1. NHAMCS-Outpatient Department Data 2008-2011
Does Health Education Improve Health? The Impact of Targeted Health Education
Abstract
This paper seeks to examine the impact of health education on patient health.
Many scholars have previously documented the effects of education on health, but
specifically education concerning health conditions and outcomes has not yet been
thoroughly understood. This analysis aims to add on to previous literature that has
explained the need for targeted health education. Using Probit estimation in examining
outpatient facility data, this analysis finds the importance in focusing on targeted health
education rather than a broad curriculum for patients. This model involves a variety of
specified health education variables in conjunction with their associated health condition.

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The Introduction
There is a distinction between education and health education. For instance
education is simply the knowledge or skill received from attending school. Health
education is any combination of learning designed to help improve an individual or
communities health. Does health education positively affect patient health?
A patient is a person under the care of a physician or medical professional. Health
education can be taught in school, at home, or charitable organization. For this analysis
we are looking at health education ordered or provided by medical professionals in an
outpatient facility.
Institutions like United Healthcare and Blue Cross Blue Shield would find the
answer to this question valuable. By knowing that health education provided in outpatient
facilities improves patient health these companies would implement programs that
incentivize health education. Healthcare companies want patients healthier in order to
reduce the number of claims they receive, which would decrease their costs.
Communities would also benefit from health education because a healthier community
means less costs on healthcare for families or individuals.
I hypothesize that targeted health education does positively affect patient health.
The independent variable of this hypothesis is targeted health education provided to the
patient. Many patients receive pamphlets and other literature at outpatient facilities that
inform them on general health tips. Targeted health education is when diet and exercise is
provided to a patient with obesity.
Literature Review
Almost all studies find that education positively impacts health (Cutler, et al.
2014). There are two areas that have been understudied however: health education and

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targeted health education. Studies that have observed education look at variables such as
years of schooling in comparison to how healthy an individual or society is. While years
of schooling are vitally important in the impact of well-being, health education
specifically focuses on health issues. As we are seeing the population live longer we are
also seeing an increasing number of persons who and will require acute and chronic
medical care. This is a major reason why it is important for patients to engage and
proactively be involved in their own healthcare; health education is a critical component
to this (Andiric, 2010).
Patients often times wait in a doctor office with nothing to do. There is a vital
need for health education in doctor offices (Strauss, 1945). By having visual charts and
posters around the waiting room on how to properly clean your teeth or what a healthy
diet includes the patient is gaining a small dose of health education. That dose of health
education will hopefully incentive the patient to act on the literature and therefore self
sufficiently improve their health.
Providing general health education in medical facilities to patients only helps a
select few. A piece of literature in the waiting room regarding a healthy diet may not
have a substantial effect on a patient who does eat right, but smokes and suffers from
lung cancer. Targeted health education matters, addressing multiple factors concerning an
individual’s personal health condition are needed to have the maximum affect on that
patient (Chaponniere, et al. 2013).
Data & Descriptive Statistics
The data used in this analysis is from the National Center of Health Statistics,
under the Center for Disease Control and Prevention. The data set is part of the National
Hospital Ambulatory Medical Care Survey Data; this analysis is utilizing outpatient

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department data from 2008 to 2011. The datasets range from 395 to 446 variables, most
of which are not applicable thus this analysis utilizes a select number of factors to
represent health conditions and education.
Measurements of Health
For overall health measurements four categories were created to analyze the
causes of health education on health. The first is healthy, this variable is designed to
capture patients who were not currently using tobacco and had no chronic conditions. The
second is semi-healthy, this category is for patients who were tobacco users and/or had
one chronic condition. Unhealthy is the third category and includes patients with two to
five chronic conditions. The last category is very unhealthy and is for patients who suffer
from six plus chronic conditions. Chronic conditions include: arthritis, asthma, cancer,
cerebrovascular disease, chronic renal failure, congestive heart failure, chronic
obstructive pulmonary disease, depression, diabetes, hyperlipidemia, hypertension,
ischemic heart disease, obesity, osteoporosis.
Measurements of Health Education
Health education was measured on if health education was ordered or provided to
the patient during the visit. When looking at targeted health education, which was the
hypothesis in this analysis, we account for specific health education concerning the
correlated condition. For example if asthma education was provided to a patient with
asthma.
Model & Results
The hypothesis of this analysis is that targeted health education positively affects
patient health. Looking at if a patient was provided or ordered health education will show
why targeted education is so vitally important. The first model estimated was the impact

