The Direct and Indirect Financial Burden of Utilizing Canadian Health Care Services

By: Istvan Kery

Abstract

The topic of this research paper is the financial burden of utilizing health services based on limited

public insurance and the financial cost of supplemental insurance. The paper will explore how this

financial burden affects low-income households’ health care utilization in Canada. It will attempt to

find evidence that low-income households face a ‘double disadvantage’ where low-income

households directly face a barrier to pay for health services without public insurance and indirectly

face a barrier to pay for health services from the financial cost of supplemental health care

insurance. The research finds low-income Canadian residents are unable to pay for health services

directly and are unable to pay for supplemental health insurance, the owning of which would reduce

health service cost. Ultimately, the research does provide evidence that Canadian residents face a

financial barrier based on household income that limits their use of health services and suggests

they face a financial double disadvantage.

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1. Introduction

The provision, financing, and delivery of health care services in Canada have undeniable economic

and social significance. Canadians’ general well-being derives from their ability to utilize these health

care services. The Canadian Health Act (CHA) is a foundational legislation that attempts to provide

universal public coverage for physician and hospital services, regardless of ability to pay. Accessibility

is one of the CHA’s five pillars, which states residents of Canada will be provided uniform insurance

that grants reasonable access to medically necessary health services. The public insurance compensates

patients for the financial cost of these health services.

The limitation to this pillar is there are important health services that are not considered ‘medically

necessary’ and thus are not included in the universal public insurance plan. Specifically, Canadian

residents are not compensated for the cost of prescription drugs, dental services, and eye care services.

Without public insurance, these services can be a financial burden on Canadian residents because it is

highly likely that residents will require these services on multiple occasions. This burden creates a

demand for supplemental health care insurance as a complement to the CHA public insurance. In

Canada, the federal government, employers, or private services provide the supplemental insurance.

These forms of insurance are not considered universal because there is a financial cost to holding the

insurance and this cost can be a barrier to obtaining the insurance. Thus, holding supplemental insurance

can also be a financial burden for Canadian residents.

The topic of this research paper is the financial burden of utilizing health services based on limited

public insurance and the financial cost of supplemental insurance. The paper will explore how this

financial burden affects low-income households’ health care utilization in Canada. It will attempt to find

evidence that low-income households face a ‘double disadvantage’ when utilizing health services

excluded from the CHA. The ‘double disadvantage’ implies low-income households directly face a

barrier to pay for health services without public insurance and indirectly face a barrier to pay for health

services from the financial cost of supplemental health care insurance. Ultimately, these barriers could

force Canadian residents to utilize fewer health services than they actually require.

The paper will ask two parallel research questions:

1. Do high-income households utilize more health care services than low-income households?

2. Do residents with supplemental health insurance utilize more health care services than those

without supplemental health insurance?

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The intuition underlying these questions is formed from two hypothesized relationships. First, it is

hypothesized there is a positive relationship between health service utilization and income, where higher

income leads to higher utilization. High-income households are able to afford more services excluded

from the CHA. Second, it is hypothesized there is a negative relationship between health service

utilization and the price of health services, where lower prices lead to higher utilization as a decrease in

price increases demand. Supplemental insurance enters the relationship because insurance reduces the

price for health care services for households, which increases utilization. Therefore, two hypotheses will

be made:

Hypothesis 1:  Households with high income will utilize more health care services than households

with low income.

Hypothesis 2:  Residents from high-income households are more likely to hold supplemental health care

insurance.

The research will explore the direct and separate effects that household income and

supplemental health insurance have on health care utilization. The intent of this research is find

evidence for hypothesis 1. The research will then explore the direct effect household income has on

supplemental insurance. The intent of the research is to provide evidence for hypothesis 2. These

hypotheses will be used to find evidence for the double disadvantage that low-income households

experience. The research will attempt to find evidence that low-income residents directly utilize

fewer health services due to the financial burden of the services, and low-income residents

indirectly utilize fewer health services due to the financial burden of supplemental insurance, the

owning of which tends to induce health service utilization.

2. Literature Review

After review and research of related literature, there is evidence to support that there are

financial barriers restricting Canadian residents from utilizing the full range health services in

Canada. These barriers are present in both level of household income and access to supplemental

health insurance. The review provides evidence for both of these literatures and demonstrates how

exploring the double disadvantage contributes a new perspective to the existing literature.

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2.1 Household Income and Health Care Utilization

Allin and Hurley (2009) found evidence for the hypothesis on household income by

demonstrating there is an income gradient present in the utilization of health services. Specifically,

private financing of prescription drugs demonstrates households with higher incomes can purchase

more prescription drugs. The authors used the Canadian Community Health Survey (CCHS) to find

income creates a ‘pro-rich’ inequity, where those with higher incomes are more willing to pay out-

of-pocket for health services. The authors also found there is a pro-rich inequity with physician

visits because prescription drugs are private cost complements to a visit. This implies individuals

who can afford to pay for prescription drugs will also utilize more physician visits, assuming

individuals use these visits to gain access to prescription drugs. Therefore, Allin and Hurley

suggested a more universal and accessible health care system would benefit more Canadian citizens

by reducing the cost of private cost complements.

Additionally, Asada and Kephart (2007) also found an income gradient by using a two-part

model to provide evidence that lower income is correlated with fewer physicians and specialist

visits. The authors used the CCHS and attempted to control for medically necessary services in their

methodology in order to isolate the potential scenario that individuals were not using other

important services because they could not afford them. In essence, the authors uncovered a crucial

question about income inequities that must be considered in the Canadian health system and in this

paper.

Williamson and Fast (1998) conducted a specific survey-based research study in Edmonton.

The goal of the project was to determine whether there was a relationship between poverty and

medical treatment in the city. Their survey found 38% of respondents failed to obtain physician

services when they were ill, and 40% did not fill out prescriptions because they could not afford the

cost. The value in this approach was they divided the impoverished demographic into two different

categories: working poor and social assistance beneficiaries. Their distinction is important to

understanding the financial barriers low income creates in the health system. The surveys showed

93% of the working poor demographic could not afford to fill out prescriptions, and they were not

even able to afford the reduced Blue Cross health insurance. The social assistance beneficiaries

were less restricted for purchasing the prescription drugs because additional public health insurance

is included in their assistance. The limitation to this research study is the geographic specificity.

There are undeniable differences in the social and economic circumstances of each province and

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city, which limits the credibility of extrapolating these research results nation-wide. However, the

research study still contributes evidence that low income creates a financial barrier to health

services.

2.2 Supplemental Insurance and Health Care Utilization

Stabile (2001) contributes to this literature with his paper on the propensity to purchase

supplemental insurance if tax exemptions can be offered on these plans. The variation in provincial

tax laws, specifically Quebec’s taxation of employer provided health insurance, offers a point of

analysis because Stabile was able to consider the effect of various sized tax exemptions. Stabile

found this process allowed him to isolate the moral hazard effect, where individuals utilize more

health services because they hold insurance, not because their need has changed. This does not

contribute to adverse selection because the motivation for adverse selection is being sicker, not

experiencing increased tax exemptions. Stabile found almost half of the increase in utilization based

on supplemental insurance is a result of the moral hazard effect. This effect provides further

evidence that lack of supplemental insurance is a barrier to utilizing more health services.

Devlin et al. (2011) provide further research to the literature by considering the effect of

supplemental insurance on the utilization of prescription drugs. The paper ultimately concluded the

reimbursement from insurance packages for private health costs encourage the utilization of these

services. With the 2005 CCHS, the authors used a latent class model for the analysis where the

population is divided into two categories: high service users and low service users. This implied

high service users are those individuals who are sick or have chronic diseases and low service users

are those individuals who are healthy. The analysis showed the high service users’ propensity to

utilize services is unaffected by obtaining insurance, which intuitively implies those who are sick

will still be willing to utilize services without insurance. However, the propensity for low users to

increase their utilization increases when insurance can be obtained. This heterogeneous result is

valuable for the literature because it isolates lack of insurance as a barrier to utilizing health

services.

Kapur and Basu (2005) further contribute to the literature by examining how combined

forms of insurance coverage for prescribed drugs affect their purchase. The authors compiled a

micro-dataset from multiple national health surveys. For this examination, the authors stated two

broad categories of prescribed drug insurance coverage: catastrophic and conventional insurance.

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Catastrophic plans are for households that have high drug expenses relative to their household

income, and conventional plans are the packages that include sizeable deductibles for the prescribed

drugs. Kapur and Basu (2005) found when the forms of coverage overlap, 96% of Canadians have

some coverage for prescribed drugs. However, when this overlapping coverage was decomposed,

only 25% of these residents were insured publicly. This suggested the majority of respondents

solely held private prescription drug insurance, providing evidence that prescription drug insurance

is predominantly private. Thus, if a Canadian resident is unable to hold private supplemental health

insurance, it is likely the individual is not holding public insurance either. This lack of insurance

can be a strong barrier to utilizing prescription drugs.

Nyman (1999) offered a unique perspective on the relationship between supplemental

insurance and health care utilization by suggesting demand for supplemental health insurance is for

its access motive. Nyman found supplemental health insurance was not only about reducing

financial risk, but also about gaining access to crucial services for which an individual would

otherwise pay. He found by overlooking the access motive, economists will be missing an

important, albeit intangible, benefit to supplemental health insurance. This research demonstrated

supplemental health insurance not only protects individuals in medical emergencies, but can also

remove barriers to accessing other crucial health services by reducing or covering the immediate

expense. This research paper builds from this idea by highlighting the positive relationship between

insurance and health care utilization, ultimately demonstrating an individual is at a disadvantage to

utilizing health services without insurance.

