses a variable regarding whether diabetics were treated for other priority conditions other than diabetes, such as heart disease, skin conditions, or asthma, which are also expensive to treat and maintain. Additional variables include whether a patient received recommended tests in 2009 which detect the onset of secondary symptoms, including four A1C tests, at least one neuropathy test and at least one retinopathy test. Although other tests are used, the tests mentioned were selected for my study because of the available MEPS data.
The following figures provide a comparison of the descriptive statistics between Type 1 and Type 2 diabetics, as well as nondiabetics in each age group for the variables. Actual numbers are provided in Appendix A.
The number of years that a patient has lived with diabetes is more indicative than age of the onset of conditions such as retinopathy or nephropathy. Figure 2.2 compares by age group the mean number of years that individual patients have lived with the disease. This variable was added to the third regression set to compare it with current age. I wanted to demonstrate whether the number of years with the disease or age plays a more significant role in the advancement of diabetes.
In Figure 2.3, women (represented as 1 in the data) were slightly more likely to be Type 1 diabetic than men (represented as 0 in the data), especially those age 50 and younger. In the age 18-24 group, women were a whopping 95 percent of Type 2 diabetics, although the high percentage was likely due to the small sample size, with only nine individuals with Type 1 and seven with Type 2 diabetes. Women were also more likely than men to be diabetic in the age 25-40 group, and men were only slightly more likely to be Type 2 diabetic than women in the 41-50 age range.
Being married has been shown to improve an individuals’ health, particularly among males. The improvement is partly the result of having improved access to quality and affordable health care, since married individuals have access to both their own and their spouses’ health care provider (Gomez, et al. 2016). As illustrated in Figure 2.4 (where single, divorced or widowed is 0 and married is 1 in the data), nondiabetics were more likely to be married than diabetics in most age categories. Type 1 diabetics were the least likely to be married, which affects their socioeconomic status and access to health care.
Not many individuals diagnosed with Type 1 diabetes have traditionally lived to be age 51-64, but newer medicines and lifestyle recommendations have made it possible in recent years for individuals with this disease to live longer, given their access to and ability to afford the needed healthcare and prescriptions. For example, Type 1 diabetics who can afford insulin pumps, glucose monitors can significantly delay the onset of secondary symptoms and thus reduce their overall diabetes-related expenses (Huang, et al. 2010). Pancreas transplants are also performed, usually in conjunction with another organ transplant, and they can effectively cure a patient for up to 10 years.
Race. The 2009 MEPS race data for total nondiabetics (see Figure 2.51) lined up closely with the projected U.S. population for 2010 (2009 U.S. Census; see Table 2.7). However, diabetes does not occur among races proportionally. In fact, in recent years, the Center for Disease Control and Prevention (CDC) reported in the 2014 National Diabetes Statistics Report that new cases of diabetes are skyrocketing, particularly among U.S. minority populations (see Table 2.8).
Although Caucasians (whites) still make up a majority of total diabetics today, the numbers are changing. According to the CDC (2014), new cases of diabetes in 2000 affected 7.6% of Non-Hispanic whites, 9% of Asian Americans, 12.8% of Hispanics, 13.2 percent of Non-Hispanic blacks, and a staggering 15.9% of the small Native American population (24.1% of which are living in southern Arizona reservations).
The Hispanic population is on the increase in the United States, and the U.S. Census (2017) predicts that it will grow substantially in the next 40 years. By 2050, the group is expected to comprise almost 30% of the U.S. population, nearly double their current ratio. Cases of both Type 1 and Type 2 diabetes among this population are also on the rise from previous years.
The MEPS variable indicating race originally had values of:
1 – Caucasian (white)
2 – African American
3 – American Indian or Alaska Native
4 – Asian
5 – Hawaiian or Pacific Islander
6 – Multiple races reported
For purposes of conforming to STATA’s requirements, I divided the original race column into five binary columns, with Caucasians holding the null value and African Americans, Native Americans, Asians, Pacific Islanders, and Mixed Race each having a column where their respective value was one. Hispanics had separate columns in the raw data, and I added it as a sixth option for race. 394 individuals classified as mixed race, and of those, 52 indicated Hispanic origins, 141 listed as African in origin also identified as
Hispanic. Of the Native Americans, 136 listed some Hispanic ethnicity, 18 Asians included some Hispanic ethnicity, and 12 Pacific Islanders included some Hispanic ethnicity.
