HCA 3306, Community Health 1
Course Learning Outcomes for Unit III
Upon completion of this unit, students should be able to:
2. Recognize effective principles of health programming for community health on a global scale.
2.1 Identify sources of data in community and public health biostatistics.
2.2 Identify the types of statistical analysis utilized in epidemiological research and community and
public health studies.
Course/Unit
Learning Outcomes
Learning Activity
2.1
Unit Lesson
Chapter 4
Chapter 5
Unit III Article Review
2.2
Unit Lesson
Chapter 4
Chapter 5
Unit III Article Review
Required Unit Resources
Chapter 4: Descriptive Biostatistics in Community and Public Health
Chapter 5: Inferential Biostatistics in Community and Public Health
Unit Lesson
Biostatistics
Statistics—the very word often brings about fear and anxiety. That feeling applies to many students and to
many health care professionals alike, but it does not have to be that way. In this lesson, we will explain what
you really need to know about descriptive and inferential statistics in order to be effective as a health care
leader. Remember, there are experts known as professional statisticians who are available to help us when
we get stuck. This lecturer/author has requested that kind of help often over the course of a career. Our
professional statisticians are ready and, in fact, anxious to help.
A statistic is a value that describes a sample taken from a particular population. That could be a population of
just about anything from spotted owls to sea turtles to redwood trees. But in our case, it is usually a human
population such as the entire population of a nation, the entire population of a state, or the entire population
within a certain age group or a certain ethnic group.
The reason that we study a sample from a population to develop a statistic is that typically the entire
population is just too large to work with. For example, as of 2020, the population of the United States is over
329 million people (The United States Census Bureau, n.d.). It would be awesome to survey, test, or screen
every U.S. citizen in our research, but the time and cost involved with doing so would simply be prohibitive.
So we work with samples most of the time in community health. In the unusual case that we do have a
measure for the entire population of anything studied, we call that statistic a parameter.
UNIT III STUDY GUIDE
Biostatistics in Community Health
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Descriptive Statistics
Let us consider the two primary types of biostatistics: descriptive and inferential. Descriptive statistics are all
about describing data. We can organize data, summarize it for users, simplify it, and then present it in
meaningful ways. Descriptive statistics are generally categorized into three primary types: frequency
distributions, graphical representations, and summary statistics.
Frequency distributions tell us the number of people who fall into a particular category. Let’s take a simple
example of a frequency distribution. Perhaps, we want to know what percentage of patients who use our
facility are Medicare beneficiaries. We take the number of Medicare patients, divide by the total number of
patients, and multiply by 100. Just that simply, we have the frequency distribution for Medicare patients who
utilize our facility. For many U.S. hospitals today, the Medicare percentage is in the range of 40–50%, making
hospitals very dependent on the Medicare program for survival!
With frequency distributions, sometimes, we want to categorize on the basis of more than one variable
simultaneously. For example, we might want to look at Medicare patients but also look at patients with a
particular diagnosis. Chronic obstructive pulmonary disease (COPD) is a hot topic for hospitals today because
of the 30-day readmission penalty, which has been instituted for patients with that diagnosis. So a reasonable
thing to cross-tabulate today would be the percentage of patients who are on Medicare and also have a
COPD diagnosis. View Image 1 below to see a frequency distribution table demonstrating data on blood
pressure.
Graphical representations of data are so helpful, and they have really benefitted us in community health.
Important factors and trends can sometimes seem buried in long columns and rows of numbers. But once we
display the same data graphically, important patterns become obvious. The most common graphical
representations in community health are the bar graph, histogram, polygon line graph, and frequency
distribution. The bar graph in Image 2 indicates the relationship between HCV infection and type 2 diabetes.
Image 1: Frequency Distribution Table: Blood Pressure Data
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Summary statistics describe data in just two numbers. The measure of central tendency, such as the typical
average score for a measure, and a measure of variability, such as the typical average variation for a
measure. A practical example includes how the typical average adult fasting blood sugar might be 100 mg/dl,
with typical average variation of 20 mg/dl above and below. Many of our normal ranges in health care are
created on this basis.
Among measures of central tendency, for quantitative data we can consider these items:
• mean, the arithmetic average of the data;
• mode, the most frequently occurring observation in a set of data; and
• median, the middle value in the data.
