Principles of Epidemiology

🧭 Overview

🧠 One-sentence thesis

Epidemiology is an interdisciplinary science that studies health phenomena in populations to predict, prevent, and explain disease and health outcomes through various causal models.

📌 Key points (3–5)

• What epidemiology is: the science to understand and explain health phenomena occurrences in populations, not just disease but all health outcomes.
• Why it's interdisciplinary: integrates tools and information from statistics, medicine, biology, sociology, nutrition, and other fields to create models and analyses.
• Common confusion—epidemiology vs. biostatistics: epidemiology has its own terminology and methodology that make it different from biostatistics, though they are related.
• Main uses: predict and prevent health outcomes, track disease spread, study infectious and chronic diseases, plan health services, evaluate programs, and address social determinants.
• Causal models evolved: from the simple epidemiological triangle (host-agent-environment-time) to chain of infection to web of causation to social determinants of health.

🔬 Core definition and scope

🔬 What epidemiology studies

Epidemiology is the science to understand and explain the occurrences of health phenomena in a population.

• As a branch of public health, it studies not only disease but different health outcomes.
• It goes beyond just counting cases—it examines patterns, causes, and impacts on populations.
• Example: studying not just how many people have diabetes, but why certain communities have higher rates and what factors contribute.

🧩 Interdisciplinary nature

• Epidemiology integrates information from multiple fields: statistics, medicine, biology, sociology, nutrition, cultural studies, gender studies, anthropology.
• It produces models, formulas, and analyses to predict, prevent, and analyze health phenomena including quality of life and wellness.
• Trade-off: being interdisciplinary brings complications—results are never free of errors—but the field auto-corrects through trial and error, making it stronger.
• Don't confuse: interdisciplinary means drawing from many fields, not just being a subfield of one discipline like biostatistics.

🛠️ Common uses of epidemiology

🛠️ Prediction and prevention

• Epidemiology predicts and prevents adverse (or positive) health outcomes, especially with complex health issues.
• The prediction feature forecasts epidemics; the prevention part confirms epidemiology is essential to public health.
• Example: forecasting flu season peaks to prepare healthcare systems.

📊 Tracking and surveillance

• Collects information and tracks different health outcomes in a population.
• Uses surveillance systems to systematically collect, analyze, and disseminate health data for public health programs and services.
• Helps allocate resources for specific health conditions.

🦠 Disease study scope

• Originally focused on infectious diseases (where the field started).
• Expanded to include chronic diseases such as cardiovascular disease and cancer, also called lifestyle diseases because risk factors are linked to how people live.

🏥 Planning and evaluation

Use	How it works	Example
Planning health services	Provides data for clinical and population-level programs	Vaccination study in low-income communities informs immunization programs and policy
Program evaluation	Quantifies impact of interventions for funding	Most funding organizations require evaluation; epidemiology provides statistics and predictive data
Data collection design	Develops surveys and questionnaires	Population-based instruments for quantitative data collection

⚖️ Social justice applications

• Addresses public health problems linked to poverty, low socioeconomic status, access to resources, racism, and discrimination.
• Epidemiology plays a role in social justice because many adverse health outcomes are linked to social issues.
• In recent years, includes qualitative data (case studies, in-depth interviews, personal stories) to enhance and "put a face to the numbers," though epidemiology is traditionally quantitative.

🔺 Causal models in epidemiology

🔺 The epidemiological triangle (foundational model)

For disease to occur, there must be a host, an agent, and an environment.

• Later, time was added as a fourth dimension (minutes, hours, days for disease manifestation).
• Agent/pathogen: major causal factor—bacteria, viruses, fungi, other microbes, parasites.
• Host: a person or animal that harbors the disease; may show symptoms or be symptom-free.
• Environment: everything that surrounds the host or promotes the agent's existence; external factors contributing to disease development.
• Time: duration of disease, including incubation periods (no symptoms manifested) and symptom manifestation.

Limitation: This model works well for infectious diseases based on the medical model, but falls short for non-infectious diseases where many unpredictable factors interact.

🔗 Chain of infection model

• Similar to the epidemiological triangle but more developed.
• Components: etiologic agent/pathogen, source/reservoir, mode of transmission, and host.
• Improved version adds: means of entry, means of escape, host susceptibility, and pathogenic organism (expanding on pathogen).
• Don't confuse: means of entry/escape are essentially related to mode of transmission, not separate mechanisms.

🕸️ Web of causation

• Premise: more than one factor is involved in disease development—it is multifactorial.
• When several factors are put together, the model looks like a web.
• Example given: use of prostate-specific antigen (PSA) test to assess if a man needs cancer treatment—multiple factors influence this decision.
• More realistic than triangle/chain models but challenging to operationalize all factors.