2.3 Income-Insurance Relationship

The literature demonstrates socioeconomic status of Canadian citizens is a common thread

between income and insurance. In most of the research there was evidence suggesting both low

income and lack of supplemental insurance are characteristics of low socioeconomic status. Also, it

is found low socioeconomic status also contributes to lower health service utilization. Curtis and

MacMinn (2008) contribute to this relationship by examining the post CHA years to determine

whether there had been a change in the access to medical services, and specifically, if the change in

access had been a result of inequity caused by socioeconomic status. Consistent with previous

findings, the authors demonstrated there is a positive relationship between health care utilization

and holding supplemental insurance. It should be recognized the findings demonstrated low

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education levels also contribute to lower utilization levels as compared to wealthier individuals with

higher education.

In another study completed in Sweden, Lundberg et al. (1998) found similar results where

low socioeconomic status individuals, characterized by low income and education, were more

sensitive to price changes in prescription drugs. This implies having low socioeconomic status

makes prescription drugs less accessible should the prices increase. Lundberg et al. (1998) further

explained policy decisions should not only encourage increased public funding, but should

encourage increased education for this demographic, while simultaneously developing the

neighbourhoods and regions to increase socioeconomic status.

This research paper will contribute to the literature on household income, supplemental

health insurance, and health care utilization by providing further evidence about the relationship

between income and supplemental insurance. Specifically, the paper will demonstrate household

income affects health service utilization directly through service payment and indirectly through the

financial burden of paying for supplemental insurance. The analysis will show income and

insurance separately affect health care utilization, but the contribution to the literature will be

demonstrating how income affects utilization indirectly via the relationship between household

income and supplemental insurance.

3. The Data Set

The data comes from Statistics Canada’s Canadian Community Health Survey (CCHS),

cycle 3.1. The CCHS is a national and cross-sectional survey conducted every 2-years. Cycle 3.1

was collected between January 2005 and December 2005. The CCHS collects information through

telephone survey and self-administered questionnaires that include a wide range of health-related

questions targeting household and individual level information. Cycle 3.1 was chosen because it

includes detailed information about supplemental health insurance and includes multiple measures

of health care utilization. Specifically, this paper will use general practitioner (GP) visits, dental

visits, and eye exams, as the measures of utilization for the research analysis.

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3.1 Sample Restrictions

Geography Restriction: The sample is restricted to respondents who are residents of Ontario. Cycle

3.1 only collected insurance coverage data for residents in Ontario, so a national analysis cannot be

completed. Also, there would likely be variations of supplemental insurance plans inter-

provincially, which would complicate the regression analysis. Therefore, the most effective analysis

will occur with sample data from Ontario residents. The CCHS sample of 132,221 respondents

decreases by 62,908 with this restriction.

Age Restriction: The sample was restricted to respondents 18-64 years of age. Respondents who are

65 or older complicate the analysis because this resident demographic has public insurance

coverage for prescription drug use. Supplemental prescription drug insurance is one of the main

independent variables being analyzed for the hypotheses of this paper. Respondents who are 18 or

younger are more likely to live with a parent or guardian. The respondents would likely be

beneficiaries of a parent’s insurance, if the parent held insurance, which complicates the health care

utilization patterns of the demographic. Ultimately, utilization patterns still centre on the

respondents who hold supplemental insurance, which is why this restriction is applied. The CCHS

sample of 132,221 respondents decreases by 40,431 with this restriction.

3.2 Dependent Variables

Health care utilization will be measured in three forms: visits to GP, visits to dentist and

receiving an eye exam. Regular dentist visits and eye exams are health services excluded from the

CHA, making the services a valuable point of analysis. GP visits are covered under the CHA and

purchasing prescription drugs is a complementary health service. However, prescription drugs are

excluded from the CHA, so GP visits is used to make inference about prescription drug utilization.

Table 1 – Summary Statistics: Utilization Variables

Variable Obs Mean Std. Dev. Min Max

# of GP visits in last 12m 28814 3.128 4.378 0 31

Dentist visit in last 12m 28882 0.674 0.469 0 1

Eye exam in last 12m 28882 0.353 0.478 0 1

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Table 1 shows, on average, respondents in the sample make 3.12 visits to the GP. Dentist visits and

eye exams are measured by the probability a respondent made a visit or had an exam in the last 12

months. Table 1 shows approximately 67% of the sample made a dentist visit and 35% of the

sample had an eye exam in the last 12 months.

3.3 Independent Variables – Socio-demographic

The socio-demographic independent variables are: age, sex, highest level of education, and

household income. Each variable was converted to a binary variable to isolate respondents in the

sample to a specific category. Table 2 shows the percentage respondents in each category.

Table 2 – Summary Statistics: Socio-demographic Variables

Variable Obs Mean Std. Dev. Min Max

Income Less than $15k 28882 0.057 0.232 0 1

Income $15k - $30k 28882 0.093 0.291 0 1

Income $30k-$50k 28882 0.173 0.379 0 1

Income $50k-$80k 28882 0.245 0.430 0 1

Income greater than $80k 28882 0.316 0.465 0 1

Income not stated 28882 0.116 0.320 0 1

Age: 18-19 28882 0.043 0.204 0 1

Age: 20-24 28882 0.083 0.276 0 1

Age: 25-29 28882 0.096 0.295 0 1

Age: 30-34 28882 0.114 0.317 0 1

Age: 35-39 28882 0.119 0.324 0 1

Age: 40-44 28882 0.127 0.333 0 1

Age: 45-49 28882 0.098 0.297 0 1

Age: 50-54 28882 0.105 0.306 0 1

Age: 55-59 28882 0.113 0.317 0 1

Age: 60-64 28882 0.102 0.303 0 1

Female 28882 0.533 0.499 0 1

Male 28882 0.467 0.499 0 1

No high school diploma 28882 0.060 0.238 0 1

High school diploma 28882 0.115 0.319 0 1

Some Post Sec. Education 28882 0.053 0.224 0 1

Uni/College Degree 28882 0.695 0.460 0 1

Education level not stated 28882 0.077 0.266 0 1

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The respondents report household income levels ranging from under $15,000 to $80,000 or above.

Approximately, 55% of the sample have household income over $50,000 and 15% have

income under $30,000. The sample includes households that tend to have a higher income.

The age distribution of respondents is relatively even as each age category contains approximately

10% of the sample. Age categories 18-19 and 20-24 represent the smallest percentage of the

sample at 4.3% and 8.3%, respectively.

Table 2 shows approximately 47% of the sample is male and 53% of the sample female.

The respondents report education level as having no high school diploma, a high school diploma,

some post-secondary education, or a university or college degree. Table 2 shows almost

70% of the sample has received a university or college degree, which suggests the sample

may have a high education bias.

3.4 Independent Variables – Health Status

The health status variable is collected from the self-reported health status of the respondents.

The respondents are asked to evaluate their current health status between five categories: excellent,

very good, good, fair, or poor. Table 3 also includes a variable for chronic conditions, where

respondents report if they have a chronic health condition. The variable does not specify the health

condition, so the severity of the respondent’s condition has a wide range. For example, the

respondent may have asthma or diabetes.

Table 3 - Summary Statistics: Health Status Variables

Variable Obs Mean Std. Dev. Min Max

Excellent HS 28882 0.225 0.418 0 1

Very Good HS 28882 0.393 0.488 0 1

Good HS 28882 0.272 0.445 0 1

Fair HS 28882 0.080 0.272 0 1

Poor HS 28882 0.029 0.168 0 1

Health Status not stated 28882 0.000 0.022 0 1

Has chronic health condition 28882 0.717 0.450 0 1

Table 3 shows almost 90% of respondents report health status as ‘good’ or higher. Specifically, the

majority of respondents report health status as ‘very good’ with approximately 39% of the sample

in this category. Also, Table 3 shows over 70% of the sample report having some form of chronic

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health condition. It is possible to infer these chronic conditions are less severe, as the majority of

the sample reports higher health status while simultaneously reporting a chronic condition.

3.5 Independent Variable – Insurance Coverage

The insurance variables are divided into three types: prescription drug, dental, and eye care

insurance. Respondents report whether they hold a form of insurance in each category. Each

insurance category is further divided into the provider of that insurance. Cycle 3.1 specifies the

insurance providers as federal government, employers, or private firms. Each descriptive table

shows the percentage of sample that holds some form of insurance in the respective category and

the percentage of sample for each specific provider.

Table 4 – Summary Statistics: Drug Insurance Variables

Variable Obs Mean Std. Dev. Min Max

Has drug insurance 28882 0.733 0.442 0 1

No drug insurance 28882 0.267 0.442 0 1

Has gov't drug insurance 28882 0.083 0.276 0 1

Has employer drug insurance 28882 0.606 0.489 0 1

Has private drug insurance 28882 0.043 0.202 0 1

Table 4 shows approximately 73% of the sample holds some form of prescription drug insurance.

After decomposing the provider of the insurance, Table 4 shows an overwhelming majority of

respondents, over 60%, receive employer provided drug insurance. This is compared to 8.3% of the

sample that has government insurance and 4.3% of the sample that has private insurance.

Table 5 – Summary Statistics: Dental Insurance Variables

Variable Obs Mean Std. Dev. Min Max

Has dental insurance 28882 0.688 0.463 0 1

No dental insurance 28882 0.312 0.463 0 1

Has gov't dental insurance 28882 0.064 0.245 0 1

Has employer dental insurance 28882 0.587 0.492 0 1

Has private dental insurance 28882 0.035 0.183 0 1

Table 5 shows approximately 68% of the sample holds some form of dental insurance. Similar to

table 4, decomposing the provider of the insurance in table 5 shows approximately 58% of

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respondents receive employer provided dental insurance. This is compared to 6.4% of the sample

that has government insurance and 3.5% of the sample that has private insurance.

Table 6 – Summary Statistics: Eye Care Insurance Variables

Variable Obs Mean Std. Dev. Min Max

Has eye care insurance 28882 0.622 0.485 0 1

No eye care insurance 28882 0.378 0.485 0 1

Has gov't eye care insurance 28882 0.060 0.238 0 1

Has employer eye care insurance 28882 0.530 0.499 0 1

Has private eye care insurance 28882 0.031 0.172 0 1

Table 6 shows approximately 62% of the sample holds some form of eye care insurance. Again,

decomposing the provider of the insurance in table 6 shows the majority of respondents receive

employer provided eye care insurance, approximately 53%. This is compared to 6.0% of the sample

that has government insurance and 3.1% of the sample that has private insurance.