Table 2.5.1 displays the races of observations from all age groups combined. All race categories comprised percentages that were proportional to their populations. Native Americans are only proportionally represented among Type 2 diabetics and only have trace representation among Type 1 and nondiabetics.
Neither Type 1 nor Type 2 diabetes is believed to be genetically tied to a specific race. In Bao, et al. (2013), when analyzing multiple studies that sought to identify whether there was a genome-wide association (racial origin) of the risk of diabetes, the results indicated that genome-wide association did not provide additional foresight from traditional lifestyle indicators, such as an elevated BMI, elevated blood pressure, and a family history of diabetes, to identify the likelihood that someone would develop diabetes. Researchers (Charatan 2001) have found that eating a healthy diet and getting regular
exercise can reduce the risk of developing Type 2 diabetes by half, despite any genetic predisposition to the disease. Any racial correlations can be explained by cultural traditions and community lifestyles.
In the 18-24 age category (Table 2.5.2), Hispanics comprise 33 percent of Type 1 diabetics and nearly 65 percent of Type 2 diabetics.
In the 25-40 group (Table 2.5.3), the number of Hispanic Type 2 diabetics is still elevated at 24.2%. African Americans also comprise 25.7% of Type 2 diabetics and 19.8% of Type 1 diabetics. It is
unknown what causes Type 1 diabetes, but there does not appear to be a racial component to the disease. In all age groups, cases of Type 1 diabetes appear to match proportions of racial populations.
It is known that instances of Type 1 diabetes are on the increase worldwide, particularly among children. Patterson et al. (2009) reports that the number of diagnoses in Europe among children age 15 years and younger is increasing an average of 3.9% each year, and prevalence is predicted to rise
from 94,000 in 2005 to 160,000 in 2020. This trend may explain why in this study cases of Type 1 diabetes are higher among minority races in the younger age groups.
Figure 2.6 compares patients’ subjectivity to the poverty line based on annual household income, where the poverty line equals 1. The darker colored dots represent the mean, and the corresponding bars represent -1σ to 1σ. When looking at all age groups combined, you can see there was little variability between the Type 1, Type 2 and nondiabetics regarding where patients stood against the poverty line. However, differentiation was more apparent when the data was broken into age groups. In almost every age group, diabetics were poorer than nondiabetics, and Type 2 diabetics were poorer than Type 1 diabetics in many age groups. It is unclear from the data whether diabetic patients were experiencing poverty at the time of diagnosis or if poverty had developed post-diagnosis.
Type 1 diabetics had more education than Type 2 diabetics overall and in most of the age categories. Type 1 diabetics and nondiabetics also had similar levels of education in every age range except ≥ 65 and the total, as illustrated in Figure 2.7. However, if someone’s stated number of years of education was less than zero, they were removed from the data set, as a negative number indicates an answer such as “I refuse to say” or “I don’t know.” A patients’ level of education is important to this study, as education plays a significant role in socioeconomics. As a result, I removed 396 individuals from the population sample.
Over the course of their lifetimes, Type 1 diabetics will spend more out-of-pocket for prescriptions, in particular, but also for all medical expenses, than Type 2 diabetics. It should be noted that the MEPS data only represent patients’ experiences in 2009. In Figure 2.8, patients who were not able to receive financial care were strongly linked to the inability to afford the care. In all age groups combined, Type 1 diabetics had the highest instance of this challenge, especially in the age 25-40 age
group, where they had more than double the percentage (12%) of patients with this problem than any other segment.
In Figure 2.9, only about half of patients who said they were delayed in getting care identified cost as the reason for the delay. The data, when split into age groups, varied, but when all age groups were combined, the data was consistent between Type 1, Type 2 and nondiabetics, with Type 1 and Type 2 diabetics only slightly higher than nondiabetics.
Patients who are not able or are delayed in receiving care are less likely to maintain healthy A1C levels and are thus more likely to experience an accelerated onset of secondary symptoms. The appearance of secondary symptoms quickly increases medical expenses for all diabetics, as it often involves a hospital stay and can require expensive procedures.
Patients who balance their carbohydrate and protein intake, eat the recommended amount of fruits and vegetables, and get regular exercise can delay the onset of secondary symptoms such as blindness, foot amputations, heart disease and stroke. These events skyrocket medical expenses that are attributed to the treatment of diabetes and can lead to the inability to work (Secrest et al. 2011).