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of health education on patient health. This model requires control variables because there
are many factors that contribute to an individual’s health condition. The data set provided
age and bmi (body mass index), these are two good control variables because depending
on your age and bmi you may be more or less healthy.
The model suffers from endogeneity, health education can causes a person to be
healthy, but a healthy person may use health education to be healthy. The independent
variable is correlated with the error term in the model. Instrumental variables are
implemented to extract the part of the error term that is correlated with our independent
variable.
Table 1.1 Probit estimation on healthy patients in
outpatient departments
I II III IV
mfx mfx
Patients provided health
education -1.208*** -1.208*** -.872*** -.872***
(0.434) (0.434) (0.202) (0.202)
Age -0.025*** -0.025*** -0.027*** -0.027***
(0.003) (0.003) (0.000) (0.000)
BMI -0.029*** -0.029*** -0.033*** -0.033***
(0.006) (0.006) (0.002) (0.002)
Constant 1.976*** 1.906***
(0.051) (0.065)
Observations 39870 39870 39870 39870
Robust standard errors in parentheses,Marginal
effects as mfx
***1% significance, **5% significance, *10% significance

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In columns I and II the instrumental variable used for patients who were provided
health education was insured. Insured patients include those under Medicare, Medicaid
and private insurance (employer provided is included.) The story is that patients who are
insured will more likely be able to pay for health education or an outpatient facility will
more likely provide health education if it will be paid for by the insurer. Though just
because a patient is insured doesn’t make them healthier. Using this made the regression
exactly identified. With insured as the instrumental variable patients who were provided
health education were less likely to be healthy. The marginal effects suggest those who
were provided health education were 120.8% less likely to be healthy.
In columns III and IV an over identified regression was used by adding hospitals
that are voluntary non-profit. The story behind this is that non-profit or church related
hospitals want to educate their patients in hope of reducing the amount of money they
spend per patient compared to for profit hospitals who make more money on patients. A
voluntary non-profit hospital is more likely going to provide health education even if it is
not paid for on the basis of their morals, but being treated in a voluntary non-profit wont
make a patient healthier compared to other hospitals. With insured patients and those
treated in voluntary non-profits, as the instrumental variables, patients who were
provided health education were less likely to be healthy. The marginal effects suggest
those who were provided health education were 87.2% less likely to be healthy compared
to those who were not provided education.

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The same instrumental variables in columns III and IV of table 1.1 were used in
this estimation. This model was used to show how health education impacts patients in
varying health conditions. Table 1.2 shows how health education provided to patient’s
impacts unhealthy individuals. The data suggests the same story as table 1.1, but only the
reverse. Patients who were provided health education were more likely to be unhealthy.
The marginal effects specifically suggest provided health education make patients
129.7% more likely to be unhealthy.
Both tables 1.1 and 1.2 are telling the same story, which is health education has a
negative or no affect on a person’s health. These results are abnormal or even troubling to
most who think health education must make people healthier. There could be a few
reasons that advocate these results are valid. One would be that individuals who are
educated on health conditions and factors know where to draw the line or the risks that
are associated with certain activities. Take medicine for example, a patient who is well-
Table 1.2 Probit estimation on unhealthy patients in outpatient departments
I II
mfx
Patients provided health education 1.297*** 1.297***
(0.184) (0.184)
Age -0.027*** -0.027***
(0.001) (0.001)
BMI -0.031*** -0.031***
(0.003) (0.003)
Constant -3.243***
(0.109)
Observations 53968 53968
Robust standard errors in parentheses,Marginal effects as mfx
***1% significance, **5% significance, *10% significance

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read on the risk and side effects of a pill may be more or less inclined to take the pill or
even how many pills. Another reason is that education on stress management probably
will not improve a patient’s condition whom is suffering from cancer or ischemic heart
disease.
The second estimation model is the underlying answer to the hypotheses. The first
model utilized data paring general health education and patient’s overall health condition.
This model utilizes specific data for both health education and health condition. Age and
bmi are still needed as control variables. This model also suffers from endogeneity as the
independent variable, targeted health education, is correlated with the error term. Only
one instrumental variable is needed and the model uses the voluntary non-profit variable.
The story still applies to this model in that voluntary non-profit hospitals not only
educate their patients, but also educate them on their specific health needs. Voluntary
non-profits are more likely to do this than other hospitals because they generally lose
money when conducting expensive procedures on patients, this is an incentive for non-
profit hospitals to provide health education in hopes of patients improving their health
condition self sufficiently. Many non-profits are church related and are known for
providing free health education and literature.