🌍 Social determinants of health (current approach)

Social determinants are conditions in the places where people live, learn, work, and play that affect a wide range of health and quality-of-life risks and outcomes.

• Goes beyond agent-host-environment to integrate risk factors and multiple direct/indirect factors.
• Recognizes health and disease as dynamic concepts.
• The "older" models can still be seen in this approach, especially as an expansion of the environment concept.

📈 Epidemic patterns and terminology

📈 Epidemic

An increase in the frequency of a disease; an "excess" in the number of cases.

• Key concept: excess above a baseline occurrence.
• Applies mostly to infectious diseases, though recently used for non-infectious diseases like obesity.
• Pattern: number of cases ascends (increases), then typically declines.
• Don't confuse with seasonal disease: occurs during certain seasons/times of year, then declines (e.g., seasonal flu October–May, nearly disappears June–August).

🌐 Pandemic

• A disease that after being epidemic crosses borders and becomes a health problem across the world.
• Typical shape: comes in waves; entire process resembles a "W" letter.
• Tends to appear every one hundred years or closer (e.g., Spanish flu comparison to COVID-19).
• Epidemiologists use historical patterns to compare and predict current and future pandemics.

🗺️ Endemic

• A disease with constant frequency limited to a specific area or region.
• Example: malaria is endemic in some regions of Africa and Latin America.
• Related terms (less frequent): hyperendemic and holoendemic.

🩺 Natural history of disease and prevention levels

🩺 Natural course of disease

• Each disease has a natural course from beginning to end if no medical intervention is taken.
• Natural history is well-documented for some diseases but not well understood in others.
• Model stages: susceptible host → symptoms develop → recovery/death/disability → rehabilitation (if needed).

🛡️ Three levels of prevention

Prevention level	When it occurs	What it involves	Examples
Primary	Susceptible host stage	Interventions before symptoms develop; early detection	Pap smear, mammograms, digital rectal exams, colonoscopies, screening tests
Secondary	Symptoms developing	Treatment when person seeks healthcare; most expensive stage	Hospitalization for serious cases, supervised treatment for middle cases
Tertiary	After recovery/disability	Rehabilitation beyond medical care	Helping affected individuals improve quality of life and return to society productively

🏥 Clinical model connection

• Primary care links to primary prevention
• Secondary care links to secondary prevention
• Tertiary care links to tertiary prevention

Don't confuse: rehabilitation (tertiary prevention) is more than just medical care—it means helping the individual return to productive life, not just medical services during recovery (secondary prevention).

🧭 Overview

🧠 One-sentence thesis

Epidemiology evolved from ancient observations of disease patterns into a systematic science through key historical events—from Greek environmental theories and Roman sanitation to modern pandemic responses—with the U.S. developing its own public health trajectory parallel to but distinct from European traditions.

📌 Key points (3–5)

• Core historical thread: The search for disease causation, treatment, prevention, and control has driven epidemiology from ancient civilizations through the 21st century.
• Key figures and milestones: Hippocrates (air/water/place), John Graunt (vital statistics), John Snow (cholera mapping), Robert Koch (bacterial postulates), and U.S. pioneers like Wade Hampton Frost shaped the field.
• U.S. vs. European parallel: The U.S. public health history mirrors England's but lagged decades behind—dealing with immigrant sanitation crises in the 1800s while Europe had already advanced; the Shattuck Report (1850) marked the U.S. beginning.
• Common confusion: Don't assume all epidemiology history is European; the U.S. faced unique challenges (waves of immigrants, diverse ethnic populations, ethical failures like Tuskegee) requiring its own historical narrative.
• Pandemic patterns: Major infectious disease outbreaks (plague, cholera, smallpox, flu, COVID-19) repeatedly shaped public health infrastructure and revealed preparedness gaps.

🏛️ Ancient foundations and early sanitation

🏛️ Greek theories of disease causation

• Hippocrates (circa 400 B.C.) proposed systematic connections between nature and health:
  • Air, water, and place: "bad air," water quality, and geographic characteristics (altitude, soil, terrain) could cause disease.
  • This was the first systematic method to identify causation, even without microscopes or germ theory.
• Greeks also attributed disease to environmental factors influenced by gods and the theory of four fluids (blood, phlegm, yellow bile, black bile) based on four elements (fire, earth, water, air).

🚰 Roman public health engineering

• Romans developed aqueducts and sewage systems to prevent diseases related to poor sanitation.
• They brought clean water to cities, improved sanitation and personal hygiene—essential for health.
• Created the first rudimentary disposal systems for excreta (latrines) before modern toilets.
• Example: The Pont du Gard aqueduct and Ostia public latrines show early infrastructure investments in disease prevention.