In Figure 2.10, which discusses patients who self-reported that they were unable to get prescriptions, patients acknowledged that their inability was due to financial reasons. However, more Type 2 diabetics said they were experiencing this challenge than Type 1 or nondiabetics.
In Figure 2.11, Type 1 diabetics were more likely to be delayed in getting prescriptions than
Type 2 diabetics, especially in the 25-40 age group. These are individuals who are able to get prescriptions but are not able to get them in a timely way. Type 1 diabetics acknowledged that financial reasons were the cause for the delay in getting prescriptions for both the 18-24 and 25-40 age groups. In all age groups combined, Type 1 and Type 2 diabetics were significantly more likely to be delayed in getting prescriptions than nondiabetics.
The financial reasons unable or delayed variables were assessed at a value of one for each of the following reasons listed in the MEPS:
All other possibilities were given a value of zero. The “unable to get” and “delayed in getting care,” as well as “unable” and “delayed in getting prescriptions” variables were adjusted to values of one for yes and zero for no.
Both Type 1 and Type 2 diabetics often experience challenges in the workforce because of their medical conditions. Administering insulin to themselves during work hours or needing to take an extended break if they experience a sudden blood sugar low may seem to some as beyond “reasonable accommodations.” But diabetics are protected under the Americans with Disabilities Act Amendments Act (ADAAA) of 2008, which established protections for individuals who have chronic medical conditions (American Diabetes Association 2014). ADAAA applies to all employers with 50 or more employees. Unfortunately, diabetics may find themselves getting passed up for promotions when in competition with fellow employees, but diabetics, even those with secondary symptoms, statistically have not been found to have less productivity in the workplace than nondiabetics (Lavigne et al. 2003). They rely on quality and affordable insurance from their employer.
Figure 2.12 shows the instance of patients on food stamps. In general, Type 2 diabetics were closer in line with nondiabetics than Type 1 diabetics, who were less likely to be on food stamps. Type 1 diabetics’ highest instance of food stamp use was in the age 18-24 group at 17 percent. Type 2 diabetics’ highest instance of food stamp use was in the 25-40 age group, at more than 30 percent. Nondiabetics’ highest instance of food stamp use was at about 12 percent in both the 18-24 and 25-40 age groups.
Figure 2.13 illustrates the monthly monetary value of food stamps and displays both the mean and standard deviation. Although Type 1 diabetics were generally less likely to be on food stamps, they
were issued the greatest value of food stamps in the 18-24, 25-40 and 41-50 age categories, where the means of each were about $400 per month. The standard deviation for Type 1 diabetics was also higher than for the other categories.
Figure 2.14, which compares family size, indicates that diabetics tended to have smaller households than nondiabetics overall. The total number of Type 1 diabetics had larger families than the total number of Type 2 diabetics, but breaking down the statistics by age groups provided additional information. Type 2 diabetics tended to have larger family sizes in the Age 25-40 and ≥ 65 groups, and nondiabetics tended to have larger family sizes in the 41-50 and 51-64 age ranges. The only age category where Type 1 diabetics had significantly larger family sizes was in the Age 18-24 group, when patients may still be living with their parents and siblings.
Figure 2.15 indicates that for the percentage of subjects who are insured, there are many more who are not insured, regardless of whether they have diabetes or not. The “ever had insurance” variable was the combination of three columns from the original data corresponding to the three rounds of the survey. The same was done for “employee insurance” and the three initial “employer offers insurance” variables. Uninsured for all of 2009 was adjusted to one for yes and zero for no.
Because of the exorbitant medical costs that they experience, most diabetics must have access to affordable health insurance to have any chance of sustaining a healthy lifestyle. As a result, they, or a member of their household, traditionally have had to work full time for an employer that provides health insurance, or they have had to receive government sponsored medical insurance such as Medicaid. Figures 2.16.1 through 2.16.4 indicate the percentage of individuals in the data set who were enrolled in various types of insurance in during 2009, including Tricare/Champ VA, Medicaid, Medicare and private insurance. In some cases, patients may have enrolled in multiple types of insurance. For
example, they may receive private insurance through their employer and also qualify for Medicare. In 2009, many self-employed individuals and those who work part-time were not able to qualify for affordable insurance because of pre-existing conditions, but since then, the Affordable Care Act has improved the access to health insurance for many (Shaw et al. 2014).
