For Epidemiology (copy)

Key Components of a Case and Case Definition:

Case, A case is any individual who meets the criteria for the disease or condition being investigated during the outbreak.

• Confirmed Case: A person who has been diagnosed with the disease based on laboratory tests, clinical diagnosis, or other scientific evidence.

• Probable Case: A person who shows symptoms or signs of the disease but may not yet have confirmed laboratory results.

• Suspected Case: A person who exhibits symptoms or has been exposed to the disease but has not yet been diagnosed.

Case Definition, A case definition is a set of standardized criteria used to classify and count cases during an outbreak. It typically includes:

• Clinical criteria: The specific signs and symptoms that define the disease (e.g., fever, rash, gastrointestinal distress).

• Laboratory criteria: If applicable, the laboratory test results needed to confirm the presence of the pathogen or disease (e.g., blood tests, stool samples).

• Epidemiological criteria: Exposure history, such as where the individual lives, where they have traveled, or whether they have had contact with other infected people.

Endemic

• Definition: A disease is considered endemic when it is consistently present in a particular population or geographic area over a long period of time. It occurs at a predictable, stable rate and is often part of the local environment or culture.

• Example: Malaria is endemic in many parts of Sub-Saharan Africa, where it is a common and ongoing health concern due to the presence of mosquito vectors and local conditions that support transmission.

Epidemic

• Definition: An epidemic refers to a sudden increase in the number of cases of a particular disease beyond what is normally expected in a population or geographic area. It typically involves a localized or regional surge in cases that surpasses the usual baseline.

• Example: An outbreak of measles in a community where it had previously been controlled or low in numbers, due to a lapse in vaccination rates or an introduction of the virus from outside the area.

Pandemic

• Definition: A pandemic is an epidemic that has spread across multiple countries or continents, affecting a large proportion of the global population. It typically involves a new or highly contagious infectious disease, and its spread may lead to widespread social, economic, and healthcare system impacts.

• Example: The COVID-19 outbreak, caused by the SARS-CoV-2 virus, was declared a pandemic in 2020 because it spread rapidly worldwide, causing infections in virtually every country.

Epidemiological Methods for Infectious Diseases:

• Epidemiology is crucial in managing and preventing infectious diseases because they are often caused by pathogens that can spread rapidly through populations. 

• Chronic diseases, such as heart disease, diabetes, and cancer, require a different approach due to their long-term, often non-contagious nature.

Circumstances when a disease requires Epidemiologic investigation

• Outbreaks of Infectious Diseases

• Unexplained Increase in Disease Incidence

• Epidemic or Pandemic Events

• Emergence of New or Novel Diseases

• Sporadic or Isolated Cases

• Detection of Unusual or Atypical Symptoms

• Bioterrorism or Intentional Release of Pathogens

Long-term epidemiological cohort studies

• Crucial for studying chronic diseases because they provide valuable insights into the development, risk factors, and prevention of these conditions over extended periods of time. These studies follow large groups of people (cohorts) over many years, collecting detailed data on various health indicators, behaviors, exposures, and genetic factors.

• Short comings of cohort studies

  • Biases and Confounding Variables

    • Selection bias: Cohort studies often involve participants who are healthier or more motivated to follow study protocols, which can skew the results.

    • Confounding variables: In long-term studies, many factors (e.g., genetics, socioeconomic status, access to healthcare) can influence the results, and it's challenging to control for all these potential confounders.

  • Loss to Follow-Up

    • One of the most significant challenges in cohort studies is loss to follow-up, where participants drop out of the study over time. This can lead to missing data, reducing the power of the study and introducing bias if those who drop out are systematically different from those who remain in the study

  • Time and Cost

    • Long-term cohort studies are expensive and require many years or even decades to produce meaningful results.

  • Changes in Population

    • Over long periods, societal factors, medical advances, and environmental changes can alter the characteristics of the population being studied

Death rates and life expectancy

• Death rates

  • Death rates measure the number of deaths in a specific population within a set period (usually one year)

• Life Expectancy

  • Life expectancy is the average number of years a person can expect to live

• Mortality rate

  • Mortality rate = (no. of deaths from cause) / (Total pop. at risk) * how many people are in the pop ur testing

Morbidity impacted by increased Longevity

• Refers to the incidence of disease or illness in a population, is impacted by increased longevity in the United States in several important ways. As life expectancy increases, people live longer, which can have both positive and negative effects on the overall morbidity rates.

  • While people are living longer, many are living with multiple health conditions that require ongoing management.

Incidence and Prevalence

Incidence

• Incidence refers to the number of new cases of a disease that develop in a specific population during a certain time period. It is used to measure the risk of contracting a disease within a specific timeframe

• Incidence helps assess the rate at which a new disease is occurring in a population, which can help in understanding the dynamics of an outbreak or the effectiveness of disease prevention measures.

  • Incidence Rate = (no. of new cases of disease in that time period) / (pop at risk during time) * People at risk

• Improved and more accessible screening = increased incidence, temporarily by detecting cases that would have otherwise gone undiagnosed.

Prevalence

• Prevalence refers to the total number of cases (both new and pre-existing) of a disease present in a population at a specific point in time or over a defined period.

• Prevalence is used to understand the overall disease burden in a population and to plan healthcare resources accordingly

  • Prevalence = (total no. new and existing) / (total pop at time) * how many people are in the pop ur testing

• Improved treatment = reduced prevalence, if it cures the disease more quickly or prevents death

What Are Notifiable Diseases?

• Notifiable diseases must be reported to public health authorities by healthcare providers, laboratories, or other officials. These diseases are tracked because they pose significant public health risks like outbreaks, epidemics, or potential bioterrorism threats.

Who notifies?

