The Importance of Statistics in Healthcare (With Examples)

The field of statistics is concerned with collecting, analyzing, interpreting, and presenting data.

In the field of healthcare, statistics is important for the following reasons:

Reason 1: Statistics allows healthcare professionals to monitor the health of individuals using descriptive statistics.

Reason 2: Statistics allows healthcare professionals to quantify the relationship between variables using regression models.

Reason 3: Statistics allows healthcare professionals to compare the effectiveness of different medical procedures using hypothesis tests.

Reason 4: Statistics allows healthcare professionals to understand the effect of lifestyle choices on health using incidence rate ratio.

In the rest of this article, we elaborate on each of these reasons.

Reason 1: Monitor the Health of Individuals Using Descriptive Statistics

Descriptive statistics are used to describe data.

Healthcare professionals often calculate the following descriptive statistics for a given individual:

• Mean resting heart rate.
• Mean blood pressure.
• Fluctuation in weight during a certain time period.

Using these metrics, healthcare professionals can gain a better understanding of the overall health of individuals.

They can then use these metrics to inform individuals on ways they can improve their health or even prescribe specific medications based on the health of the individual.

Reason 2: Quantify Relationship Between Variables Using Regression Models

Another way that statistics is used in healthcare is in the form of regression models.

These are models that allow healthcare professionals to quantify the relationship between one or more predictor variables and a response variable.

For example, a healthcare professional may have access to data on total hours spent exercising per day, total time spent sitting per day, and overall  weight of individuals.

They might then build the following multiple linear regression model:

Weight = 124.33 – 15.33(hours spent exercising per day) + 1.04(hours spent sitting per day)

Here’s how to interpret the regression coefficients in this model:

• For each additional hour spent exercising per day, total weight decreases by an average of 15.33 pounds (assuming hours spent sitting is held constant).
• For each additional hour spent sitting per day, total weight increases by an average of 1.04 pounds (assuming hours spent exercising is held constant).

Using this model, a healthcare professional can quickly understand that more time spent exercising is associated with lower weight and more time spent sitting is associated with higher weight.

They can also quantify exactly how much exercise and sitting affect weight.

Reason 3: Compare Medical Procedures Using Hypothesis Tests

Another way that statistics is used in healthcare is in the form of hypothesis tests.

These are tests that healthcare professionals can use to determine if there is a statistical significance between different medical procedures or treatments.

For example, suppose a doctor believes that a new drug is able to reduce blood pressure in obese patients. To test this, he may measure the blood pressure of 40 patients before and after using the new drug for one month.

He then performs a paired samples t- test using the following hypotheses:

• H0: μafter = μbefore (the mean blood pressure is the same before and after using the drug)
• HA: μafter < μbefore (the mean blood pressure is less after using the drug)

If the p-value of the test is less than some significance level (e.g. α = .05), then he can reject the null hypothesis and conclude that the new drug leads to reduced blood pressure.

Note: This is just one example of a hypothesis test that is used in healthcare. Other common tests include a one sample t-test, two sample t-test, one-way ANOVA, and two-way ANOVA.

Reason 4: Understand Effects of Lifestyle Choices on Health Using Incidence Rate Ratio

An incidence rate ratio allows healthcare professionals to compare the incident rate between two different groups.

For example, suppose it’s known that people who smoke develop lung cancer at a rate of 7 per 100 person-years.

Conversely, suppose it’s known that people who do not smoke develop lung cancer at a rate of 1.5 per 100 person-years.

We would calculate the incidence rate ratio (often abbreviated IRR) as:

• IRR = Incidence rate among smokers / Incidence rate among non-smokers
• IRR = (7/100) / (1.5/100)
• IRR = 4.67

Here’s how a healthcare professional would interpret this value: The lung cancer rate among smokers is 4.67 times as high as the rate among non-smokers.

Using this simple calculation, healthcare professionals can gain a good understanding of how different lifestyle choices (like smoking) affect health in individuals.