The MEPS data did not specify between those who were enrolled in Tricare, for active U.S. military and their families, or Champ VA, for disabled U.S. veterans. A column that represented the entire data set of all insurance types, as well as the uninsured, indicated if a patient was presently active in the U.S. military. There were less than 1% in all age groups, and they were all nondiabetic. Type 1 and Type 2 diabetics are disqualified from entering the military, but becoming diabetic during their service is not an automatic cause for dismissal. Another column indicated whether a patient had been honorably discharged from the military, which also represented the whole data set, and there was a proportional representation of Type 1, Type 2 and nondiabetics included.
Diabetics dedicate significantly more of their budget to medical expenses than nondiabetics, which means they typically have less expendable income. In addition to quarterly doctor’s visits and expensive oral or intravenous medications, diabetics may need to pay for needles, syringes, blood glucose meters and test strips. In impoverished situations, patients sometimes choose to pay for the needs of another family member at the expense of paying for part or all of their own medications.
It should be noted that a majority of diabetics in the study were treated for other priority conditions (see Table 2.9) in addition to diabetes. These conditions may be related to diabetes such as high blood pressure or a stroke, or they may be unrelated, such as a chronic skin condition or asthma. Priority conditions include a group of medical conditions that are selected for their prevalence, expense, or relevance to policy, including hypertension, heart disease, high cholesterol, emphysema, chronic bronchitis, diabetes, cancer, arthritis, asthma, attention deficit/hyperactivity disorder, and stroke (Agency for Healthcare and Research Quality 2017). I have included them as variables in the fourth regression set to acknowledge that patients’ medical expenses could be spent on other conditions besides diabetes. Priority conditions are used by the MEPS for statistical purposes to track the expenses associated with specific chronic medical conditions that merit their attention. Some examples of priority conditions are shown in Table 2.9.
Figure 2.17 indicates the percentage of patients who were treated for priority conditions in 2009, and it’s clear that a majority of diabetic patients are treated for other conditions that are important to maintain and can be expensive over time.
Figure 2.18 provides the percentage of diabetics who received all the required tests in 2009 to detect the onset of secondary symptoms. The tests include four A1C tests, one neuropathy test in the feet and one retinopathy test in the eyes.
My thesis project consisted of four sets of regression analyses. The dependent and independent variables used in the first set are listed in Table 2.10. The dependent variables were the population samples for Type 1 and Type 2 diabetics as well as nondiabetics, and the independent variables included the demographics and socioeconomic indicators. The demographics were run individually for Type 2 diabetics only, and they were combined and run with each individual socioeconomic indicator variable against all population samples.
Table 2.11 shows the dependent and independent variables for the second regression set. The dependent variables were the appearance of secondary symptoms in the population samples for Type 1, Type 2 and nondiabetics. Additional dependent variables were comparisons between Type 1 & nondiabetics and Type 2 & nondiabetics to determine if having diabetes makes an individual more likely to develop these symptoms. Nephropathy (kidney failure) and retinopathy (blindness), which are unique to diabetics, were run on Type 1 and Type 2 patients only against the independent variables including
the demographics and socioeconomic indicators. For these tests, each socioeconomic indicator was run individually along with the demographic variables combined.
Increased risks of cardiovascular problems and stroke are also associated with diabetes, so those factors were run as dependent variables in the regression tests against all population samples. These other dependent variables were run against the demographic variables combined.
The tests that the American Diabetes Association recommends on an annual basis, which include four A1C tests (one each quarter), a foot test for detecting the presence of neuropathy, a dilated eye test for detecting the presence of retinopathy, a cholesterol test and the flu vaccine were added as independent variables and run together against nephropathy and retinopathy for both Type 1 and Type 2 population samples. The tests were run along with the demographic variables combined, as well as the decimal value of the poverty line or one of the socioeconomic indicators.
The third regression set, shown in Table 2.12, is similar to the second set but adds an additional independent variable, the number of years with the disease. It was run against the Type 1 and Type 2
population samples only. This variable was generated by subtracting the current age of the patient from the reported age of diagnosis. Patients who did not list an age at diagnosis were not included in the set, which comprised approximately 10% of Type 1 diabetics and 5% of Type 2 diabetics. Since this data is self-reported, the age at diagnosis is approximate. Each regression in the set was run with age and without age to see whether age or the number of years with the disease has a stronger influence on the development of secondary symptoms. It was also performed to detect any differences due to the possibility of age and years with diabetes being correlated with each other.