• Healthcare Providers (doctors, nurses, hospitals, clinics) – Report cases when they diagnose a notifiable disease.

• Laboratories – Report positive test results for certain diseases.

• Public Health Officials – Sometimes identify cases through surveillance programs

Who Gets Notified?

• Local Health Departments – First level of reporting, collects data within cities or counties.

• State Health Departments – Aggregates data and monitors disease trends.

• National Health Agencies (e.g., CDC in the U.S.) – Tracks diseases at the national level and provides guidance.

• Global Health Organizations (e.g., WHO) – For internationally significant diseases like Ebola or pandemic flu.

Intervention study vs Case-Control vs Cohort

Intervention study 

Case-Control

Cohort

• Lost-to-follow-up (also called attrition) occurs when participants in a study drop out or cannot be contacted before the study is completed. 

• You worry about this the most in cohort and intervention studies

 Incidence vs. Prevalence

• Difference between the two

• How to calculate both (what goes in the numerator, what goes in the denominator)

• “Population at Risk” for Incidence

Incidence

• Incidence refers to the number of new cases of a disease that develop in a specific population during a certain time period. It is used to measure the risk of contracting a disease within a specific timeframe

• Incidence helps assess the rate at which a new disease is occurring in a population, which can help in understanding the dynamics of an outbreak or the effectiveness of disease prevention measures.

  • Incidence Rate = (no. of new cases of disease in that time period) / (pop at risk during time) * People at risk

• Improved and more accessible screening = increased incidence, temporarily by detecting cases that would have otherwise gone undiagnosed.

Prevalence

• Prevalence refers to the total number of cases (both new and pre-existing) of a disease present in a population at a specific point in time or over a defined period.

• Prevalence is used to understand the overall disease burden in a population and to plan healthcare resources accordingly

  • Prevalence = (total no. new and existing) / (total pop at time) * how many people are in the pop ur testing

• Improved treatment = reduced prevalence, if it cures the disease more quickly or prevents death

5. Mortality Rate

• Measures total no. of deaths in population

• Mortality rate = (total deaths in given period) / (total pop at risk at same time) * per __ people

• Case Fatality Rate

• Measures the severity of a disease by calculating the percentage of people with the disease who die from it.

• CFR = (deaths of disease) / (total casesof disease) * 100

• Proportionate Mortality Rate

• Proportionate Mortality Rate (PMR) measures the percentage of total deaths that are due to a specific cause within a population

• PMR = (deaths from specific cause) / (total deaths) * 100

6. Cohort Studies

• Study Design and Strengths/Weaknesses

• Follows a group (cohort) over time to see who develops the disease based on exposure status.

• Strength

  • Measures incidence, establishes timing of exposure & outcome

• Weakness

  • Expensive, long follow-up time

• Relative Risk

• RR = (incidence in exposed) / (incidence in non exposed)

• calculation and Interpretation of both

7. Case-Control Studies

• Study Design and Strengths/Weaknesses

• Compares people with vs. without disease

• Strength

  • Good for rare diseases, faster & cheaper

• Weakness

  • Cannot measure incidence, recall bias

• Odds Ratio (How to calculate)

• OR = (odds of exposure in cases) / (odds of exposure in controls)

• Circumstances when OR approximates RR?

• Rare Disease Assumption:

  • If the outcome (disease) occurs in less than 10% of the population, OR closely approximates RR.

  • This is because when a disease is rare, the odds of disease and probability of disease are very similar.

• Cohort or Cross-Sectional Studies with Rare Outcomes:

  • In cohort studies with uncommon disease, OR can be used instead of RR.

  • Cross-sectional studies sometimes report OR when RR is difficult to calculate.

8. Cross-Sectional Studies

• Study Design and Strengths/Weaknesses

• Study Design

  • Data is collected at one point in time (a "snapshot").

  • It measures both exposure and outcome simultaneously.

  • Participants are selected without knowing their exposure or outcome status.

• Strengths

  • Quick & inexpensive (no need for long follow-up).

  • Measures prevalence of diseases or conditions.

  • Good for generating hypotheses for further studies.

  • Can study multiple exposures & outcomes at once.

• Weaknesses

  • Cannot establish causality (since exposure & outcome are measured at the same time).

  • Prone to survival bias (e.g., only people who survived a disease are studied).

  • Not good for rare diseases (because it measures prevalence, not incidence).

• Prevalence

• Since cross-sectional studies measure existing cases in a population, they are used to calculate prevalence.

• Prevalence = (No. of existing cases) / (Totoal pop) * 100

• Ecological Study (Type of Cross Sectional Study)

• Looks at population-level data rather than individual data 

  • Ex. Examining air pollution levels and asthma rates in different cities

• Strengths

  • Useful for generating hypotheses.

  • Uses existing data (e.g., government reports, census data).

  • Can study trends in large populations.

• Weaknesses

  • Cannot link exposure to outcome at the individual level.

  • Prone to ecological fallacy (see below).

• Ecological Fallacy

• An ecological fallacy occurs when researchers assume that relationships observed at the group level also apply at the individual level.

9. Terminology

• Observational vs. experimental study

• Observational studies cannot establish causation directly, only associations.

  • Researchers do not interfere; they observe relationships between exposure and outcome.

• Experimental studies (like randomized controlled trials, RCTs) can establish causation because of controlled conditions.

  • Researchers assign exposures to participants and control variables.

• Analytic vs. Descriptive Epi

• Descriptive epidemiology focuses on patterns & trends (describing health events).

  • Describes who, what, where, and when of a disease without analyzing cause-effect relationships.

• Analytic epidemiology focuses on causes & risk factors (testing hypotheses).

  • Investigates how and why diseases occur by examining relationships between risk factors and outcomes.

• Case Report

• A detailed report on a single patient