Epidemiology - the backbone of public health

Epidemiology - the backbone of public health

Assessment

Interactive Video

Science

11th Grade - University

Hard

Created by

Quizizz Content

FREE Resource

The video introduces epidemiology, highlighting its complexity and importance in public health. It explains the two main focuses: distribution and causation. Distribution involves understanding how diseases and health states are spread across demographics, geography, and time. Causation examines the relationship between exposures and health outcomes, emphasizing the need to distinguish correlation from causation. The video also discusses the role of confounding variables and other factors that can affect epidemiological studies.

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7 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a common feeling among students when they first start studying epidemiology?

Boredom

Excitement

Clarity

Confusion

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why is it crucial to have a basic understanding of epidemiology in public health?

To design better hospitals

To measure distribution and causation

To improve patient care

To reduce healthcare costs

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is NOT a factor in the distribution of diseases?

Geographic

Temporal

Astrological

Demographic

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does the term 'exposures' refer to in epidemiology?

Only social determinants of health

Only environmental hazards

Factors that may cause or prevent disease

Only infectious diseases

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a common mistake when observing a correlation between two variables?

Assuming a causative relationship

Ignoring the data

Overanalyzing the data

Underestimating the sample size

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following is an example of a confounding variable?

Beach location

Shark species

Hot weather

Ice cream flavor

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What should be considered before assuming a causative relationship between two variables?

Factors like bias, error, and chance

The historical data

The opinions of experts

The popularity of the variables