Nephropathy and retinopathy were run against the socioeconomic indicators, in combination with all the demographics. The other secondary symptoms were only compared with the demographic variables combined. The three recommended tests used to detect the onset of secondary symptoms were run against the decimal value of the poverty line, as well as the demographic variables combined. In both the second and third sets, the test results reported for 2010, 2009, 2008 and before 2008 were included.
The independent variables for the fourth regression set, shown in Table 2.13, included whether patients have received the recommended tests, including four A1C tests, one neuropathy test, and one retinopathy test. The three tests are also combined as a fourth binary category. These variables were run against the population samples for Type 1 and Type 2 diabetics, as well as Type 1 & 2 combined.
Independent variables included the patients’ insurance type, such as private insurance, Medicare, Medicaid, or Tricare/ Champ VA. It is possible that some patients were enrolled in more than one insurance type. Additional independent variables are whether patients were treated for a priority condition, including a diabetes priority condition or other priority condition. The demographic variables were also included as independent variables and were run together along with each of the other independent variables
A fifth regression set included the appearance of secondary symptoms as the dependent variables and used Type 1 and Type 2 diabetics as two separate population samples. The independent variables included the insurance types as well as the demographics. This test was completed to answer the second research question regarding whether there is one insurance type more likely to delay the onset of secondary symptoms.
A strong correlation was found in Type 2 diabetics in the first regression set, which separately compared Type 1 and Type 2 diabetics against nondiabetics. Nearly all the demographics were statistically significant for Type 2, with no correlation for Type 1 diabetics. With one exception, age, gender, race and marital status were always significant among Type 2 diabetics. When “years of education” was run with the demographic variables combined, gender and Hispanic ethnicity were not significant.
It is no surprise that as you get older, your age increases the likelihood of getting Type 2 diabetes by 0.8% each year. For example, at age 50, you are 40% more likely to be diagnosed with the disease
* Only statistically significant variables are presented
just from a statistical perspective. Being female and being married reduces the likelihood that you will get Type 2 diabetes by a small percentage.
Table 3.1 provides the correlation coefficients for Type 2 diabetes. Sadly, some of the smallest minority groups greatest chance of getting the disease, with Pacific Islanders (19% more likely) and Native Americans (21.4% more likely), and the high percentages are not due to a sampling error. The Native American tribe of the Tohono O’odham in southern Arizona has the highest rate of diabetes in the world. Until 1960, no one on the reservation had diabetes, but when these populations changed from pursuing their historically active lifestyles and eating their traditional foods (such as the prickly pear cactus) to the sedentariness and processed foods of Westernization, it triggered their genetic predisposition to the disease (Block 2009). The Tohono O’odham are indicative of other Native American tribes including those in Alaska. Pacific Islanders also have an active history of fishing and traveling on the water by canoe, and their sedentary lifestyle here in the mainland is affecting them negatively.
Type 2 diabetics were statistically significant in 10 of the 13 socioeconomic indicators studied. Every point that you are above the poverty line increases the likelihood of avoiding diabetes by 1%, and every additional year of education that you have reduces the likelihood of developing the illness by 1.6%. However, the negative correlation in both “ever insured in 2009” and “uninsured in 2009” is mysterious and warrants more attention. In the fourth regression, I break down the insured by insurance type to see which provided the most coverage and had the fewest out-of-pocket expenses.
In the Type 1 regressions of the first set, “percentage of poverty line” tested as expected. It was not a significant factor in having Type 1 diabetes. However, “financial reason delayed in getting care,” “delayed in filling prescriptions,” “financial reason for delay in filling prescriptions,” and “uninsured in all of 2009” all tested as significant, with T-scores of 2.09, 3.62, 2.86 and 2.03 respectively. Coefficients for those variables were 0.006, 0.010, 0.010 and 0.003, respectively. Age, gender, race and marital status were not significant in any of the Type 1 regressions. Results of all four regression sets are available in
T-Score and coefficient format in Appendix B.