4. Methodology

This paper will be using the ordinary least square (OLS) method to estimate the unknown

parameters in a regression model. The regression models will follow:

y=β0+β1 X1+ β2 X2+… βn Xn+ε

This method will find estimate values for β coefficients of the independent variables, which will be

used to estimate the linear relationship between health care utilization and income and supplemental

insurance. This estimate will represent the slope of the estimated OLS line between the dependent

variable and the independent control variables. A positive coefficient estimate suggests as the

independent variable increases, the dependent variable increases. A negative coefficient estimate

suggests as the independent variable increases, the dependent variable decreases. Hypothesis testing

will be used to determine the statistical significance of each β coefficient. The hypothesis test will

be:

H0: β1 ¿ 0HA: β1 ≠ 0

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This means if a coefficient estimate is not equal to 0 it will demonstrate there is some linear

relationship between the dependent and independent variable. The statistical significance of this

estimate will be evaluated with 95% confidence. This confidence interval was selected because the

CCHS sample was significantly reduced after making the sample restrictions. The smaller sample

reduces the accuracy of estimate on the true population parameter, so a slightly lower confidence

interval more effectively represents statistical significance of the coefficient estimates.

5. Regression Analysis

In order to provide evidence for the research hypotheses and to address the larger research

questions, the regression analysis has been divided into two sections. The first section provides

evidence for the separate effects of household income and supplemental insurance on health care

utilization. Health care utilization is represented differently in each model as GP visits, dentist

visits, and eye exams to provide extensive evidence of the effect of income and insurance. The

second section explores the relationship between household income and supplemental insurance and

how income could effect utilization via supplemental insurance and contribute to the double

disadvantage faced by low-income residents.

5.1 Household Income and Supplemental Insurance on Utilization

The first regression models focused on how income and prescription drug insurance affected

the number of GP visits made by respondents as a form of health care utilization. Separate models

were used to determine the effects before and after controlling for other variables that affect health

care utilization. Column 2 shows the income coefficient estimates without control variables and

column 3 shows the drug insurance coefficient estimate without control variables. Column 4 shows

income and drug insurance estimates with control variables. Column 5 shows estimates for specific

insurance types with control variables.

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Table 7 – Estimates of income levels and holding prescription drug insurance on decision to visit GP

Independent Variable # of GP visits in last 12m

# of GP visits in last 12m

# of GP visits in last 12m

# of GP visits in last 12m

Income Less than $15k 1.870*(0.135)

0.864*(0.130)

0.681*(0.134)

Income $15k - $30k 1.3148(0.102)

0.590*(0.100)

0.489*(0.101)

Income $30k-$50k 0.591*(0.076)

0.276*(0.073)

0.243*(0.073)

Income $50k-$80k 0.333*(0.064)

0.095*(0.060)

0.089*(0.060)

Has drug insurance 0.412*(0.055)

0.433*(0.055)

Has gov't drug insurance 0.918*(0.103)

Has employer drug insurance 0.333*(0.057)

Has private drug insurance 0.258*(0.119)

β0 Estimate 2.677*(0.039)

2.729*(0.047)

0.093*(0.156)

0.161*(0.155)

n 28882 28882 28882 28882

R2 0.012 0.002 0.151 0.152

Note: Parentheses represents the standard error * significant at 5% confidence level

Income category $80k or higher was omitted from the regression model to serve as the

reference category for the coefficient estimates. The β0 estimate in column 1 represents the effect

of having income greater than $80 000, where having this level of income is associated with having

made approximately 2.7 visits to the GP in the last 12 months. The estimates of the other income

categories demonstrate there is a clear income gradient to making GP visits. Specifically,

respondents with lower income make more visits to the GP. Table 7 shows a household with

income less than $15 000 has made 1.87 more visits to the GP in the last 12 months, than a

household with income greater than $80 000. A household with income between $50 000 and $80

000 only makes 0.33 visits more than a household with income greater than $80 000. Therefore, as

the household income increases, respondents make fewer visits to the GP.

Column 4 shows, after controlling for age, sex, health status, and highest attained education

level, this income gradient persists, but magnitude of the estimates decrease. A household with

income greater than $80 000, represented by the β0 estimate, had made approximately 0.1 visits to

the GP. Households with income less than $15 000 had made 0.86 more visits, and a household

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with income $50 000 to $80 000 only made 0.1 more visits. This still provides evidence for the

income gradient, but the smaller estimate magnitudes imply the estimates in column 2 were

overstated because of omitted variable bias. Estimates in column 4 demonstrate income only has a

partial effect on the decision to visit the GP. Though the magnitudes suggest income has virtually

no effect on GP visits, in relative terms, a low-income household makes almost one full visit more

than households in the highest income category. This further provides evidence for the income

gradient.

Table 7 also shows the estimated effects of holding prescription drug insurance on having

visited the GP in the last 12 months. The β0 estimate in column 3 represents the effect of holding no

form of drug insurance, where not holding drug insurance is associated with having made

approximately 2.7 visits to the GP in the last 12 months. Table 7 shows a respondent who holds a

form of drug insurance is estimated to have made 0.4 more visits to the GP than a respondent

without drug insurance. After controlling for age, sex, health status, and highest attained education

level, column 4 shows the positive effect of holding some form of drug insurance holds true. The

effect of holding drug insurance actually increases as respondents with drug insurance made 0.43

more visits to the GP than respondents without drug insurance.

Column 5 shows the estimated effect of different prescription drug insurance types on GP

visits. After controlling for age, sex, health status, and highest attained education level, Table 7

shows each type of drug insurance has a positive effect on GP visits. The β0 estimate in column 5

still represents the effect of holding no form of drug insurance, which is associated with having

made approximately 0.16 visits to the GP in the last 12 months. A respondent with private drug

insurance made 0.25 more visits and a respondent with employer drug insurance made 0.33 more

visits. The largest effect comes from having government drug insurance where respondents made

0.92 more visits to the GP. Therefore, the positive effect of holding prescription drug insurance on

GP visits holds true regardless of the insurance provider.

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Table 8 – Estimates of self-reported health status on decision to visit GP

Independent Variable # of GP visits in last 12m

Very Good HS 0.458*(0.059)

Good HS 1.156*(0.065)

Fair HS 3.149*(0.102)

Poor HS 6.989*(0.158)

Has chronic health condition 1.246*(0.051)

β0 Estimate 0.093*(0.156)

n 28882

R2 0.151

Note: Parentheses represents the standard error * significant at 5% confidence level

There is also a noticeable gradient in terms of self-reported health status. The regression

model that controlled for income, holding drug insurance, age, sex, and highest attained education

level, showed those with lower health status made more visits to the GP. This aligns with the basic

intuition that when an individual is sicker, the individual will demand more health care services. In

this model, health status category ‘excellent’ was omitted and the β0 estimate represents the effect

‘excellent’ health status has on GP visits. Specifically, a respondent with ‘excellent’ health status is

associated with having made approximately 0.1 GP visits in the last 12 months. Table 8 also shows

respondents with ‘very good’ health status only made 0.46 more visits to the GP than those in

‘excellent’ health. However, respondents with ‘poor’ health status made 7 more visits to the GP in

12 months than respondents in ‘excellent’ health. This is a substantial change in the effect on

visiting the GP. Additionally, table 8 shows respondents who report a chronic health condition

made 1.25 more visits to the GP than respondents without a chronic condition. This aligns with the

health status gradient, where a respondent with a chronic condition is likely to demand more health

services to combat the condition.

Similar regression models from table 7 were used in table 9 to provide evidence for the

effect of income and insurance on health care utilization represented by dentist visits and eye care

exams. These models were intended to provide further evidence to the coefficient estimate patterns

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demonstrated in table 7. Column 2 shows the estimated effects of income and holding dental

insurance with control variables, and column 3 shows the estimated effects of income and specific

dental insurance type with control variables. Column 4 shows the estimated effects of income and

holding eye care insurance with control variables, and column 5 shows the estimated effects of

income and specific eye care insurance type with control variables.

Table 9 – Estimates of income levels and holding dental or eye care drug insurance on decision to visit dentist or receive eye exam

Independent Variable Dentist visit in last 12m

Dentist visit in last 12m

Eye exam in last 12m

Eye exam in last 12m

Income Less than $15k -0.194*(0.015)

-0.052*(0.016)

Income $15k - $30k -0.208*(0.011)

-0.081*(0.012)

Income $30k-$50k -0.146*(0.008)

-0.047*(0.009)

Income $50k-$80k -0.087*(0.007)

-0.050*(0.007)

Has dental (eye care) insurance

0.228*(0.006)

0.077*(0.006)

Has gov't dental (eye care) insurance

0.161*(0.013)

0.092*(0.014)

Has employer dental (eye care) insurance

0.231*(0.006)

0.074*(0.006)

Has private dental (eye care) insurance

0.239*(0.014)

0.079*(0.017)

β0 Estimate 0.585*(0.017)

0.590*(0.017)

0.282*(0.018)

0.284*(0.018)

n 28882 28882 28882 28882

R2 0.126 0.126 0.046 0.046

Note: Parentheses represents the standard error * significant at 5% confidence level

Income category $80k or higher was omitted from the regression model to serve as the

reference category for the coefficient estimates. The β0 estimate in column 2 represents the effect

of having income greater than $80 000 after controlling for age, sex, health status, and highest

attained education level. Household income greater than $80 000 is associated with a 0.59

probability of having visited the dentist in the last 12 months. Compared to the results from table 7,

the income gradient in this regression model is the opposite. The probability of having visited the

dentist increases as household income increases. Specifically, the probability of a household with

income between $50 000 and $80 000 having visited the dentist is only 9.0 percentage points lower

17

than a household with income greater than $80 000. However, the probability of a household with

income less than $15 000 having visited the dentist is 19.0 percentage points lower. Therefore, in

this model, health care utilization increases as household income increases.