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The data in table 2.1 implies that patients who were provided asthma education
were less likely to have asthma. Asthma education can be information on the elimination
of allergens that cause asthma attacks, or activities that tend to lead to attacks. Marginal
effects suggest patients who were provided asthma education were 519.3% less likely to
have asthma. These results are directly in line with the hypothesis that targeted health
education improves health. Asthma education is directly associated with asthma, but
many of the variables in the dataset are not directly associated with any one chronic
condition. To see if the hypothesis is probable estimations using different health
education on a different chronic condition are needed.
Many medical professionals associate depression with stress. Individuals who are
under large amounts of stress often develop or undergo some form of depression. Stress
management is seen as a possible way to treat depression. This model, like table 2.1,
Table 2.1 Probit estimation on asthma patients in outpatient departments
I II
mfx
Patients provided asthma education -5.193*** -5.193***
(1.268) (1.268)
Age -0.006*** -0.006***
(0.000) (0.000)
BMI 0.004** 0.004**
(0.002) (0.002)
Constant -0.532*
(0.273)
Observations 54711 54711
Robust standard errors in parentheses,Marginal effects as mfx
***1% significance, **5% significance, *10% significance

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suffers from endogeneity and utilizes voluntary non-profit hospitals as the instrumental
variable for stress management education.
Table 2.2 Probit estimation on patients with depression in outpatient departments
I II
mfx
Patients provided stress
management -3.281*** -3.281***
(0.380) (0.380)
Age 0.003*** 0.003***
(0.000) (0.000)
BMI 0.012*** 0.012***
(0.001) (0.001)
Constant -1.315***
(0.103)
Observations 54711 54711
Robust standard errors in parentheses,Marginal effects as mfx
***1% significance, **5% significance, *10% significance
The estimation above falls in line with table 2.1 and the hypothesis. Column I
suggest patients who were provided education on stress management were less likely to
be diagnosed with depression. Stress management education includes information to help
patients reduce stress through exercise, yoga, and other vices; it also includes referrals to
health professionals specializing in coping with stress. The marginal effects suggest
patients who were provided education on stress management were 328.1% less likely to
be diagnosed with depression. That is a very large and significant statistic and only
validates the hypothesis that targeted health education positively affects patient health.
Congestive heart failure is when the heart isn’t able to pump blood as well as it
normally should. This chronic condition is associated with shortness of breath, fatigue,

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rapid heartbeat and more. There is no cure, but many treatments have been proven to
help. Eating a healthier diet is a top treatment recommended to many patients with this
condition. The estimation model in table 2.3 follows suit with the two before. It suffers
from endogeneity, where voluntary non-profit hospitals are the instrumental variable for
diet/nutrition education.
Table 2.3 Probit estimation on patients with congestive heart failure in outpatient departments
I II
mfx
Patients provided diet/nutrition
education -2.526*** -2.526***
(0.065) (0.065)
Age 0.004*** 0.004***
(0.001) (0.001)
BMI 0.016*** 0.016***
(0.001) (0.001)
Constant -0.932***
(0.237)
Observations 54711 54711
Robust standard errors in parentheses,Marginal effects as mfx
***1% significance, **5% significance, *10% significance
The table above continues to validate the hypothesis. Column I implies that
patients who were provided education on nutrition were less likely to suffer from
congestive heart failure. The marginal effects suggest those who were provided
nutritional education were 252.6% less likely to be diagnosed with congestive heart
failure.
The third model in this analysis implements time fixed effects, which is common
when using panel data. Variables like education and health caused the previous models to