In the second regression set, which compared the onset of secondary symptoms with demographic variables and socioeconomic indicators, the following correlations were found:
Average correlation coefficients of demographics in Type 1 nephropathy, and Type 2 nephropathy and retinopathy in the second regression set are provided in Table 3.2. And Table 3.3 provides average correlation coefficients for “age” and the “decimal value of the poverty line” in the
other secondary symptoms for Type 1, Type 2 and nondiabetics. The higher coefficients in age among Type 1 diabetics increase the likelihood of developing these other secondary symptoms at a faster pace than even Type 2 diabetics, who develop the symptoms at about twice the speed of nondiabetics and only have larger coefficients in high blood pressure and high cholesterol. There was no correlation between Type 1 diabetics and the “decimal value of the poverty line” for the other secondary symptoms, which indicates that Type 1 diabetics are not linked to socioeconomics.
The third regression set was very similar to the second set except the dependent variable, “years with the disease” was added to distinguish it from age. Age and years have some collinearity and can even be confused with each other in terms of their effect on diabetes. Although age plays a role, years with the disease is more indicative of the onset of secondary symptoms. For example, someone who is 50 years old and has had the disease for three years is going to be in a different circumstance than another 50-year-old who has had the disease for 30 years. Table 3.4 lists correlation coefficients for nephropathy and retinopathy that compared age-related variables in the third and second sets. As listed
in Table 3.5, age was not significant for other secondary symptoms for Type 1 diabetics, but it was in Type 2 diabetics for most of the other secondary symptoms. Years with the disease was significant for a majority of the secondary symptoms for both Type 1 and Type 2 diabetics.
The fourth regression set included Type 1, Type 2 and Type 1 & 2 combined as population samples. In comparing insurance type with the recommended tests that measure the appearance of secondary symptoms, there was a positive correlation with private insurance in receiving the recommended neuropathy and retinopathy tests but not for receiving A1C tests among all population samples. Type 2 Medicare patients were more likely to receive the required A1C tests, as well as receive all the recommended tests, although the individual neuropathy and retinopathy variables were not significant by themselves. Neuropathy by itself is only significant when Type 1 & 2 diabetics were combined. Among Medicaid and Tricare/ Champ VA, there was no significance. Table 3.6 lists the correlation coefficients related to insurance type.
Type 1 diabetics who are treated for nondiabetic priority conditions are more likely to spend a lower percentage of income on medical and prescription expenses because they have a tighter budget and less expendable income. In the variable “treated for priority conditions other than diabetes,” there was a negative correlation among Type 1 diabetics in the categories related to the percentage of income spent on medical and prescription expenses. Among Type 2 diabetics, however, no variables were significant.
As shown in Table 3.7, there was also a positive correlation between “priority codes” and meeting the A1C standard for all population samples, and among Type 2 diabetics and Type 1 & 2 combined, there was positive correlation in receiving each of the recommended tests. For the diabetes priority conditions, there was also a positive correlation in receiving each of the recommended tests among Type 2 diabetics, but among Type 1 diabetics, there was no significance.
In the demographics, there was a negative correlation among Asians in receiving the neuropathy test. There was also a negative correlation among Asians and Hispanics in receiving the retinopathy test. African and Asian Type 2 diabetics were also less likely to receive all the recommended A1C tests. Age
was significant (positively correlated) among Type 2 diabetics in meeting the neuropathy, retinopathy and all three test categories combined, although A1C by itself was not significant. In comparing the retinopathy test and marital status, married Type 2 diabetics were more likely to receive the test, and married Type 1 diabetics were less likely to receive it.
Results of the fifth regression set, which compared insurance types with the appearance of secondary symptoms, indicated a negative correlation for the Type 1 variables of nephropathy and stroke as well as the Type 2 variables for eight of the nine variables, including nephropathy, retinopathy, high blood pressure, coronary heart disease, angina, heart attack, other heart disease and stroke (see Table 3.8).
The negative correlation indicates that patients with private insurance are less likely have these secondary symptoms. Since this sample is from one year, I am not able to assess whether these patients would be less likely to develop these conditions over time. Tricare/Champs VA had a negative correlation in the one variable for which there was significance, which was heart attacks among Type 2 diabetics.
The significant variables for Medicaid and Medicare insurance types were positive, meaning that its patients were more likely to have the appearance of these secondary symptoms. As expected, age was significant for both Type 1 and Type 2 diabetics, meaning that the older patients were the more likely they would have these secondary symptoms. However, there was no correlation for nephropathy and retinopathy for Type 1 diabetics, which may indicate that years with the disease, as discussed in the third regression set, may play a more important role in the development of these symptoms. Findings among the demographics in the fifth regression set include the following, as shown in Table 3.9.