☠️ Medieval plague (Black Death)

• The medieval period saw deadly diseases like the plague kill thousands.
• This era highlighted the devastating toll of infectious diseases before scientific understanding of transmission.

📊 Birth of vital statistics and disease mapping

📊 John Graunt and the Bills of Mortality

John Graunt (1603): London cloth merchant who compiled the first systematic register of births and deaths in England, called the "Bills of Mortality."

• What he did: Showed that human life conforms to predictable statistical patterns.
• Recorded deaths by age, sex, cause, location, and time—the foundation of vital statistics.
• He is considered the first demographer and initiator of systematic death reporting.
• Why it matters: Established that population health events can be measured, tracked, and analyzed quantitatively.

🗺️ John Snow and cholera mapping

Dr. John Snow (1813–1858): British physician and anesthesiologist to Queen Victoria; considered the father of public health.

• The 1849 London cholera epidemic: Snow used a "natural experiment" approach with advanced methods still valid today.
• Created a famous dot map (early choropleth/GIS map) showing where cholera cases lived and worked.
• Key finding: Identified contaminated drinking water from a specific pump as the epidemic source.
• Intervention: Removed the pump handle → people stopped drinking contaminated water → epidemic stopped.
• Don't confuse: Snow didn't know about bacteria (no microscope); he saw "small white, flocculent particles" but correctly linked water contamination to disease transmission.

🔬 Robert Koch and bacterial proof

Dr. Robert Koch (1843–1910): German physician, founder of bacteriology; discovered anthrax cycle and bacteria causing tuberculosis and cholera; Nobel Prize 1905.

• Koch's postulates (four criteria for proving a microorganism causes disease):
  1. Microorganism found in diseased but not healthy individuals.
  2. Microorganism cultured from diseased individual.
  3. Inoculation of healthy individual with cultured microorganism recapitulates disease.
  4. Microorganism re-isolated from inoculated individual matches original.
• Connection to Snow: If Snow had access to microscopes and Koch's bacteriological knowledge, he would have identified Vibrio cholerae bacterium instead of "impurities."
• Why it matters: Established reproducible experimental methods for identifying infectious agents and health determinants.

📈 William Farr and disease classification

William Farr: British epidemiologist appointed compiler of abstracts in England, 1839.

• Examined linkage between mortality rates and population density.
• Created the foundation for vital statistics as a field.
• Provided foundation for classification of diseases (ICD system).

🇺🇸 U.S. public health emergence and parallels

🇺🇸 Cholera in New Orleans (1848–49)

• Winter 1848–49: cholera outbreak in New Orleans killed ~3,000 residents in two months.
• Source: passenger ship from Liverpool and Le Havre (December 22, 1848) brought contagion.
• Who was affected: Low-income individuals in improper sanitary conditions, no access to clean water; immigrants from Ireland, Germany, England, Spain, Prussia, France, Switzerland.
• Parallels Snow's 1849 England epidemic: both affected populations with low health education and poor sanitation.
• Don't confuse timing: The New Orleans outbreak happened simultaneously with Snow's London investigation but independently.

🏫 Response: Tulane School of Public Health (1834)

• The 1832 cholera epidemic (and malaria, yellow fever, smallpox) prompted creation of one of the first public health schools in the U.S.
• Originally a medical college in New Orleans focused on tropical medicine training for Louisiana and the South.
• Shows how epidemics directly drove institutional public health infrastructure.

📜 The Shattuck Report (1850)

The Shattuck Report: Written by Dr. Lemuel Shattuck, published 1850 by Massachusetts Sanitary Commission.

• What it documented: Serious health problems among people living in unsanitary conditions in Boston.
• Key recommendations:
  • Create state health departments.
  • Establish local boards of health in each town.
  • Develop and implement public health interventions.
• Result: In 1866, New York City created the first such organization in the U.S.—considered the beginning of public health in the U.S.
• Parallel to England: The U.S. in 1850 dealt with issues England had already controlled; England/France were managing established healthcare systems while the U.S. was still addressing basic sanitation.

🔬 U.S. epidemiology pioneers

🔬 Wade Hampton Frost (1921)

Dr. Wade Hampton Frost: Appointed professor of epidemiology at Johns Hopkins University School of Hygiene & Public Health, Baltimore, 1921.

• Considered the first epidemiologist in the United States.
• Marked the beginning of teaching epidemiology in a U.S. public health school.
• Advocated for quantitative methods to support public health research (mainly infectious diseases at the time).

🔬 Alexander Langmuir (1949–1970)

Dr. Alexander Langmuir: First Chief Epidemiologist at CDC (1949–1970).