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suffer from endogeneity. When a model suffers from endogeneity instrumental variables
are needed to isolate the independent variable from the error term, as this analysis has
done so far. For every endogenous variable an instrumental variable is needed, but since
time fixed effects generates dummy variables for every time period a regular Probit
estimation had to be used for this model.
This dataset did not provide an adequate number of instrumental variables to
correct endogeneity for dummy every year and education variable. The purpose of this
model is to observe if health education causes a change in the impact on health over a
number of years, even if a bias occurs. To do this interaction terms were generated
between health education provided and the dummy year variables. These variables allow
for estimation on if health education impacted a certain year differently than another.
Table 3.1 Probit estimation on healthy patients in outpatient departments
I II
mfx
Patients provided health education -0.318*** -0.099***
(0.030) (0.009)
Age -0.028*** -0.008***
(0.000) (0.000)
BMI -0.036*** -0.011***
(0.001) (0.000)
Year dummies YES YES
Health education provided in 2009 0.008 0.002
(0.044) (0.013)
Health education provided in 2010 0.133*** 0.042***
(0.042) (0.013)
Health education provided in 2011 0.098*** 0.030***
(0.043) (0.013)
Constant 1.716***
(0.035)
Observations 39870 39870
Robust standard errors in parentheses,Marginal effects as mfx
***1% significance, **5% significance, *10% significance

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Taking into account the possible bias due to endogeneity, the model suggests
some interesting findings. Patients who were provided health education, age, and bmi all
had the same impact on health as they did in table 1.1. This shows confidence that the
results in the above table may suffer little, if any, from endogeneity. Data on individuals
who were provided health education in 2009 had no statistical significance. However, the
data for 2010 and 2011 is statistically significant at the 1% level. Patients who were
provided health education in 2010 were more likely to be healthy then they were in 2008.
The marginal effects indicate patients who were provided health education in 2010 were
4.2% more likely to be healthy compared to those in 2008.
In 2011 health education provided to patients caused them to likely be healthier
than those in 2008. Marginal effects for 2011 suggest individuals who were provided
health education were 3.0% more likely to be healthy compared to those in 2008. Both
2010 and 2011 show improvements in health conditions caused by health education
compared to 2008, but the improvements did have a downward trend between those
years. In 2010 there was a significant policy enactment on the federal level, the
Affordable Care Act. This law aimed to insure more Americans and make basic
healthcare more accessible to more citizens. Though the law did not fully take effect until
2014, some implementation and expectations started in 2010. Increasing health education
was part of the new law. The story behind the data may suggest the Affordable Care
Act’s enactment in 2010 boosted the amount of health education that was provided to
patients in that year and slowed down the year after. A reason it may have slowed down

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was because of the news coverage and focus on healthcare declined a bit after the law
was signed.
Conclusion
The models confidently supported the hypothesis. The question was, does health
education positively affect patient health? The hypothesis stated that targeted health
education does positively affect patient health. The results from the model found that to
be valid. Indeed, when analyzing general health education the model showed that it in
fact negatively affected health, but that could have been for a variety of reasons as
explained in the model and results section. When testing the hypothesis directly the
model showed that targeted health education greatly improved health.
As discussed in the literature review, as time goes on society is finding not only
life expectancy increasing, but also the number of health conditions increasing.
Communities all over the globe should prepare to take on these new and increasing
challenges facing the healthcare industry. As the data showed merely providing general
health education to random patients is not worth the money, but if health insurers and
medical professionals focus on targeting health education to individual’s health
conditions society would aim to benefit. In closing, this area of focus needs to be studied
further. Governments and healthcare institutions appear to be wasting capital if the
money they spent on health education is not properly allocated.

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References
Cutler, D, Wei Huang, and Adriana Lleras-Muney. (2014). When does education matter?
The protective effect of education for cohorts graduating in bad times. Social
Science & Medicine (0), –.
Strauss, H. (1945). Health Education in Hospitals and Outpatient Departments. American
Journal of Public Health, 35(11), 1175-1180.
Chaponniere, PA, Susan M. Cherup, Lillie Lodge. (2013). Measuring the Impact of
Health Education Modules in Cameroon, West Africa. Journal of Transcultural
Nursing, 24(3), 254-262.
Andiric, L. (2010). Patient Education and Involvement in Care. UNF These and
Dissertations, 272, 3-5.