25. Epidemiology

Introduction to Epidemiology

25. Epidemiology

Introduction to Epidemiology: Videos & Practice Problems

Topic summary

Epidemiology studies the distribution, patterns, and determinants of diseases in populations, essential for understanding endemic, sporadic, epidemic, and pandemic diseases. Morbidity measures illness incidence and prevalence, with incidence proportion calculated as $\frac{new cases}{population at risk}$ and prevalence proportion as $\frac{total cases}{total population}$ . Mortality rate and case fatality rate differ by population scope, crucial for assessing disease impact and guiding public health interventions.

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Introduction to Epidemiology

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Introduction to Epidemiology Video Summary

Epidemiology is a fundamental discipline within public health, which focuses on protecting and improving the health of entire human populations. Public health serves as the overarching mission, while epidemiology provides essential methods and tools to achieve this goal. Epidemiology is defined as the study of the distribution, patterns, and determinants of health and disease in human populations. This field encompasses etiology, which specifically investigates the causes or origins of diseases, such as whether a disease is caused by microbes like bacteria or viruses, toxins, or genetic mutations.

Epidemiologists act as disease detectives, seeking to answer critical questions about where a disease originated, who is susceptible to it, and how its spread can be controlled or prevented. In the United States, the primary agency responsible for disease prevention is the Centers for Disease Control and Prevention (CDC), while internationally, the World Health Organization (WHO) plays a similar role in global health protection.

Understanding epidemiology is crucial for identifying health risks, implementing effective interventions, and ultimately improving population health outcomes. This field integrates knowledge of disease causation, transmission patterns, and prevention strategies, making it indispensable for public health professionals worldwide.

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Scope of Diseases

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Scope of Diseases Video Summary

Understanding the scope and geographic spread of diseases is essential for assessing the overall risk they pose to populations. Diseases can be classified based on their occurrence patterns and distribution, which helps in public health planning and response.

A sporadic disease occurs only occasionally and irregularly within a population, often appearing in isolated or scattered groups. The term "sporadic" originates from the Greek word meaning "scattered," reflecting its unpredictable and scattered nature. An example of a sporadic disease in the United States is rabies. On a graph plotting time against the number of cases, sporadic diseases show an irregular pattern with sudden appearances and disappearances of cases.

In contrast, an endemic disease is constantly present within a population, exhibiting a predictable and regular pattern over time. The word "endemic" combines the Greek roots "en" (in) and "demic" (people), meaning a disease that is always "in the people." The common cold in the United States is a typical example of an endemic disease. It is important to note that what is endemic in one region may not be endemic in another; for instance, malaria is endemic in parts of Africa but not in the United States.

An epidemic refers to a sudden increase in the number of disease cases above what is normally expected in a population. The term derives from "epi" (upon) and "demic" (people), indicating a disease that comes "upon the people." Epidemics often follow an outbreak, which is a smaller, localized surge in cases. For example, the measles epidemic in the Southwestern United States in 2025 was preceded by an outbreak. On a graph, an epidemic is characterized by a sharp peak in case numbers.

When an epidemic spreads across multiple countries or continents, affecting a large portion of the global population, it is termed a pandemic. The prefix "pan" means "all," so a pandemic is a disease "among all the people." The COVID-19 pandemic, which began in late 2019, exemplifies this global spread. Pandemics require coordinated international public health responses due to their widespread impact.

These classifications—sporadic, endemic, epidemic, and pandemic—are fundamental in epidemiology for understanding disease dynamics and guiding prevention and control strategies. Recognizing the patterns of disease occurrence helps in predicting outbreaks, allocating resources, and implementing timely interventions to protect public health.

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Introduction to Epidemiology Example 1

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Introduction to Epidemiology Example 1 Video Summary

Understanding the terms endemic, epidemic, outbreak, and pandemic is essential in epidemiology to describe the patterns of disease occurrence within populations. An endemic refers to a disease that is consistently present within a specific population or geographic area. The term derives from the roots "en-" meaning "in" and "demic" meaning "people," indicating a disease that is always among the people. This constant presence distinguishes endemic diseases from other types.