• Established CDC's Epidemiologic Intelligence Service (EIS).
• Convened the first Conference of State and Territorial Epidemiologists (1952), which became the organization representing U.S. epidemiologists.
• Defined disease surveillance at CDC.
• Made extraordinary contributions to CDC as an institution and public health generally.

🚗 Public health interventions: lessons and parallels

🚗 Seat belt law parallel to Snow's pump handle

• Snow's intervention (1849 England): Removed contaminated water pump handle → people stopped drinking bad water → cholera epidemic stopped.
• U.S. seat belt law (1968–1970s): Initially optional, people didn't buckle up; enforcement through traffic tickets changed behavior.
• Lesson learned: Removing barriers and enforcing policies helps people change behavior; laws enforce public health policies effectively.
• Example: Just as Snow physically removed access to contaminated water, seat belt laws removed the option to drive unsafely, making compliance a norm over time.

🚬 Tobacco and cardiovascular disease

🚬 England vs. U.S. tobacco regulation

• England started tobacco regulations and public awareness earlier than the U.S.
• U.S. milestone (1964): U.S. Surgeon General Luther L. Terry released the first report concluding:
  • Cigarette smoking causes lung cancer and laryngeal cancer in men.
  • Probable cause of lung cancer in women.
  • Most important cause of chronic bronchitis.
• Why it matters: Represented a big step in the search for health determinants; gave physicians and healthcare providers data about chronic tobacco use and cardiovascular disease effects on their own population.

❤️ Major U.S. cardiovascular cohort studies

The search for cardiovascular disease risk factors led to three major cohort studies:

Study	Location	Population focus	Start year	Key contributions
Framingham Heart Study	Massachusetts	White population	1948	First U.S. cohort on heart disease risk factors; provided common knowledge about effects of exercise, diet, smoking on cardiovascular disease; still ongoing
Bogalusa Heart Study	Louisiana	Black population (children)	1972	Showed adult heart disease begins in childhood; cardiovascular risk factors identifiable in early life; levels differ in children vs. adults; targeted African American children
San Antonio Heart Study	Texas	Latino/Hispanic population	Ongoing	Focuses on cardiovascular disease and type II diabetes in Latinos; mainly Mexican-American population

Critique of San Antonio study: Lacks representativity of different U.S. Latino ethnic groups (mainly Mexican-American); excludes Central American and Caribbean immigrants; results cannot be generalized to all U.S. Latinos. However, it represents pioneering research on two major morbidity/mortality problems in U.S. Latino population.

Common confusion: These studies reflect a systemic problem on race in U.S. research—why were some ethnic groups excluded? Why were some lied to and abused? (See Tuskegee below.)

⚠️ Research ethics failure: Tuskegee

⚠️ The Tuskegee Syphilis Study (1932–1972)

Official name: "Tuskegee Study of Untreated Syphilis in the Negro Male."

• Location: Macon County, Alabama.
• Duration: 40 years (participants told it would last 6 months).
• Participants: 600 African American men (399 with syphilis, 201 without).
• What happened:
  • Study of the natural history of syphilis conducted without informed consent.
  • Participants left without treatment for syphilis even though treatment was available.
  • Offered regular medical care for other problems, free meals, and burial insurance.
  • Told they were being treated for "bad blood" (common term for syphilis, anemia, fatigue).
• Impact: Exposed participants, their families, and generations of African Americans in the South to harm.
• Why it matters: Frequently cited as an example of what investigators must not do in human subjects research.
• Open question: What can be done to prevent similar situations in the future of U.S. research?

🦠 Major pandemics and disease eradication

🦠 Smallpox: from epidemic to eradication

🦠 Smallpox in Europe

Smallpox: Disease caused by the variola virus; believed to have existed for at least 3,000 years; caused millions of deaths especially during Medieval times; now eradicated.

• Dr. Edward Jenner (English doctor): Created the first smallpox vaccine.
• Observation: Milkmaids who had gotten cowpox were protected from smallpox (received immunity).
• This became the basis for vaccine development, which contributed to eradication.

🦠 Smallpox in the United States and Native Americans

• Smallpox was mainly a European problem until colonization of the Americas.
• Colonizers brought the disease to natives who had no immunity (never exposed).
• Killed thousands of natives in North America, the Caribbean, and South America.
• Biological warfare example: Colonizers gave Native Americans blankets intentionally contaminated with smallpox (at least once)—an early episode of terrorism using biological weapons.
• The strategy worked; entire populations of Native Americans were killed.