For Type 1 diabetics:
For Type 2 diabetics:
The following is a review of the problem statements and hypotheses in this study, which compared the coverage of private insurance, Medicaid, Medicare, and Tricare/ Champ VA insurance types among diabetic patients.
In the fourth regression set, only two of the four insurance types delivered statistically significant variables, when compared with receiving the recommended annual tests that measure the onset of secondary symptoms. Private insurance and receiving the neuropathy and retinopathy testing recommendations were positively correlated with both Type 1 and Type 2 diabetics. Medicare positively correlated only with Type 2 diabetics for providing the recommended four A1C tests, and there was also correlation between Medicare and the variable for meeting all three standards. However, the variables for neuropathy and retinopathy exams were not statistically significant individually when run against Medicare.
For this research question, the null hypothesis was rejected. It stated that the insurance type would make no difference on whether diabetics receive the recommended tests, but two of the three had statistical significance. Medicare can be commended for its positive correlation with the recommended A1C tests and all the tests measured together for Type 2 diabetics, but unfortunately, it is not making the grade with Type 1 diabetics in the same categories.
Among the nine secondary symptom variables tested separately for Type 1 and Type 2 diabetics in the fifth regression set, private insurance was significant in eight variables for Type 2 diabetics and in two for Type 1 diabetics. Each of the significant variables had negative coefficients, indicating that patients who had private insurance were less likely to develop the secondary symptoms, so null is rejected in regards to private insurance. Although Medicare and Medicaid had a similar number of significant variables, their coefficients were positive, indicating that their patients were more likely have these conditions. To be fair, patients who have Medicaid are unable to receive benefits from an employer, or they are unable to work due to a medical condition. If the medical condition that keeps them from working is diabetes, the patient likely developed the secondary symptoms by the time they enrolled in the insurance plan. Medicare patients may have developed the secondary symptoms before the time of enrollment, as well. I cannot determine what lack of resources or behaviors over time caused the secondary symptoms to develop, but it is likely due to old age (Medicare) or poverty (Medicaid).
New cases of both Type 1 and Type 2 diabetes have continued to occur at a faster rate for most of the last four decades. The CDC (2015) reports that the number of new diabetes cases of patients aged 18-79 years has been increasing each year by hundreds of thousands since 1980. It peaked in 2009 at
1.7 million new cases, but surprisingly, the number has declined since, with 1.4 million new cases reported in 2014. Perhaps the decline is a positive result of efforts to curb the obesity epidemic and the Affordable Care Act, which provided millions of previously uninsured patients with access to medical insurance. However, diabetics are not getting diagnosed as effectively as they should. An estimated 27.8% of diabetics in the United States are undiagnosed (CDC 2014), and providing healthcare to everyone improves the likelihood that diabetics will be diagnosed and can begin to manage their symptoms before a severe and costly medical emergency occurs.
The undiagnosed are among a number of diabetics who are in denial about the severity of their symptoms and choose to continue to eat the diet they always have and to live the way they have always lived. In a study of diabetics’ perceptions of their treatment, both Type 1 and Type 2 diabetics ranked their medications as significantly more important than diet or exercise for controlling diabetes. In the study, medication adherence was associated with lower perceived consequences of diabetes, higher personal control, lower distress and fewer symptoms (Broadbent et al. 2011). Patients who have more to live for such as the companionship of family or volunteer work at charity organizations are more likely to experience delayed secondary symptoms, improved quality of life and reduced medical costs.
By maintaining steady and healthy A1C levels, diabetic patients can delay the onset of secondary symptoms, which are largely the cause of more than 50% of annual medical expenses associated with diabetes. Dall, et al. (2014), a similar study to his 2009 report, provided an updated total of
$175.8 billion annually (in 2012) for the national medical costs associated with diagnosed diabetes. He estimated that an additional $68.6 billion was spent on nonmedical costs, such as the inability to work and reduced productivity. Delaying secondary symptoms can reduce both medical and nonmedical costs for individuals, healthcare providers and the national economy.