Column 2 also shows the effect of holding some form of supplemental dental insurance. The

estimate demonstrates the probability a respondent with some form of dental insurance having

visited the dentist is 22.8 percentage points higher than the probability of a respondent with no

dental insurance. However, column 3 shows the estimated effect of different dental insurance types

on dentist visits. After controlling for income, age, sex, health status, and highest attained education

level, column 3 shows each type of dental insurance still has a positive effect on dentist visits. The

β0 estimate in column 3 represents the effect of holding no form of dental insurance, which is

associated with having visited the dentist with a probability of 0.59. The probability of a respondent

with employer dental insurance and private dental insurance having visited the dentist is 0.23 and

0.24 percentage points higher, respectively, than a respondent with no dental insurance.

Government dental insurance shows the smallest effect, as the probability of a respondent with

government dental insurance having visited the dentist is 0.16 percentage points higher than

respondents with no dental insurance.

Similar results are demonstrated when health care utilization is represented as receiving an

eye exam. The β0 estimate in column 4 represents the effect of having income greater than $80 000,

after controlling for age, sex, health status, and highest attained education level. Household income

greater than $80 000 is associated with a 0.28 probability of having received an eye exam in the last

12 months. The income category estimates demonstrate households with higher income have a

higher probability of having received an eye exam. The probability of a household with income

between $50 000 and $80 000 having received an eye exam is only 5.0 percentage points lower than

a household with income greater than $80 000. However, the probability of a household with

income between $15 000 and $30 000 having received an eye exam is 8.0 percentage points lower.

The smaller difference in magnitude of the estimates suggests a weaker income gradient than

estimates in column 2 with dentist visits. However, the direction of the gradient holds true, where

health care utilization increases as household income increases.

Column 4 also shows the effect of holding some form of supplemental eye care insurance.

The estimate demonstrates the probability a respondent with some form of eye care insurance

having received an eye exam is 7.7 percentage points higher than the probability of a respondent

18

with no eye care insurance. The positive effect of each insurance type is also demonstrated with eye

care insurance, after controlling for income, age, sex, health status, and highest attained education

level. The table 9 estimates show a more even distribution, where government, employer, and

private eye care insurance have a relatively even effect on the probability of having received an eye

exam. The β0 estimate in column 5 represents the effect of holding no form of eye care insurance,

which is associated with having received an eye exam with probability 0.28. The probability of a

respondent with government, employer, or private eye care insurance having received an eye exam

is 9.0, 7.0, and 7.0 percentage points higher, respectively.

In conclusion, section 5.1 of the regression analysis provides evidence for the effect of

household income and supplemental insurance on health care utilization in the form of GP visits,

dentist visits, and eye exams. Results in table 7 show respondents visited the GP more when income

was lower, whereas the results in table 9 show respondents were more likely to have visited the

dentist and to have received an eye exam when income was higher. Results in table 7 and table 9

demonstrate that holding any form supplemental insurance increases visits to the GP and the

probability of having visited the dentist or receiving an eye exam in the last 12 months.

5.2 Household Income on Supplemental Insurance

This section of the regression analysis is intended to provide evidence for the relationship

between household income and supplemental insurance. Evidence for this relationship could

contribute to the double disadvantage concept by demonstrating the overlapping effects income and

insurance have on health care utilization. The three types of health care utilization divide the

regression models in this section and the results tables show how income level affects the

probability of having insurance for each utilization type.

19

Table 10 – Estimates of income levels on probability of holding prescription drug insurance

Independent Variable Drug Insurance(no controls)

Drug Insurance(with controls)

Employer Drug Insurance

Income Less than $15k -0.290*(0.014)

-0.302*(0.014)

-0.577*(0.015)

Income $15k - $30k -0.356*(0.010)

-0.365*(0.010)

-0.491*(0.011)

Income $30k-$50k -0.228*(0.008)

-0.233*(0.008)

-0.251*(0.008)

Income $50k-$80k -0.079*(0.006)

-0.080*(0.006)

-0.085*(0.007)

β0 Estimate 0.866*(0.004)

0.829*(0.016)

0.748*(0.017)

n 28882 28882 28882

R2 0.106 0.133 0.169

Note: Parentheses represents the standard error * significant at 5% confidence level

Table 10 shows the effect of household income level on the probability of holding

prescription drug insurance. Column 2 shows there is an income gradient in the probability of

holding drug insurance, where households with higher income are more likely to hold drug

insurance. Income category $80k or higher was omitted from the regression model to serve as the

reference category for the coefficient estimates. The β0 estimate in column 2 represents the effect

of having income greater than $80 000, which is associated with having a 0.87 probability of

holding prescription drug insurance. Compared to household income greater than $80 000, the

probability of a household with an income between $15 000 and $30 000 holding drug insurance

decreases by 35.6 percentage points. However, the probability of households with income between

$50 000 and $80 000 holding drug insurance only decreases by 7.9 percentage points. Therefore, as

the household income increases, the probability of holding drug insurance also increases.

Column 3 shows estimated effects of household income on the probability of holding drug

insurance, after controlling for age, sex, health status, and highest attained education level. The

estimates show the same pattern as column 2, as the same income gradient is present. The β0 estimate in column 2 shows having income greater than $80 000 is associated with having a

probability of 0.83 for holding prescription drug insurance. Compared to household income greater

than $80 000, the probability of a household with an income between $15 000 and $30 000 holding

20

drug insurance decreases by 36.5 percentage points. However, the probability of households with

income between $50 000 and $80 000 holding drug insurance only decreases by 8.0 percentage

points. In this model, the magnitude of the estimates increase, which suggests household income

has a stronger effect on the probability of holding prescription drug insurance after controlling for

other health related variables.

The summary statistics showed majority of respondents with drug insurance held employer-

provided drug insurance. Table 10 shows the effect of household income on the probability of

specifically holding employer drug insurance. Column 4 shows the estimated effect of each income

level, after controlling for age, sex, health status, and highest attained education level. The same

income gradient persists for the estimates. A household with income greater than $80 000,

represented by the β0 estimate, holds employer-provided drug insurance with probability of 0.748.

The probability of a household with an income between $15 000 and $30 000 holding employer

drug insurance decreases by 57.7 percentage points and the probability of household with income

between $50 000 and $80 000 holding employer drug insurance only decreases by 8.5 percentage

points.

Table 11 – Estimates of income levels on probability of holding dental insurance

Independent Variable Dental Insurance(no controls)

Dental Insurance(with controls)

Employer Dental Insurance

Income Less than $15k -0.388*(0.014)

-0.390*(0.014)

-0.580*(0.015)

Income $15k - $30k -0.435*(0.010)

-0.432*(0.011)

-0.512*(0.011)

Income $30k-$50k -0.274*(0.008)

-0.272*(0.008)

-0.274*(0.008)

Income $50k-$80k -0.105*(0.007)

-0.103*(0.007)

-0.101*(0.007)

β0 Estimate 0.860*(0.004)

0.884*(0.016)

0.773*(0.017)

n 28882 28882 28882

R2 0.129 0.147 0.174

Note: Parentheses represents the standard error * significant at 5% confidence level

The same regression models were used to estimate the effect of household income on the

probability of holding dental insurance. Similar to the previous results, table 11 demonstrates an

income gradient in the probability of holding dental insurance. Column 3 demonstrates, after

21

controlling for age, sex, health status, and highest attained education level, the probability of a

household with an income greater than $80 000 holding dental insurance, represented by the β0 estimate, is 0.86. Compared to household income of greater than $80 000, the probability of a

household with an income between $15 000 and $30 000 holding dental insurance decreases by

43.2 percentage points. However, the probability of households with income between $50 000 and

$80 000 holding dental insurance only decreases by 10.3 percentage points. Therefore, as household

income increases, the probability of holding dental insurance increases.

Summary statistics also shows majority of respondents with dental insurance are provided

the insurance through an employer. Column 4 shows the effect of household income on the

probability of specifically holding employer dental insurance, after controlling for age, sex, health

status, and highest attained education level. The same income gradient from table 10 persists for the

estimates. A household with income greater than $80 000, represented by the β0 estimate, holds

employer dental insurance with probability 0.773. Compared to household income greater than $80

000, the probability of a household with an income less than $15 000 holding employer dental

insurance decreases by 58.0 percentage points. The probability of household with income between

$50 000 and $80 000 holding drug insurance only decreases by 10.1 percentage points. Therefore,

the probability of holding employer dental insurance decreases by less as income increases,

suggesting higher household income leads to a higher probability.

Table 12 – Estimates of income levels on probability of holding eye care insurance

Independent Variable Eye Care Insurance(no controls)

Eye Care Insurance(with controls)

Employer Eye Care Insurance

Income Less than $15k -0.357*(0.015)

-0.364*(0.015)

-0.539*(0.015)

Income $15k - $30k -0.413*(0.011)

-0.417*(0.011)

-0.485*(0.012)

Income $30k-$50k -0.277*(0.008)

-0.279*(0.008)

-0.271*(0.009)

Income $50k-$80k -0.106*(0.007)

-0.106*(0.007)

-0.101*(0.007)

β0 Estimate 0.778*(0.004)

0.760*(0.017)

0.688*(0.018)

n 28882 28882 28882

R2 0.1101 0.1321 0.1503

Note: Parentheses represents the standard error * significant at 5% confidence level

22

The same regression models were used to estimate the effect of household income on the

probability of holding eye care insurance. Similar to the results in table 10 and 11, table 12

demonstrates the same income gradient in the probability of holding eye care insurance. Column 3

shows, after controlling for age, sex, health status, and highest attained education level, the

probability of a household with an income greater than $80 000 holding eye care insurance,

represented by the β0 estimate, is 0.76. Compared to household income greater than $80 000, the

probability of a household with an income between $15 000 and I03 holding eye care insurance

decreases by 41.7 percentage points. However, the probability of households with incomes between

$50 000 and $80 000 holding eye care insurance only decreases by 10.6 percentage points.