An epidemic describes a sudden and significant increase in the number of disease cases within a large population. The prefix "epi-" means "upon," combined with "demic" for "people," illustrating a disease that suddenly affects many individuals. This unusual surge in cases marks an epidemic, which is more intense than the steady presence seen in endemic diseases.

An outbreak is a smaller-scale, localized, and brief increase in disease cases, often serving as a precursor to an epidemic. It involves a rapid rise in cases confined to a specific area or community, highlighting the initial spread of infection before it potentially escalates.

A pandemic occurs when an epidemic spreads across multiple countries or continents, affecting a global population. This widespread transmission signifies the highest level of disease spread, emphasizing the importance of international public health responses.

These definitions help clarify how diseases vary in their occurrence and spread, providing a framework for public health strategies. Recognizing the differences between endemic, epidemic, outbreak, and pandemic is crucial for effective disease monitoring, prevention, and control.

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Morbidity: Incidence vs. Prevalence

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Morbidity: Incidence vs. Prevalence Video Summary

Morbidity refers to the condition of having an illness or disease, excluding death caused by the disease. It is commonly measured using two key epidemiological concepts: incidence and prevalence. Incidence represents the number of new cases of a disease that develop in a population during a specific time period. In contrast, prevalence accounts for the total number of disease cases—both new and preexisting—within a population at a particular point in time.

Both incidence and prevalence are initially raw counts, which means they simply tally the number of cases. However, raw counts can be misleading without considering the size of the population. For example, 500 new cases might seem significant, but in a population of 300 million, this number is relatively small. To address this, epidemiologists use standardized measures such as proportions, which relate the number of cases to the population size, providing a clearer understanding of disease frequency.

The incidence proportion is calculated by dividing the number of new disease cases during a specified time period by the population at risk at the beginning of that period, then multiplying by 100 to express it as a percentage. The population at risk excludes individuals who already have the disease or are not susceptible to it, ensuring the measure reflects the fraction of healthy, susceptible individuals who develop the disease. This can be expressed mathematically as:

\[\text{Incidence Proportion} = \frac{\text{Number of new cases during time period}}{\text{Population at risk at start of time period}} \times 100\%\]

On the other hand, the prevalence proportion captures the total number of disease cases (both new and existing) at a specific point in time divided by the total population size at that time, regardless of susceptibility, multiplied by 100 to convert it to a percentage. This provides a snapshot of how widespread the disease is within the population at that moment:

\[\text{Prevalence Proportion} = \frac{\text{Number of existing cases} + \text{Number of new cases at specific time}}{\text{Total population at that time}} \times 100\%\]

In summary, incidence focuses on the risk of developing a disease by measuring new cases among those susceptible, while prevalence reflects the overall burden of disease by including all current cases. Understanding these measures is essential for interpreting disease patterns and guiding public health interventions effectively.

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Introduction to Epidemiology Example 2

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Introduction to Epidemiology Example 2 Video Summary

In epidemiology, understanding the difference between incidence proportion and prevalence proportion is essential for measuring disease frequency within a population. Consider a college dormitory with 200 students, where 8 students are already sick with influenza at the start of the month. Over the course of the month, 42 new influenza cases are recorded, including 24 cases linked to a dorm party attended by 60 students, 3 of whom were initially sick. By the end of the month, 18 students remain actively sick, with 5 new cases reported on the last day.

The incidence proportion quantifies the risk of developing a new disease during a specified time period. It is calculated by dividing the number of new cases by the population at risk at the beginning of the period, then multiplying by 100 to express it as a percentage. Since 8 students were already sick, they are excluded from the at-risk population. Thus, the at-risk population is \$200 - 8 = 192$ students. Using the total 42 new cases during the month, the incidence proportion is:

\[\text{Incidence Proportion} = \frac{42}{192} \times 100\% \approx 21.9\%\]

This means that approximately 21.9% of the initially healthy students contracted influenza during the month.

On the other hand, the prevalence proportion measures the total burden of disease at a specific point in time, including both new and existing cases. It is calculated by dividing the total number of active cases by the total population, then multiplying by 100. At the end of the month, 18 students are actively sick out of the total 200 students, so the prevalence proportion is:

\[\text{Prevalence Proportion} = \frac{18}{200} \times 100\% = 9\%\]

This indicates that 9% of the dorm population was sick with influenza at the end of the month, regardless of when they contracted the illness.