🦠 Influenza pandemics

🦠 The 1918 flu pandemic (Spanish flu)

• Duration: 1918–1919.
• Mortality: 50 to 100 million persons worldwide.
• Pattern: Three waves; the second wave was higher/stronger than the first; the third wave also higher than the initial wave.
• Called "The Mother of All Pandemics" (before COVID-19).
• Also a 1927 flu pandemic (less noticed globally but killed significant numbers, especially U.S. army soldiers).
• Don't confuse: Flu pandemics historically repeated every ~100 years, leading to predictions that the next pandemic would be flu (not coronavirus).

🦠 The 2009 H1N1 influenza pandemic

• Duration: 2009–2010.
• First identified: United States, April 2009 (CDC reported first two cases).
• Scale: Grew to 60 million cases by summer 2010.
• Age pattern: Preference for people 14–64 years; those 65+ least affected.
• Lesson not learned: Should have prepared the U.S. for future pandemics, but skepticism grew that another major pandemic was unlikely or that the U.S. had enough resources (vaccines, public health infrastructure) to control it—proven wrong by COVID-19.

🦠 COVID-19 pandemic (2019–present)

🦠 Origins and response failures

• Started: Wuhan, China; agent is coronavirus named COVID-19 (or SARS-CoV-2).
• U.S. response delay: News was everywhere, but the U.S. didn't pay attention until almost two months later (March 2020).
• Preparedness failures:
  • No vaccine available initially.
  • No public health infrastructure to respond properly.
  • Much unknown about the disease and transmission.
  • Measures (masks, stay-at-home mandates) lacked social acceptance in a society valuing personal freedom.
  • Federal government and public health authorities gave contradictory messages.
• Misinformation epidemic: Campaign of misinformation became one of the major problems, hard to eliminate even today.

🦠 Agent and statistics

COVID-19 agent: Coronavirus with many variants, including those causing respiratory illness called COVID-19 or SARS-CoV-2; may have originated in an animal (unclear which type) and mutated to cause human illness.

• Scale: Officially affected 191 countries and territories worldwide (data from 2020–2021).
• Key feature: Excess mortality; high hospitalizations never seen in decades, probably not since the last century.
• Data sources: Johns Hopkins Coronavirus Resource Center tracks ongoing estimates.
• Example: Global distribution maps show cumulative excess mortality rates for 2020–21 across all continents.

🔗 Connecting U.S. and European histories

🔗 Immigration and public health crises

• Second wave of immigrants in the U.S.: Major issue shaping U.S. public health history.
• Immigrants from distant countries (especially Irish) faced:
  • Unsanitary conditions in New York buildings.
  • Crowdedness, lack of proper disposal of excreta.
  • High cross-contamination of food and water.
  • Children often died of dysentery or other oral-fecal diseases.
• Epidemiological landscape difference: U.S. faced different challenges than Europe (England), which was already ahead in sanitary controls and housing conditions.
• Don't confuse: While methodologies were similar, the U.S. experienced a public health crisis while Europe had already moved past these issues.

🔗 Why write a separate U.S. history?

• Author's proposal: The U.S. should write its own epidemiology history, not because European events don't matter (they do—they are important milestones), but because:
  • There is a need for historical information that applies to current times and needs of the U.S. population served in public health.
  • Events in the Americas (including the U.S.) occurred later than in Europe.
  • The U.S. faced unique challenges (immigration waves, ethnic diversity, ethical failures).
• Parallel structure: Most epidemiology books use English historical events to draw parallels with U.S. public health; this works until we realize the U.S. dealt with issues that were already resolved in England.

🎯 Key takeaways and remarks

🎯 Cardiovascular disease research as a milestone

• The study of cardiovascular disease and associated factors represents one of the major milestones in epidemiological investigation in the U.S.
• A history of epidemiology without these studies would be incomplete—that's why they're included in this historical section.

🎯 Systemic race problems in research

• Major research efforts on cardiovascular disease in the U.S. reflect systemic problems on race:
  • Why were some ethnic groups excluded from studies?
  • Why were some lied to and abused (e.g., Tuskegee)?
• In the name of science and medical advances, abuses have been made.

🎯 Intertwined histories

• The history of public health and epidemiology are intertwined.
• Historical events confirm that epidemiology as a science has made great contributions to medicine and public health in:
  • The search for determinants.
  • The design of effective public health interventions.

🎯 Medicine without microscopes

• A common factor for the majority of historical events: they revolved around the search for causation and how to prevent and treat disease.
• Since most deaths in earlier times were caused by infectious agents (usually bacteria), progress in identifying causes had to wait until the discovery of the microscope (most agents cannot be seen with the naked eye).
• Example: Hippocrates proposed systematic methods ~400 B.C., but bacterial proof required Koch's work in the late 1800s with microscopes.