Therefore, as household income increases, the probability of holding eye care insurance decreases

by less compared to a household with income greater than $80 000. This suggests higher household

income is associated with higher probability of holding eye care insurance.

Table 12 also shows the effect of household income on the probability of specifically

holding employer drug insurance, as majority of eye care insurance holders have employer provided

insurance. Column 4 shows the estimated effect of each income level, after controlling for age, sex,

health status, and highest attained education level. The same income gradient persists for the

estimates as the results of table 10 and 11. A household with income greater than $80 000,

represented by the β0 estimate, holds employer eye care insurance with probability 0.688. The

probability of a household with an income less than $15 000 holding employer eye care insurance

decreases by 53.9 percentage points and the probability of household with income between $50 000

and $80 000 holding employer eye care insurance only decreases by 10.1 percentage points. The

results in table 12 demonstrate that the probability of holding employer eye care insurance increases

as household income increases.

6. Discussion

The first section of the regression analysis addressed hypothesis 1 stating Canadian

households with higher income utilize more health services. Unfortunately, the evidence did not

clearly demonstrate that higher household income results in higher utilization of health services.

When health utilization was measured as the probability of having visited the dentist or the

probability of having received an eye exam in the last 12 months, evidence for the first hypothesis

was provided. A respondent was more likely to have visited a dentist or received an eye exam as

23

household income increased. However, when health care utilization was measured as visits made to

the GP in the last 12 months, income level had the opposite effect. As household income increased,

the number of visits made to the GP decreased. In this context, higher income did not lead to higher

utilization of health services.

It can be argued the strong effect of health status, reported in table 8, on visits made to the GP

is what contributes to this opposite effect. The measure of self-reported health status is a general

measure that does not specify the health conditions that are used to evaluate one’s health status.

Dental visits and eye exams are very specific forms of health care utilization. A respondent will

utilize these services if a specific oral health or eye care condition arises. Therefore, general health

status has a minimal effect on the probability of having visited the dentist or receiving an eye exam

because a specific oral health or eye care condition may not be included in a respondent’s report

(refer to appendix tables 5a and 7a). However, a visit to the GP can be motivated by any form of

health condition based on the broader services provided by a GP. Thus, self reported health status

has a larger effect on the utilization of GP visits because any health condition considered in the

respondent’s health self-evaluation might have induced a GP visit.

Table 13 - Estimate of income level on probability of having poor health status

Independent Variable Poor HS

Income Less than $15k 0.123*(0.004)

Income $15k - $30k 0.061*(0.004)

Income $30k-$50k 0.019*(0.003)

Income $50k-$80k 0.008*(0.003)

β0 Estimate -0.010*(0.005)

n 28882

R2 0.043

Note: Parentheses represents the standard error * significant at 5% confidence level

This distinction between the effects of health status on the different measures of health service

utilization is important to the research hypotheses because there is a prominent relationship between

health status and income. Table 13 shows there is an income gradient in the probability of having

‘poor’ self reported health. Specifically, as household income decreases, the probability of having

24

‘poor’ health status increases. After controlling for age, sex, and highest attained education level,

the probability of a household with an income greater than $80 000 reporting ‘poor’ health status,

represented by the β0 estimate, is - 0.01. Compared to household income greater than $80 000, the

probability of a household with an income less than $15 000 reporting ‘poor’ health status increases

by 12.3 percentage points. However, the probability of a household with income between $50 000

and $80 000 reporting ‘poor’ health status only increases by 0.08 percentage points.

Table 13 demonstrates having lower income increases the probability of having ‘poor’ health

status. This could be contributing to the evidence that low-income households make more GP visits,

because underlying this pattern is the fact that low-income households also have lower health status.

This income and health status relationship has less effect in dental and eye care utilization because

of the specificity of the services. Therefore, when health care utilization is measured as dentist visits

or eye exams, high-income households utilize more health services. When health care utilization is

measured as GP visits, high-income households utilize fewer health services, likely due to the effect

of self reported health status.

The first section of the regression analysis also demonstrates respondents with supplemental

insurance visited the GP more and were more likely to have visited a dentist and to have received an

eye exam. This finding from the first section is necessary for addressing hypothesis 2 because it

first needed to be found that supplemental insurance has a direct effect on health service utilization

in order to argue that income indirectly affects health service utilization via insurance. The second

section of the regression analysis considered the effects of household income on supplemental

insurance. These regression results provide evidence that the financial burden of supplemental

insurance also reduces health care utilization, as a lower household income decreases the

probability of holding supplemental insurance. High-income households were more likely to hold

some form of prescription drug, dental, and eye care insurance compared to low-income

households. These results contribute to the double disadvantage of low-income residents because

holding supplemental insurance increases health service utilization, but a higher income increases

the probability of holding supplemental insurance. Therefore, a respondent with lower household

income reduces health service utilization because the respondent would be less likely to have

supplemental insurance. Thus, the double disadvantage holds true. However, the first section of the

regression analysis demonstrated having a lower income directly results in more GP visits, which

contradicts the double disadvantage.

25

This contradiction appears when utilization is measured in GP visits, and not in dentist visits

or eye exams, because the health status and income relationship has a stronger effect on making GP

visits compared to the effect of holding prescription drug insurance. Holding prescription drug

insurance will induce more visits to the GP if the individual is demanding prescription drugs for

their health condition. However, GPs provide broad health services that extend further than writing

drug prescription. This means an individual may be induced to visit a GP for other health conditions

that are unrelated to prescription drugs, making the effect of holding prescription drug insurance

insignificant. Thus, prescription drug insurance has less effect on inducing GP visits. The effect of

being low-income and having ‘poor’ health status becomes a larger determinant for utilizing the

broad GP services, which ultimately contradicts the double disadvantage. However, it is possible

the double disadvantage holds true for dental visits and eye exams because supplemental insurance

has a greater effect on inducing utilization for dental and eye care services based on the specificity

of the services. In terms of dental visits and eye exams, having low household income decreases the

probability of holding supplemental insurance, which decreases utilization of these services and

provides evidence for the double disadvantage.

7. Conclusion

This paper demonstrates there is evidence to support the double disadvantage low-income

Canadian residents face when utilizing health services. The research shows having a low household

income decreases the probability of having made a dentist visit or the probability of having received

an eye exam. Low household income does not decrease the number of GP visits made. This

different result may occur based on the noticeable relationship between health status and income

level, where low income increases the probability of having ‘poor’ health status. The research also

clearly demonstrates that having any form of supplemental health insurance increases health service

utilization. Respondents who held government, employer, or private provided insurance for

prescription drugs, dental services, or eye care services were more likely to utilize the health

services. After exploring the separate effects of income level and supplemental insurance, it was

demonstrated that high household income also increases the probability of holding supplemental

insurance. This suggests low-income households indirectly utilize fewer health services because

these residents are less likely to have supplemental insurance, the owning of which, increases health

service utilization. In conclusion, it can be argued low-income Canadian residents are unable to pay

for health services directly and are unable to pay for supplemental health insurance, the owning of

26

which would reduce health service cost. With the caveat discussed with GP visits, the research does

provide evidence that Canadian residents face a financial barrier based on household income that

limits their use of health services and suggests they face a financial double disadvantage.

References

Allin, S. & Hurley, J. (2009) Inequity in publicly funded physician care: What is the role of private prescription drug insurance? Health Economics, 18: pp. 1218-1232

Asada, Y. & Kephart, G. (2007) Equity in health services use and intensity of use in Canada. BMC Health Services Research, 7: 41

Curtis, LJ. & MacMinn, WJ. (2008) Health care utilization in Canada: Twenty-five years of evidence. Canadian Public Policy, 34: pp. 65–87

Devlin, R., Sarma, S., & Zhang, Q (2011). The role of supplemental coverage in a universal health insurance system: Some Canadian evidence. Health Policy, (100) pp. 81-90

Kapur, V. & Basu, K. (2005) Drug coverage in Canada: Who is at risk? Health Policy, 71: pp. 181-193

Lundberg, L. et al. (1998) Effect of user charges on the use of prescription medicines in different socio-economic groups, Health Policy, 44: pp. 123-134

Nyman, JA. (1999) The value of health insurance: The access motive. Journal of Health Economics 18: pp. 141–52

Stabile, M. (2001). Private insurance subsidies and public health care markets: Evidence from Canada. The Canadian Journal of Economics, Vol. 34 pp. 921-942

Williamson, D. & Fast, J. (1998) Poverty and medical treatment: When public policy comprises accessibility. Canadian Journal of Public Health, 89: 2 pp. 120-124

27

Appendix Tables

28

Regression Tables – Income, Drug Insurance, and GP Visits

Table 1a – GP visits vs. Income (no control variables)

# of GP visits in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k 1.870 0.135 13.85 0.00 1.606 2.135Income $15k - $30k 1.314 0.102 12.90 0.00 1.114 1.514Income $30k-$50k 0.591 0.076 7.83 0.00 0.443 0.739Income $50k-$80k 0.333 0.064 5.21 0.00 0.208 0.458Income not stated 0.278 0.079 3.54 0.00 0.124 0.432_cons 2.677 0.039 68.52 0.00 2.600 2.753

Table 2a – GP visits vs. Drug Insurance (no control variables)

# of GP visits in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Has drug insurance 0.412 0.055 7.43 0.00 0.303 0.521_cons 2.729 0.047 57.50 0.00 2.636 2.822

29

Table 3a – GP visits vs. Income vs. Drug Insurance (with control variables)

# of GP visits in last 12m Coef. Std. Err. t P>t [95% Conf.

Interval]