These calculations highlight how incidence proportion focuses on new cases to assess risk over time, while prevalence proportion captures the overall disease burden at a specific moment. Both measures are crucial for public health planning and understanding the dynamics of infectious diseases like influenza in closed populations.

Problem

A nursing home houses 120 residents. At the beginning of the month of April, 10 residents already have salmonellosis, a gastrointestinal illness caused by the bacterium Salmonella, which typically spreads through contaminated food or water and causes diarrhea, fever, and abdominal cramps. During the entire month of April, 18 new cases of salmonellosis are confirmed among residents who were not already sick at the start of the month. By the last day of April, 12 residents in total are still actively sick with salmonellosis (this includes 2 carried over from the original 10 cases at the start of the month, and 10 from the 18 new cases identified during April).

What was the incidence proportion of salmonellosis in the nursing home during the entire month of April?

10.0%

15.0%

16.4%

20.0%

Problem

A nursing home houses 120 residents. At the beginning of the month of April, 10 residents already have salmonellosis, a gastrointestinal illness caused by the bacterium Salmonella, which typically spreads through contaminated food or water and causes diarrhea, fever, and abdominal cramps. During the entire month of April, 18 new cases of salmonellosis are confirmed among residents who were not already sick at the start of the month. By the last day of April, 12 residents in total are still actively sick with salmonellosis (this includes 2 carried over from the original 10 cases at the start of the month, and 10 from the 18 new cases identified during April).

Also, what was the prevalence proportion of salmonellosis in the nursing home at the end of April?

6.0%

8.3%

10.0%

12.5%

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Mortality & Case-Fatality Rates

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Mortality & Case-Fatality Rates Video Summary

Mortality and case fatality rates are important epidemiological measures used to understand the impact of diseases on populations. The mortality rate refers to the proportion of deaths among all individuals in the initial population over a specific period. It reflects the probability that a randomly selected person from the entire population will die from the disease. Mathematically, the mortality rate is calculated as the number of deaths divided by the total population at risk.

In contrast, the case fatality rate (CFR) focuses specifically on those diagnosed with the disease. It represents the proportion of deaths among only the diagnosed individuals over time, indicating the likelihood that a person who has contracted the disease will die from it. The CFR is calculated by dividing the number of deaths by the number of diagnosed cases.

For example, consider a population of five people where three are infected with a disease. If one of these infected individuals dies, the mortality rate is calculated as:

\[\text{Mortality Rate} = \frac{\text{Number of Deaths}}{\text{Total Population}} = \frac{1}{5} = 0.20 \text{ or } 20\%\]

Meanwhile, the case fatality rate is:

\[\text{Case Fatality Rate} = \frac{\text{Number of Deaths}}{\text{Number of Diagnosed Cases}} = \frac{1}{3} \approx 0.33 \text{ or } 33\%\]

It is important to note that while this example shows relatively high rates, most diseases typically have mortality and case fatality rates well below 1%. Understanding these rates helps in assessing disease severity and guiding public health responses effectively.

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Introduction to Epidemiology Example 3

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Introduction to Epidemiology Example 3 Video Summary

In epidemiology, understanding the impact of a disease within a population involves calculating key metrics such as the mortality rate and the case fatality rate. Consider a city with a population of 340,000 residents experiencing an influenza outbreak. During this event, 35,000 individuals are diagnosed with the virus, and unfortunately, 107 patients succumb to the disease.

The mortality rate measures the proportion of deaths in the entire population due to the disease. It is calculated by dividing the total number of deaths by the total population, then multiplying by 100 to express it as a percentage. Mathematically, this is represented as:

\[\text{Mortality Rate} = \left( \frac{\text{Number of Deaths}}{\text{Total Population}} \right) \times 100\%\]

Applying the values:

\[\text{Mortality Rate} = \left( \frac{107}{340,000} \right) \times 100\% \approx 0.0315\%\]

This indicates that approximately 0.0315% of the entire population died from influenza during the outbreak, a figure typically low for most diseases.