🎯 Pandemic preparedness lessons

• Prediction vs. preparation: The prediction of a new pandemic was always there, but medical and public health preparations were never in place.
• The U.S. public health system was proven unprepared for COVID-19 despite lessons from the 2009 H1N1 pandemic.
• Don't confuse: Having experienced past pandemics does not guarantee readiness for future ones without sustained infrastructure investment and public trust.

🧭 Overview

🧠 One-sentence thesis

Infectious diseases created epidemiology as a science, and understanding their transmission mechanisms, agent-host-environment interactions, and control measures remains foundational to public health practice.

📌 Key points (3–5)

• Why infectious diseases matter: They have been the main cause of mortality throughout history and drove the creation of epidemiology as a science.
• The epidemiological triangle: Disease occurs through interaction among host, agent (pathogen), and environment over time.
• Transmission pathways: Diseases spread directly (person-to-person) or indirectly (via vehicles, fomites, vectors, airborne droplets).
• Common confusion—carriers vs. symptomatic cases: Carriers harbor and spread pathogens but may be asymptomatic (healthy carrier), recovering (convalescent carrier), or intermittent spreaders; incubation period is when infection exists but symptoms have not yet appeared.
• Prevention and control: Vaccines, sanitation, quarantine, and isolation are the core methods; herd immunity requires high vaccination coverage (≥95% for many diseases) and works alongside other measures.

🦠 The epidemiological triangle and its components

🦠 Agent (pathogen)

The infectious disease agent (or pathogen): the microorganism or biological entity that causes disease.

• Without an agent, there is no disease (in the biological/medical model).
• Common agents: bacteria, viruses, fungi, other microbes, and parasites.
• Example: Viruses such as SARS-CoV-2, hepatitis B virus, or influenza.

🧑 Host

The host: a person or animal that harbors the disease.

• A host may show symptoms or be symptom-free (asymptomatic).
• The host's susceptibility and immune status influence whether disease develops.

🌍 Environment

The environment: everything external to the host or agent that contributes to disease development.

• Includes physical surroundings, climate, sanitation, and social conditions.
• The environment can promote the existence of the agent or facilitate transmission.

⏱️ Time

• Refers to the duration of disease, including the incubation period (no symptoms yet) and the full manifestation of symptoms.
• Time helps epidemiologists understand disease progression and plan responses.

🔗 Disease transmission mechanisms

🔗 Direct transmission

• Spread from person to person without an intermediary.
• Mechanisms:
  • Direct physical contact (touching, kissing, sexual intercourse, skin-to-skin).
  • Droplet spread when an infected person coughs or sneezes near a susceptible host.
• Example: Touching an infected person's contaminated hands or being coughed on at close range.

🔗 Indirect transmission

• Spread through an intermediate source: vehicles, fomites, or vectors.

🚗 Vehicles

Vehicle: the medium that contains the infectious agent.

• Examples: Used needles contaminated with blood (hepatitis transmission among IV drug users), contaminated water.

🧴 Fomites

Fomite(s): inanimate objects that may become contaminated with the infectious agent.

• Examples: Contaminated diapers in daycare centers (spread gastrointestinal and respiratory infections), personal protective equipment (PPE).
• Don't confuse: Fomites may contribute to spread, but they are not always effective transmitters.

🦟 Vectors

Vectors: insects and small animals that contribute to disease spread as part of their life cycle.

• Examples: Mosquitoes, fleas, mites, flies, ticks, small rodents.
• Two types of vector transmission:
  • Mechanical transmission: The pathogen uses the vector as a "ride" or physical transfer mechanism (e.g., a fly carrying bacteria on its legs).
  • Biologic transmission: The pathogen undergoes part of its life cycle within the vector (e.g., Plasmodium protozoan completes sexual development in the female Anopheles mosquito's intestine before being transmitted to humans, causing malaria).

💨 Airborne transmission

• Disease spreads via droplets or droplet nuclei suspended in the air.
• Droplet nuclei: particles 1–5 microns in diameter containing the infectious agent; can remain airborne for hours.
• Generation of droplet nuclei:
  • One cough: ~3,000 droplet nuclei.
  • Talking for 5 minutes: ~3,000 droplet nuclei.
  • Sneezing: tens of thousands of droplet nuclei, spreading up to 10 feet.
• Example: An infected person coughs in an enclosed space; tiny particles are expelled and inhaled by others.

💧 Waterborne transmission

• Pathogen carried via drinking water, swimming pools, streams, or lakes.
• Examples: Shigella, cholera.
• More frequent in summer when people visit public pools and recreational water sites.

🍽️ Vehicleborne and foodborne transmission

• Related to fomites: eating utensils, clothing, combs, etc.
• Examples: Scabies (Sarcoptes scabiei), head lice (Pediculosis capitis), pubic lice (Pediculosis pubis).
• Foodborne: contaminated food (food poisoning) with short incubation periods (hours).