Income Less than $15k 0.864 0.130 6.64 0.00 0.609 1.119Income $15k - $30k 0.590 0.100 5.92 0.00 0.395 0.786Income $30k-$50k 0.276 0.073 3.78 0.00 0.133 0.419Income $50k-$80k 0.095 0.060 1.58 0.11 -0.023 0.212Income not stated 0.239 0.081 2.96 0.00 0.081 0.398Has drug insurance 0.433 0.055 7.88 0.00 0.326 0.541Age: 20-24 0.241 0.132 1.82 0.07 -0.018 0.499Age: 25-29 0.345 0.135 2.56 0.01 0.080 0.609Age: 30-34 0.475 0.134 3.54 0.00 0.212 0.739Age: 35-39 0.214 0.131 1.63 0.10 -0.044 0.471Age: 40-44 0.074 0.129 0.57 0.57 -0.178 0.326Age: 45-49 0.268 0.131 2.04 0.04 0.011 0.524Age: 50-54 0.097 0.135 0.72 0.47 -0.167 0.362Age: 55-59 0.023 0.137 0.17 0.87 -0.246 0.291Age: 60-64 0.135 0.142 0.95 0.34 -0.143 0.413female 0.997 0.046 21.73 0.00 0.907 1.086No high school diploma -0.115 0.145 -0.79 0.43 -0.400 0.170Some Post Sec. Education

0.062 0.127 0.49 0.63 -0.187 0.312

Uni/College Degree 0.060 0.082 0.73 0.47 -0.101 0.221Education level not stated

-0.128 0.109 -1.18 0.24 -0.342 0.085

Very Good HS 0.458 0.059 7.70 0.00 0.341 0.574Good HS 1.156 0.065 17.68 0.00 1.027 1.284Fair HS 3.149 0.102 30.73 0.00 2.948 3.350Poor HS 6.989 0.158 44.26 0.00 6.680 7.299Health Status not stated -0.036 1.178 -0.03 0.98 -2.345 2.273Has chronic health condition

1.246 0.051 24.22 0.00 1.145 1.347

_cons 0.093 0.156 0.60 0.55 -0.212 0.398

30

Table 4a - GP visits vs. Income vs. Drug Insurance type (with control variables)

# of GP visits in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k 0.681 0.134 5.09 0.00 0.419 0.943Income $15k - $30k 0.489 0.101 4.85 0.00 0.291 0.687Income $30k-$50k 0.243 0.073 3.33 0.00 0.100 0.387Income $50k-$80k 0.089 0.060 1.48 0.14 -0.029 0.206Income not stated 0.207 0.081 2.54 0.01 0.047 0.366Has gov't drug insurance 0.918 0.103 8.91 0.00 0.716 1.120Has employer drug insurance

0.333 0.057 5.87 0.00 0.222 0.445

Has private drug insurance

0.258 0.119 2.17 0.03 0.025 0.491

Age: 20-24 0.253 0.132 1.92 0.06 -0.006 0.511Age: 25-29 0.356 0.135 2.64 0.01 0.092 0.620Age: 30-34 0.490 0.134 3.64 0.00 0.226 0.753Age: 35-39 0.232 0.131 1.77 0.08 -0.025 0.490Age: 40-44 0.090 0.129 0.70 0.49 -0.163 0.342Age: 45-49 0.279 0.131 2.13 0.03 0.023 0.536Age: 50-54 0.108 0.135 0.80 0.42 -0.157 0.373Age: 55-59 0.036 0.137 0.27 0.79 -0.232 0.305Age: 60-64 0.143 0.142 1.01 0.31 -0.135 0.421female 0.991 0.046 21.61 0.00 0.901 1.081No high school diploma -0.166 0.145 -1.14 0.25 -0.451 0.119Some Post Sec. Education 0.047 0.127 0.37 0.71 -0.203 0.296Uni/College Degree 0.058 0.082 0.70 0.48 -0.103 0.219Education level not stated

-0.145 0.109 -1.33 0.18 -0.359 0.068

Very Good HS 0.460 0.059 7.75 0.00 0.344 0.576Good HS 1.151 0.065 17.61 0.00 1.023 1.279Fair HS 3.114 0.103 30.36 0.00 2.913 3.315Poor HS 6.890 0.159 43.40 0.00 6.579 7.201Health Status not stated -0.049 1.178 -0.04 0.97 -2.357 2.259Has chronic health condition

1.242 0.051 24.14 0.00 1.141 1.343

31

Regression Tables – Income, Dental Insurance, and Dentist Visits

Table 5a - Dentist visits vs. Income vs. Dental Insurance (with control variables)

Dentist visit in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.194 0.015 -13.22 0.00 -0.223 -0.165Income $15k - $30k -0.208 0.011 -18.45 0.00 -0.230 -0.186Income $30k-$50k -0.146 0.008 -17.68 0.00 -0.162 -0.130Income $50k-$80k -0.087 0.007 -12.84 0.00 -0.100 -0.073Income not stated -0.095 0.009 -10.45 0.00 -0.113 -0.077Has dental insurance 0.228 0.006 37.92 0.00 0.216 0.240Age: 20-24 -0.053 0.015 -3.61 0.00 -0.082 -0.024Age: 25-29 -0.093 0.015 -6.16 0.00 -0.123 -0.064Age: 30-34 -0.075 0.015 -4.98 0.00 -0.105 -0.046Age: 35-39 -0.059 0.015 -3.98 0.00 -0.087 -0.030Age: 40-44 -0.015 0.014 -1.01 0.31 -0.043 0.014Age: 45-49 -0.002 0.015 -0.14 0.89 -0.031 0.027Age: 50-54 0.017 0.015 1.10 0.27 -0.013 0.046Age: 55-59 0.010 0.015 0.67 0.50 -0.020 0.040Age: 60-64 0.017 0.016 1.08 0.28 -0.014 0.048female 0.071 0.005 13.87 0.00 0.061 0.081No high school diploma -0.067 0.016 -4.10 0.00 -0.099 -0.035Some Post Sec. Education

0.021 0.014 1.47 0.14 -0.007 0.049

Uni/College Degree 0.059 0.009 6.41 0.00 0.041 0.077Educ. level not stated 0.038 0.012 3.09 0.00 0.014 0.062Very Good HS -0.021 0.007 -3.11 0.00 -0.034 -0.008Good HS -0.052 0.007 -7.22 0.00 -0.066 -0.038Fair HS -0.114 0.011 -10.05 0.00 -0.136 -0.092Poor HS -0.154 0.017 -8.81 0.00 -0.188 -0.120Health Status not stated 0.078 0.131 0.60 0.55 -0.179 0.334_cons 0.585 0.017 33.60 0.00 0.551 0.619

32

Table 6a - Dentist visits vs. Income vs. Dental Insurance type (with control variables)

Dentist visit in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.179 0.015 -11.93 0.00 -0.208 -0.149Income $15k - $30k -0.201 0.011 -17.66 0.00 -0.223 -0.178Income $30k-$50k -0.145 0.008 -17.52 0.00 -0.161 -0.128Income $50k-$80k -0.087 0.007 -12.86 0.00 -0.100 -0.074Income not stated -0.091 0.009 -9.94 0.00 -0.108 -0.073Has gov't dental insurance 0.161 0.013 12.76 0.00 0.136 0.185Has employer dental insurance

0.231 0.006 37.20 0.00 0.219 0.243

Has private dental insurance

0.239 0.014 16.77 0.00 0.211 0.266

Age: 20-24 -0.057 0.015 -3.83 0.00 -0.086 -0.028Age: 25-29 -0.098 0.015 -6.45 0.00 -0.127 -0.068Age: 30-34 -0.081 0.015 -5.36 0.00 -0.110 -0.051Age: 35-39 -0.066 0.015 -4.45 0.00 -0.094 -0.037Age: 40-44 -0.021 0.014 -1.45 0.15 -0.049 0.007Age: 45-49 -0.008 0.015 -0.54 0.59 -0.037 0.021Age: 50-54 0.011 0.015 0.71 0.48 -0.019 0.040Age: 55-59 0.004 0.015 0.28 0.78 -0.026 0.034Age: 60-64 0.010 0.016 0.65 0.51 -0.021 0.041female 0.072 0.005 14.01 0.00 0.062 0.082No high school diploma -0.061 0.016 -3.75 0.00 -0.093 -0.029Some Post Sec. Education 0.023 0.014 1.63 0.10 -0.005 0.051Uni/College Degree 0.060 0.009 6.47 0.00 0.042 0.078Education level not stated 0.038 0.012 3.11 0.00 0.014 0.062Very Good HS -0.021 0.007 -3.18 0.00 -0.034 -0.008Good HS -0.053 0.007 -7.27 0.00 -0.067 -0.038Fair HS -0.111 0.011 -9.83 0.00 -0.134 -0.089Poor HS -0.146 0.018 -8.34 0.00 -0.181 -0.112Health Status not stated 0.078 0.131 0.60 0.55 -0.178 0.335_cons 0.590 0.017 33.90 0.00 0.556 0.624

33

Regression Tables – Income, Eye Care Insurance, and Eye Exams

Table 7a - Eye Exams vs. Income vs. Eye Care Insurance (with control variables)

Eye exam in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.052 0.016 -3.33 0.00 -0.083 -0.022Income $15k - $30k -0.081 0.012 -6.70 0.00 -0.104 -0.057Income $30k-$50k -0.047 0.009 -5.26 0.00 -0.064 -0.029Income $50k-$80k -0.050 0.007 -6.97 0.00 -0.065 -0.036Income not stated -0.029 0.010 -2.98 0.00 -0.048 -0.010Has eye care insurance 0.077 0.006 12.67 0.00 0.065 0.089Age: 20-24 -0.068 0.016 -4.28 0.00 -0.099 -0.037Age: 25-29 -0.120 0.016 -7.38 0.00 -0.152 -0.088Age: 30-34 -0.142 0.016 -8.74 0.00 -0.173 -0.110Age: 35-39 -0.142 0.016 -9.01 0.00 -0.173 -0.111Age: 40-44 -0.063 0.016 -4.04 0.00 -0.093 -0.032Age: 45-49 0.001 0.016 0.04 0.97 -0.030 0.032Age: 50-54 0.026 0.016 1.61 0.11 -0.006 0.058Age: 55-59 0.043 0.016 2.62 0.01 0.011 0.075Age: 60-64 0.062 0.017 3.63 0.00 0.028 0.095female 0.080 0.006 14.48 0.00 0.069 0.091No high school diploma 0.000 0.018 -0.02 0.99 -0.035 0.034Some Post Sec. Education