Providing simple tests on a consistent basis will help patients to better manage their diabetes and ultimately reduce the onset of secondary symptoms until later in life, preferably in their later years, when they can better prepare financially for the cost of treating these conditions and rely on retirement income. Delaying these symptoms until retirement will allow diabetics to stay in the workforce when they still need to, provide for their families, and afford more comprehensive private insurance and disability insurance instead of having to rely on Medicaid which is more costly to the economy.
Unfortunately, there is not enough emphasis on providing diabetics with these tests at the recommended intervals, as evidenced by the regression results in my study. Affordable healthcare has been the narrative in the United States for years, and the goal of “affordable” should be for both the short term and long term. Mobilizing healthcare professionals to perform these inexpensive tests will help to reduce the overall cost of diabetes over time and help to improve quality of life. Publicity efforts to encourage diabetics to receive these tests are also well warranted, and patients who receive the recommended tests each year and have A1C levels within range could receive incentives such as tax deductions.
Diabetics on Oral Medications. Type 2 diabetics who take oral medications to manage their symptoms constitute 56.9% of all diabetics (CDC 2014). In some cases, Type 2 diabetics on oral medications who lose weight, increase exercise and improve their diets can put symptoms into remission, although the lifestyle changes must be maintained for the improved health to continue (Gregg, et al. 2012). Remission is a superb response, but stabilizing their condition to delay insulin dependence is also a worthy effort. Oral meds are more affordable than insulin and less encroaching on lifestyle. All diabetics benefit from maintaining healthy A1C levels, but focusing on those who take oral medications may be the most cost effective in efforts of reducing expenses over time.
Prediabetics. Another population that should be given focus is prediabetics, who the CDC (2014) estimates at 86 million, or 37% of the U.S. population. Dall, et al.(2014) quantified the medical costs associated with prediabetes at $43.9 billion. Rewinding symptoms through improved diet and increased exercise is done much more easily in a prediabetes state and with fewer long-term consequences than pursuing the effort once you have crossed the diabetes threshold.
Improving Medicare and Medicaid. Diabetics with Medicare and Medicaid who are respectively elderly or disabled often have already developed secondary symptoms at the time of enrollment. However, the Medicaid expansion as a result of the Affordable Care Act in recent years provides patients who have been uninsured previously (some for lengthy periods) with viable access to healthcare. These patients need special attention to help them to make lifestyle changes that will delay the progression of their disease. Significant improvement can occur among these patients regarding their quality of life and the prevention of costly medical expenses in the short term that will help us reduce costs overall.
The Affordable Care Act has provided many with access to healthcare, but there is still work to be done, particularly in efforts to reduce premiums and prescription costs. In particular, Medicare is hit hard regarding out-of-pocket costs for prescriptions. Establishing price control mechanisms for pharmaceuticals is an essential first step. Over time, we are seeing that applying traditional supply and demand models to medical markets is dysfunctional. Providing effective care and affordable medications to those who depend on them for sustaining life is in the best interest of our communities and national economy.
4.2. Limitations and Topics for Further Study
As I have discussed, my study used the 2009 Medical Expenditure Panel Survey, a self-reported data set of one year in time that is as accurate as the information that patients and doctors provided. A number of patients were removed from my study because they did not include information that would evaluate them comparably with the other patients. The use of a proprietary and more detailed source would provide additional measures, including more definitive ICD9 codes or comparable coding with which to conduct a study.
The use of data provided by multiple private insurance companies would offer a more detailed comparison of the care they provide, particularly in regard to diabetes coverage. Versions of such a study are being conducted by the insurance companies themselves, as well as human resources departments of major employers, which is why each insurance provider closely competes in the national marketplace. This information is not provided to consumers because of the desire of employers to emphasize wages or the products or services that they produce as a larger vehicle in which to drive employee morale.
Further, a multi-year study is needed to track patients’ progress in response to changes in healthcare legislation, including the Affordable Care Act, as well as any changes that are passed by the Republican Congress.
In the United States, there is a link between poverty and the onset of Type 2 diabetes, as well as the onset of secondary symptoms associated with the disease. When impoverished diabetics are unable to manage their symptoms and an emergency occurs, healthcare costs are much greater for them, as well as healthcare providers and the economy. We will all save money if the impoverished are provided with access to healthy foods, as well as affordable prevention techniques, such as appropriate diabetes medications, A1C blood tests, and assistance from healthcare professionals to evaluate and adjust their medications regularly.