🐾 Reservoirs, zoonoses, and related concepts

🐾 Reservoirs

Reservoir: humans, animals, plants, soil, or inanimate organic matter (feces, food) in which infectious organisms live and multiply.

• Example: Soil contaminated with Clostridium tetani spores (causes tetanus); important for gardeners and workers with open wounds.

🐾 Zoonosis and zoonoses

Zoonosis: when an animal transmits disease to a human.

Zoonoses: diseases and infections transmitted between vertebrate animals and humans.

• Examples of zoonosis: Malaria, lice, tinea, dengue fever.
• Examples of zoonoses: Mad cow disease, equine meningitis, cryptosporidium, hantavirus, toxoplasmosis, rabies.
• Public health significance: New epidemics are often linked to zoonotic diseases; veterinary medicine plays a key role.

🐾 Enzootic

Enzootic: a disease that affects only animals, in small numbers, in a persistent (endemic) manner.

• Not commonly used; refers to animal-only diseases.

🧬 Carriers and their types

🧬 What is a carrier?

Carrier: an individual who contains, spreads, or harbors an infectious organism.

• Six types: active, convalescent, healthy, incubatory, intermittent, and passive.

🧬 Active carrier

• Has been exposed to and harbors a pathogen for some time, even after recovery.
• Example: Worldwide, ~257 million chronic carriers of hepatitis B virus (as of 2020).

🧬 Convalescent carrier

• In the recovery phase of disease but still infectious.
• Example: A person recently diagnosed with COVID-19, recovering with no symptoms, but blood tests show they are still infectious.

🧬 Healthy carrier

• Exposed to and harbors a pathogen but has not become ill or shown symptoms.
• Example: Unvaccinated individuals with high serum titers of N. meningitidis but asymptomatic.

🧬 Incubatory carrier

• In the beginning stages of disease, showing symptoms, and able to transmit.
• Relates to the incubation period (discussed below).

🧬 Intermittent carrier

• Can spread disease at different places or intervals.
• Example: Chronic salmonellosis (S. typhi/enterica) in humans and animals (reptiles, exotic pets, cattle); the carrier sheds bacteria in feces intermittently.
• Public health measure: Regular checks for food industry workers (especially cooks) to prevent passive infection of others.

🧬 Passive carrier

• Exposed to and harbors a pathogen with no signs or symptoms.
• Same as a healthy carrier.
• Example: A person infected with Salmonella typhi who is asymptomatic.

🚪 Portals of entry and exit

🚪 COCONUT mnemonic

The main portals of entry/exit for infectious agents:

• Cutaneous (skin)
• Oral (mouth)
• Conjunctival (eyes)
• Other (e.g., transplacental)
• Nasal (nose)
• Urogenital
• Transfusion/injection (blood)

🚪 Effectiveness of entry routes

• Most efficient: Blood (intravenous, transplacental).
• Next: Inhalation, oral.
• Less efficient: Other routes.

⏳ Incubation period and related timing concepts

⏳ Incubation period

Incubation period: the time during which signs and symptoms are not manifested; the person has the infection but is asymptomatic.

• Helps clinicians and epidemiologists prepare for disease outbreaks.
• Varies widely by disease:
  • Short: Food poisoning (hours), common cold (12–24 hours).
  • Long: Some diseases take months or years to manifest.
• Example: A person exposed to the common cold may start symptoms 12–24 hours after exposure.

⏳ Inapparent infection

Inapparent infection: asymptomatic persons who have the disease but whose condition has not reached a clinically obvious level.

• Clinical significance: Asymptomatic individuals can transmit disease (e.g., COVID-19).
• These individuals are unknown carriers.

⏳ Generation time

Generation time: the time it takes for an infectious disease to elevate to the level of a case (full symptoms manifest, person seeks health care).

• Also: the interval between the presence of an infectious agent in a host and the maximal time of communicability.
• Practical application: Helps public health professionals plan prevention and control; some diseases have longer communicability periods, others shorter.

💉 Prevention and control methods

💉 Three key factors

1. Remove, eliminate, or contain the cause or source of infection.
2. Disrupt and block the chain of disease transmission.
3. Protect the susceptible population against infection and disease.

💉 Vaccines (immunizations)

Vaccines: preparations used to prepare the body to resist infection; most contain inactivated bacteria, viruses, or microbe toxins.