0.044 0.015 2.87 0.00 0.014 0.074

Uni/College Degree 0.057 0.010 5.76 0.00 0.038 0.076Educ. level not stated -0.021 0.013 -1.62 0.11 -0.047 0.004Very Good HS 0.012 0.007 1.66 0.10 -0.002 0.026Good HS 0.020 0.008 2.58 0.01 0.005 0.035Fair HS 0.016 0.012 1.30 0.19 -0.008 0.040Poor HS 0.051 0.019 2.73 0.01 0.014 0.088Health Status not stated 0.106 0.141 0.75 0.45 -0.170 0.381_cons 0.282 0.018 15.32 0.00 0.246 0.318

34

Table 8a - Eye Exams vs. Income vs. Eye Care Insurance type (with control variables)

Eye exam in last 12m Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.057 0.016 -3.53 0.00 -0.088 -0.025Income $15k - $30k -0.083 0.012 -6.84 0.00 -0.107 -0.059Income $30k-$50k -0.047 0.009 -5.33 0.00 -0.064 -0.030Income $50k-$80k -0.051 0.007 -6.99 0.00 -0.065 -0.036Income not stated -0.030 0.010 -3.03 0.00 -0.049 -0.010Has gov't eye care insurance

0.092 0.014 6.69 0.00 0.065 0.120

Has employer eye care insurance

0.074 0.006 11.80 0.00 0.062 0.086

Has private eye care insurance

0.079 0.017 4.76 0.00 0.046 0.111

Age: 20-24 -0.069 0.016 -4.33 0.00 -0.100 -0.038Age: 25-29 -0.120 0.016 -7.42 0.00 -0.152 -0.089Age: 30-34 -0.142 0.016 -8.77 0.00 -0.174 -0.110Age: 35-39 -0.143 0.016 -9.03 0.00 -0.174 -0.112Age: 40-44 -0.063 0.016 -4.08 0.00 -0.094 -0.033Age: 45-49 0.000 0.016 0.00 1.00 -0.031 0.031Age: 50-54 0.025 0.016 1.56 0.12 -0.006 0.057Age: 55-59 0.042 0.016 2.58 0.01 0.010 0.074Age: 60-64 0.061 0.017 3.56 0.00 0.027 0.094female 0.080 0.006 14.47 0.00 0.069 0.091No high school diploma -0.001 0.018 -0.07 0.95 -0.036 0.033Some Post Sec. Education 0.044 0.015 2.86 0.00 0.014 0.074Uni/College Degree 0.057 0.010 5.77 0.00 0.038 0.077Education level not stated -0.021 0.013 -1.60 0.11 -0.047 0.005Very Good HS 0.012 0.007 1.69 0.09 -0.002 0.026Good HS 0.020 0.008 2.58 0.01 0.005 0.035Fair HS 0.015 0.012 1.24 0.22 -0.009 0.039Poor HS 0.049 0.019 2.59 0.01 0.012 0.086Health Status not stated 0.105 0.141 0.75 0.45 -0.170 0.381_cons 0.284 0.018 15.43 0.00 0.248 0.320

Regression Tables – Income vs. Drug Insurance

35

Table 9a – Drug Insurance vs. Income (no control variables)

Has drug insurance Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k

-0.290 0.014 -21.26 0.00 -0.316 -0.263

Income $15k - $30k -0.356 0.010 -34.65 0.00 -0.376 -0.336Income $30k-$50k -0.228 0.008 -29.95 0.00 -0.243 -0.213Income $50k-$80k -0.079 0.006 -12.23 0.00 -0.092 -0.066Income not stated -0.376 0.008 -47.58 0.00 -0.391 -0.360_cons 0.866 0.004 219.60 0.00 0.858 0.874

Table 10a – Drug Insurance vs. Income (with control variables)

Has drug insurance Coef. Std. Err. t P>t [95% Conf.

Interval]

Income Less than $15k -0.302 0.014 -21.80 0.00 -0.329 -0.275Income $15k - $30k -0.365 0.010 -34.88 0.00 -0.385 -0.344Income $30k-$50k -0.233 0.008 -30.22 0.00 -0.248 -0.218Income $50k-$80k -0.080 0.006 -12.49 0.00 -0.093 -0.068Income not stated -0.315 0.008 -37.25 0.00 -0.331 -0.298Age: 20-24 -0.061 0.014 -4.30 0.00 -0.088 -0.033Age: 25-29 -0.057 0.014 -3.98 0.00 -0.086 -0.029Age: 30-34 0.007 0.014 0.51 0.61 -0.021 0.036Age: 35-39 -0.018 0.014 -1.29 0.20 -0.046 0.009Age: 40-44 0.002 0.014 0.13 0.90 -0.025 0.029Age: 45-49 0.026 0.014 1.84 0.07 -0.002 0.053Age: 50-54 0.015 0.014 1.02 0.31 -0.014 0.043Age: 55-59 0.043 0.015 2.91 0.00 0.014 0.071Age: 60-64 0.024 0.015 1.58 0.11 -0.006 0.054female 0.020 0.005 4.16 0.00 0.011 0.030No high school diploma 0.011 0.016 0.71 0.48 -0.019 0.042Some Post Sec. Education

0.056 0.014 4.06 0.00 0.029 0.082

Uni/College Degree 0.013 0.009 1.43 0.15 -0.005 0.030Education level not stated

-0.168 0.012 -14.44 0.00 -0.191 -0.145

Very Good HS -0.007 0.006 -1.03 0.31 -0.019 0.006Good HS -0.006 0.007 -0.81 0.42 -0.019 0.008Fair HS 0.017 0.011 1.54 0.12 -0.005 0.038Poor HS 0.072 0.017 4.31 0.00 0.040 0.105Health Status not stated -0.010 0.125 -0.08 0.94 -0.255 0.235Has chronic health cond. 0.045 0.006 8.13 0.00 0.034 0.056

36

_cons 0.829 0.016 51.98 0.00 0.797 0.860

Table 11a – Employer Drug Insurance vs. Income (with control variables)

Has employer drug insurance

Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.577 0.015 -38.92 0.00 -0.606 -0.548Income $15k - $30k -0.491 0.011 -43.77 0.00 -0.513 -0.469Income $30k-$50k -0.251 0.008 -30.38 0.00 -0.267 -0.235Income $50k-$80k -0.085 0.007 -12.42 0.00 -0.099 -0.072Income not stated -0.357 0.009 -39.42 0.00 -0.375 -0.339Age: 20-24 -0.045 0.015 -2.98 0.00 -0.075 -0.015Age: 25-29 -0.010 0.015 -0.63 0.53 -0.040 0.021Age: 30-34 0.078 0.015 5.04 0.00 0.048 0.108Age: 35-39 0.052 0.015 3.47 0.00 0.023 0.082Age: 40-44 0.059 0.015 3.98 0.00 0.030 0.088Age: 45-49 0.069 0.015 4.57 0.00 0.039 0.098Age: 50-54 0.064 0.016 4.13 0.00 0.034 0.094Age: 55-59 0.059 0.016 3.73 0.00 0.028 0.089Age: 60-64 0.005 0.016 0.30 0.77 -0.027 0.037female 0.012 0.005 2.32 0.02 0.002 0.023No high school diploma -0.071 0.017 -4.26 0.00 -0.104 -0.038Some Post Sec. Education

-0.008 0.015 -0.54 0.59 -0.037 0.021

Uni/College Degree -0.017 0.009 -1.76 0.08 -0.035 0.002Education level not stated

-0.148 0.012 -11.90 0.00 -0.173 -0.124

Very Good HS 0.010 0.007 1.51 0.13 -0.003 0.024Good HS 0.011 0.008 1.48 0.14 -0.004 0.026Fair HS -0.020 0.012 -1.69 0.09 -0.043 0.003Poor HS -0.061 0.018 -3.39 0.00 -0.096 -0.026Health Status not stated -0.032 0.134 -0.24 0.81 -0.294 0.230Has chronic health cond. 0.017 0.006 2.92 0.00 0.006 0.029_cons 0.748 0.017 43.78 0.00 0.715 0.781

Regression Tables – Income vs. Dental Insurance

Table 12a – Dental Insurance vs. Income (no control variables)

37

Has dental insurance Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k

-0.388 0.014 -27.96 0.00 -0.416 -0.361

Income $15k - $30k -0.435 0.010 -41.51 0.00 -0.455 -0.414Income $30k-$50k -0.274 0.008 -35.30 0.00 -0.290 -0.259Income $50k-$80k -0.105 0.007 -15.93 0.00 -0.118 -0.092Income not stated -0.401 0.008 -49.82 0.00 -0.417 -0.386_cons 0.860 0.004 213.90 0.00 0.852 0.868

Table 13a – Dental Insurance vs. Income (with control variables)

Has dental insurance Coef. Std. Err.

t P>t [95% Conf. Interval]