The case fatality rate (CFR) focuses specifically on the severity of the disease among those diagnosed. It is the proportion of deaths among confirmed cases, calculated by dividing the number of deaths by the number of diagnosed cases, then multiplying by 100 to convert to a percentage:

\[\text{Case Fatality Rate} = \left( \frac{\text{Number of Deaths}}{\text{Number of Diagnosed Cases}} \right) \times 100\%\]

Using the given data:

\[\text{Case Fatality Rate} = \left( \frac{107}{35,000} \right) \times 100\% \approx 0.3057\%\]

This means that about 0.3057% of those infected with influenza died, reflecting the disease's lethality among diagnosed patients. Both rates are crucial for public health assessments, guiding resource allocation and intervention strategies during outbreaks. Recognizing that these percentages are generally below one percent helps contextualize the relative risk and severity of infectious diseases within populations.

Problem

Disease A has a high case fatality rate but a relatively low prevalence. Disease B has a very low case fatality rate but a relatively high prevalence. Which of the following statements is true?

Disease A will infect more people than disease B because it has a higher mortality rate.

Compared to disease B, disease A has a higher % of deaths among those diagnosed.

If both diseases are present in the same population, they will likely both infect the same number of people.

Disease A spreads more easily than disease B.

Problem

Which of the following statements about mortality rates & case fatality rates is true?

For any given disease, CFR is always higher than or equal to mortality rate (if both are expressed as a percentage).

Mortality rate is often expressed as deaths per unit of population (e.g. per 100,000 people), while CFR is usually expressed as a percentage.

“Mortality” refers to death, while “morbidity” refers to illness/disease.

All of the above are true.

Problem

When calculating the case fatality rate (CFR) for this disease, you will have to divide the total number of fatalities by a value represented by which of the following in the square above?

All the blue squares.

All the green squares.

The yellow highlighted area.

All except the 2 red squares.

Problem

The mortality rate of this population is:

1%.

10%.

2%.

20%.

Problem

The case fatality rate for this disease is:

2%.

20%.

10%.

15%.

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Epidemiology is the study of the distribution, patterns, and determinants of health and disease in human populations. It is a core discipline within public health, which aims to protect and improve the health of entire populations. Epidemiologists act like disease detectives, investigating where diseases come from, who is susceptible, and how to stop their spread. This field includes studying the causes of diseases (etiology) such as microbes, toxins, or genetic mutations. Understanding epidemiology is crucial because it helps public health professionals design effective interventions, control outbreaks, and prevent epidemics or pandemics, ultimately improving population health.

Sporadic diseases occur occasionally and irregularly in scattered cases, like rabies in the USA. Endemic diseases are constantly present in a population with a predictable pattern, such as the common cold in the USA. Epidemics involve an unusually high number of cases in a population over a short time, like a measles outbreak. When an epidemic spreads globally, it becomes a pandemic, such as COVID-19. These terms help epidemiologists assess disease risk and guide public health responses based on the scale and spread of diseases.

Morbidity refers to the state of having a disease but does not include death. Incidence measures the number of new disease cases in a population during a specific time period, focusing on those at risk. Prevalence measures the total number of disease cases (new and existing) in a population at a specific time. Incidence proportion is calculated as $\frac{new cases}{population at risk}$ × 100%, while prevalence proportion is $\frac{total cases}{total population}$ × 100%. Incidence helps track disease spread, while prevalence shows how widespread a disease is at a given time.

Mortality rate is the proportion of deaths among all individuals in a population over time, indicating the chance that a random person will die from a disease. Case fatality rate (CFR) is the proportion of deaths among only those diagnosed with the disease, showing the risk of death for affected individuals. For example, if 1 out of 5 people in a population dies, the mortality rate is $\frac{1}{5}$ = 20%. If 1 out of 3 diagnosed people dies, the CFR is $\frac{1}{3}$ ≈ 33%. These rates help assess disease severity and guide healthcare strategies.

When calculating incidence, it is important to consider only the population at risk, meaning individuals who are healthy and susceptible to the disease. This excludes those who already have the disease or are immune. Including only the at-risk population ensures the incidence proportion accurately reflects the fraction of susceptible people who develop the disease during a specific time. If the entire population were used, including those not at risk, the incidence would be underestimated and misleading. This precision helps epidemiologists understand disease spread and target prevention efforts effectively.