A key concept of community health is that the mean for any data set is greatly affected by just a few outliers,
meaning values that are greatly different from most of the data presented. Back to our blood sugar example, a
few patients with blood sugars of 600 mg/dl would shift the mean significantly, but the median would not be
impacted. The middle value is, by definition, more stable than the mean, and for that reason, it is often chosen
for reporting central tendency in health care. For qualitative data, the mode is always our choice, and the
mean should not be utilized.
Considering measures of variability, we can utilize these:
• variance, the average distance that each score is away from the mean (variance is noted as s2
);
• standard deviation, the square root of s2 ; and
• standard error of the mean, SD/square root of the number of measurements.
You will see the standard deviation utilized to describe data variability in most community health research and
applications. It gives us the best understanding of how wide the differences are among clinical data points.
Image 2: Prevalence of Type 2 Diabetes in HCV Infected and Healthy Subjects
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Inferential Statistics
Next, we consider inferential statistics, which focus on generalizing from samples to populations. Keys here
are hypothesis testing and understanding the relationships among variables. Through inferential statistics, we
set about making predictions of clinical importance to our communities. We take out the crystal ball here,
predicting the impact of specific factors and proposed changes on health outcomes. Health care policy is
often based upon these predictions, making inferential statistics very important today!
For example, we would love to study the impact on lung cancer rates by studying every client who stops
smoking, following them for 10 years or 20 years downstream to track lung cancer diagnosis rates, mortality
rates, and other considerations. The reality is that we cannot track them all, but with interoperable electronic
health records (EHR) now becoming reality across America, we can track a lot of them! The larger our
sample, the better our data, and the more meaningful our results. Inferential analysis lets us apply our
sample’s reduction of lung cancer to the larger population of all smokers who quit.
As an example, the NHLBI ARDS Network web page, About the NHLBI ARDS Network, is dedicated to
sharing results from sample studies of acute respiratory distress syndrome (ARDS) patients at teaching
hospitals across the world. Researchers all share their data and findings on this website, and the process
leads to better protocols for treating ARDS patients (NHLBI ARDS Network, n.d.).
This is an exciting time for researchers who employ inferential statistics. We are finally achieving truly
interoperable medical records in America, and that means we will be able to share data much more easily and
to mine data from around the nation, and even around the world, for the betterment of our clients.
Sampling Error
Whenever we utilize inferential statistics, we need to consider sampling error. This refers to variability among
populations, which occurs due to random chance rather than a true difference in the populations. The
absolute best way to reduce the impact of sampling error is to conduct multiple studies from multiple samples
and to make sure that each sample is large enough to minimize the impact of chance variability.
That is where probability comes in. Statistical probability refers to the odds that what we observed in our
sample did not occur because of random sampling error. It asks, what is the probability that my results are not
just due to chance. That is where the concept of level of confidence comes in. We ask ourselves a couple of
questions. What is the level of confidence that we have that our sample accurately reflects the total population
we serve? Are the inferences that we are making valid? We can never know with 100% confidence, but for
many health care purposes, we insist that we know with 95% confidence before we consider our results to be
valid.
Hypothesis Testing
In inferential statistics, we are generally interested in testing a hypothesis. The null hypothesis is that the two
groups studied will not differ. The alternative hypothesis is that the two groups studied will not perform the
same. There will be a difference (Sharma & Branscum, 2020). Back to our smoking cessation example–the
null hypothesis might be that smoking cessation causes no difference in lung cancer rates after 10 years of
cessation. The alternative hypothesis is that smoking cessation does reduce the incidence of lung cancer
after 10 years of cessation. Based upon our data, we will either find that the null hypothesis is true, meaning
that we do not reject the null hypothesis, or we will find that the null hypothesis is false, and we will reject it.
Very commonly in health care, we tolerate a 5% probability that our decision about accepting or rejecting the
null hypothesis is wrong, which is another way of saying that we have 95% confidence that our decision is
correct.
Conclusion
You will learn much about descriptive and inferential statistics in this unit. Whether you actually conduct
research yourself or not, your understanding will make you a better health care leader because of your ability
to interpret and use research findings in your work.
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References
NHLBI ARDS Network. (n.d.). About the NHLBI ARDS network. http://www.ardsnet.org/
Sharma, M., & Branscum, P. W. (2020). Introduction to community and public health (2nd ed.). Jossey-Bass.
https://online.vitalsource.com/#/books/9781119633716
United States Census Bureau. (n.d.). U.S. and world population clock. U.S. Department of Commerce.
https://www.census.gov/popclock/