Income Less than $15k -0.390 0.014 -27.50 0.00 -0.418 -0.362Income $15k - $30k -0.432 0.011 -40.33 0.00 -0.453 -0.411Income $30k-$50k -0.272 0.008 -34.46 0.00 -0.288 -0.257Income $50k-$80k -0.103 0.007 -15.67 0.00 -0.116 -0.090Income not stated -0.342 0.009 -39.61 0.00 -0.359 -0.325Age: 20-24 -0.065 0.014 -4.47 0.00 -0.093 -0.036Age: 25-29 -0.085 0.015 -5.77 0.00 -0.114 -0.056Age: 30-34 -0.007 0.015 -0.50 0.61 -0.036 0.021Age: 35-39 -0.045 0.014 -3.12 0.00 -0.073 -0.017Age: 40-44 -0.018 0.014 -1.29 0.20 -0.046 0.009Age: 45-49 0.001 0.014 0.10 0.92 -0.027 0.030Age: 50-54 -0.013 0.015 -0.85 0.40 -0.042 0.016Age: 55-59 -0.011 0.015 -0.76 0.45 -0.041 0.018Age: 60-64 -0.082 0.015 -5.31 0.00 -0.112 -0.052female 0.010 0.005 2.01 0.04 0.000 0.020No high school diploma -0.002 0.016 -0.12 0.90 -0.033 0.029Some Post Sec. Education 0.044 0.014 3.12 0.00 0.016 0.071Uni/College Degree 0.007 0.009 0.83 0.41 -0.010 0.025Educ. level not stated -0.163 0.012 -13.71 0.00 -0.187 -0.140Very Good HS 0.005 0.006 0.73 0.47 -0.008 0.017Good HS -0.002 0.007 -0.24 0.81 -0.016 0.012Fair HS 0.018 0.011 1.66 0.10 -0.003 0.040Poor HS 0.065 0.017 3.80 0.00 0.031 0.098Health Status not stated 0.004 0.128 0.03 0.98 -0.247 0.255

_cons 0.884 0.016 54.47 0.00 0.852 0.916Table 14a – Employer Dental Insurance vs. Income (with control variables)

Has employer dental insurance

Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.580 0.015 -39.02 0.00 -0.609 -0.551

38

Income $15k - $30k -0.512 0.011 -45.55 0.00 -0.534 -0.490Income $30k-$50k -0.274 0.008 -33.09 0.00 -0.290 -0.258Income $50k-$80k -0.101 0.007 -14.60 0.00 -0.114 -0.087Income not stated -0.366 0.009 -40.36 0.00 -0.383 -0.348Age: 20-24 -0.039 0.015 -2.58 0.01 -0.069 -0.009Age: 25-29 -0.033 0.015 -2.15 0.03 -0.064 -0.003Age: 30-34 0.063 0.015 4.10 0.00 0.033 0.094Age: 35-39 0.032 0.015 2.11 0.04 0.002 0.061Age: 40-44 0.047 0.015 3.15 0.00 0.018 0.076Age: 45-49 0.052 0.015 3.46 0.00 0.023 0.081Age: 50-54 0.047 0.015 3.03 0.00 0.017 0.077Age: 55-59 0.025 0.016 1.60 0.11 -0.006 0.056Age: 60-64 -0.054 0.016 -3.33 0.00 -0.086 -0.022female 0.009 0.005 1.72 0.09 -0.001 0.019No high school diploma -0.063 0.017 -3.75 0.00 -0.095 -0.030Some Post Sec. Education

-0.001 0.015 -0.05 0.96 -0.029 0.028

Uni/College Degree -0.012 0.009 -1.27 0.21 -0.031 0.007Education level not stated

-0.143 0.012 -11.44 0.00 -0.167 -0.118

Very Good HS 0.012 0.007 1.75 0.08 -0.001 0.025Good HS 0.012 0.007 1.64 0.10 -0.002 0.027Fair HS -0.013 0.012 -1.12 0.26 -0.036 0.010Poor HS -0.044 0.018 -2.49 0.01 -0.080 -0.009Health Status not stated -0.029 0.134 -0.22 0.83 -0.292 0.234_cons 0.773 0.017 45.45 0.00 0.740 0.806

Regression Tables – Income vs. Eye Care Insurance

Table 15a – Eye Care Insurance vs. Income (no control variables)

Has eye care insurance Coef. Std. Err. t P>t [95% Conf. Interval]

39

Income Less than $15k -0.357 0.015 -23.98 0.00 -0.386 -0.328Income $15k - $30k -0.413 0.011 -36.82 0.00 -0.435 -0.391Income $30k-$50k -0.277 0.008 -33.21 0.00 -0.293 -0.260Income $50k-$80k -0.106 0.007 -15.08 0.00 -0.120 -0.092Income not stated -0.402 0.009 -46.53 0.00 -0.419 -0.385_cons 0.778 0.004 180.52 0.00 0.769 0.786

Table 16a – Eye Care Insurance vs. Income (with control variables)

Has eye care insurance Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.364 0.015 -24.02 0.00 -0.394 -0.334Income $15k - $30k -0.417 0.011 -36.35 0.00 -0.439 -0.394Income $30k-$50k -0.279 0.008 -33.05 0.00 -0.296 -0.263Income $50k-$80k -0.106 0.007 -15.04 0.00 -0.119 -0.092Income not stated -0.340 0.009 -36.85 0.00 -0.359 -0.322Age: 20-24 -0.083 0.015 -5.36 0.00 -0.113 -0.053Age: 25-29 -0.068 0.016 -4.31 0.00 -0.099 -0.037Age: 30-34 0.017 0.016 1.07 0.29 -0.014 0.048Age: 35-39 -0.002 0.015 -0.16 0.87 -0.033 0.028Age: 40-44 0.029 0.015 1.93 0.05 0.000 0.059Age: 45-49 0.049 0.015 3.20 0.00 0.019 0.079Age: 50-54 0.027 0.016 1.70 0.09 -0.004 0.058Age: 55-59 0.060 0.016 3.74 0.00 0.028 0.091Age: 60-64 0.008 0.017 0.46 0.64 -0.025 0.040female 0.017 0.005 3.22 0.00 0.007 0.028No high school diploma 0.023 0.017 1.32 0.19 -0.011 0.056Some Post Sec. Education 0.036 0.015 2.39 0.02 0.006 0.065Uni/College Degree 0.005 0.010 0.47 0.64 -0.014 0.023Education level not stated

-0.164 0.013 -12.87 0.00 -0.189 -0.139

Very Good HS 0.011 0.007 1.64 0.10 -0.002 0.025Good HS 0.001 0.008 0.13 0.90 -0.014 0.016Fair HS 0.029 0.012 2.48 0.01 0.006 0.053Poor HS 0.079 0.018 4.35 0.00 0.044 0.115Health Status not stated -0.001 0.137 -0.01 0.99 -0.269 0.267_cons 0.760 0.017 43.79 0.00 0.726 0.794Table 17a – Employer Eye Care Insurance vs. Income (with control variables)

Has employer eye care insurance

Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k -0.539 0.015 -34.96 0.00 -0.569 -0.508Income $15k - $30k -0.485 0.012 -41.61 0.00 -0.507 -0.462Income $30k-$50k -0.271 0.009 -31.55 0.00 -0.287 -0.254

40

Income $50k-$80k -0.101 0.007 -14.11 0.00 -0.115 -0.087Income not stated -0.341 0.009 -36.28 0.00 -0.359 -0.322Age: 20-24 -0.066 0.016 -4.20 0.00 -0.097 -0.035Age: 25-29 -0.034 0.016 -2.12 0.03 -0.065 -0.003Age: 30-34 0.050 0.016 3.12 0.00 0.018 0.081Age: 35-39 0.033 0.016 2.09 0.04 0.002 0.063Age: 40-44 0.052 0.015 3.36 0.00 0.022 0.082Age: 45-49 0.061 0.016 3.92 0.00 0.030 0.091Age: 50-54 0.040 0.016 2.51 0.01 0.009 0.072Age: 55-59 0.047 0.016 2.90 0.00 0.015 0.079Age: 60-64 -0.020 0.017 -1.22 0.22 -0.053 0.012female 0.013 0.005 2.30 0.02 0.002 0.023No high school diploma -0.040 0.017 -2.30 0.02 -0.074 -0.006Some Post Sec. Education 0.011 0.015 0.73 0.47 -0.019 0.041Uni/College Degree -0.012 0.010 -1.27 0.20 -0.032 0.007Education level not stated

-0.152 0.013 -11.72 0.00 -0.177 -0.126

Very Good HS 0.022 0.007 3.10 0.00 0.008 0.036Good HS 0.017 0.008 2.20 0.03 0.002 0.032Fair HS -0.001 0.012 -0.07 0.94 -0.024 0.023Poor HS -0.040 0.019 -2.15 0.03 -0.076 -0.004Health Status not stated 0.004 0.139 0.03 0.98 -0.269 0.276_cons 0.688 0.018 39.02 0.00 0.653 0.722

Regression Table – Health Status vs. Income

Table 18a – Poor Health Status vs. Income

Poor HS Coef. Std. Err. t P>t [95% Conf. Interval]

Income Less than $15k 0.123 0.004 27.77 0.00 0.115 0.132Income $15k - $30k 0.061 0.004 16.79 0.00 0.054 0.069

41

Income $30k-$50k 0.019 0.003 6.61 0.00 0.014 0.025Income $50k-$80k 0.008 0.003 3.03 0.00 0.003 0.013Income not stated 0.022 0.003 6.44 0.00 0.015 0.028Age: 20-24 -0.006 0.006 -0.95 0.34 -0.017 0.006Age: 25-29 0.002 0.006 0.38 0.70 -0.009 0.013Age: 30-34 0.010 0.006 1.85 0.06 -0.001 0.021Age: 35-39 0.014 0.005 2.57 0.01 0.003 0.025Age: 40-44 0.021 0.005 3.79 0.00 0.010 0.031Age: 45-49 0.030 0.006 5.30 0.00 0.019 0.041Age: 50-54 0.039 0.006 6.97 0.00 0.028 0.050Age: 55-59 0.045 0.006 8.13 0.00 0.034 0.056Age: 60-64 0.039 0.006 6.94 0.00 0.028 0.050female -0.005 0.002 -2.65 0.01 -0.009 -0.001_cons -0.010 0.005 -1.95 0.05 -0.020 0.000

42