• Mechanism: Antigen-antibody reaction. The vaccine acts as an antigen (stimulates antibody formation); antibodies protect against the infectious agent.
• Major weapon: Considered the most important defense, especially for childhood infectious diseases.
• Recent development: mRNA vaccines (Pfizer–BioNTech, Moderna) for COVID-19 do not contain attenuated infectious agents; this changes the paradigm, but more time is needed to assess mRNA technology for future vaccines.
• Common vaccines: Chickenpox, diphtheria, flu, hepatitis A & B, HPV, measles, mumps, pertussis, polio, rotavirus, rubella, tetanus, etc.

🧼 Sanitation

• Hand washing: The oldest and number one activity to prevent infectious disease.
• Personal hygiene: Frequent bathing, grooming, teeth cleaning, changing clothes.
• Environmental hygiene: Ventilation in homes and buildings, cleaning surfaces that can act as fomites.
• Face masks and PPE: Especially for health care workers at higher risk due to exposure.

🌍 Environmental controls

• Provide clean and safe air, water, milk, and food.
• Manage solid waste (trash, garbage), liquid waste (sewage).
• Control vectors (insects and rodents).

🏠 Quarantine

Quarantine: a specific amount of time in which a person is isolated or separated from those who are not infected.

• Original meaning: Forty days.
• Modern use: Can be as short as one or two weeks, depending on the situation (e.g., COVID-19 pandemic).
• Four levels: Segregation, personal surveillance, modified quarantine, complete quarantine.

🏥 Isolation

• Used for a limited number of cases (humans and animals).
• Six levels:
  1. Private isolation room.
  2. Separate infection control gowns.
  3. Staff must wear masks.
  4. Staff must wear gloves when interacting with the patient.
  5. Hand washing required upon entering and leaving the room.
  6. Proper disposal of all contaminated or possibly contaminated articles (linen, dressings, syringes, instruments).
• Combination: Quarantine and isolation are often used together in clinics and hospitals, especially for highly contagious diseases (cholera, Ebola, Marburg virus, severe tuberculosis).

🛡️ Herd immunity

🛡️ What is herd immunity?

Herd immunity: the percentage of people or animals that can be protected by immunization.

• Principle: The higher the number of immunized individuals, the higher the protection for those who are not immunized.
• Historical belief: 70% vaccination coverage was sufficient.
• Current consensus: At least 95% vaccination coverage is needed (varies by disease).

🛡️ Limitations

• Herd immunity does not work for all infectious diseases.
• Considered a "weak" and "old" concept by some.
• Effective approach: Combine vaccination with other measures—good sanitation, soap and water, hand washing, mask covering, social distancing (as seen during COVID-19).

📋 Summary

• Infectious diseases epidemiology is the original focus of epidemiology; the medical model dominates the study and control of these diseases.
• The epidemiological triangle (agent, host, environment, time) is the foundational concept model.
• Transmission occurs directly or indirectly (via vehicles, fomites, vectors, airborne droplets).
• Carriers (active, convalescent, healthy, incubatory, intermittent, passive) can spread disease with or without symptoms.
• Incubation period, inapparent infection, and generation time are key timing concepts.
• Prevention and control rely on vaccines, sanitation, environmental controls, quarantine, and isolation.
• Herd immunity requires high vaccination coverage and works best alongside other public health measures.
• These concepts are essential for disease surveillance, outbreak investigation, and calculating attack rates (covered in later chapters).

🧭 Overview

🧠 One-sentence thesis

Person, place, and time are the three fundamental variables epidemiologists use to identify patterns, associations, and determinants of health phenomena and disease in populations.

📌 Key points (3–5)

• Core triad: Person (who is affected), place (where it occurred), and time (when it happened) form the foundation of descriptive epidemiology.
• Person encompasses multiple dimensions: race/ethnicity, gender, age, occupation, marital status, socioeconomic status, religion, and other individual characteristics.
• Place is spatial and multi-level: can refer to urban vs. rural, geographic regions, states, countries, or specific coordinates analyzed through mapping.
• Time captures patterns: includes cyclic fluctuations (oscillations over years), seasonal trends (fixed periods like winter flu), secular trends (long-term changes), and clustering (grouped cases in time and place).
• Common confusion: Seasonal vs. cyclic—seasonal events recur in fixed, known periods (e.g., winter flu); cyclic fluctuations appear in variable, unfixed periods (e.g., pertussis peaks every 3–5 years).

👤 Characteristics of Person

👤 What "person" means

Person: the "who" in epidemiology—most often a human, but can also be an animal (veterinary medicine) or an ecosystem (environmental health).

• Person variables overlap with "individual lifestyle factors" in social determinants of health frameworks.
• Multiple subcategories exist: race/ethnicity, gender, age, occupation, marital status, socioeconomic status, religion.
• Key principle: Health is determined by a combination of factors, not a single variable; relying on one indicator (e.g., socioeconomic status alone) becomes confusing or useless.

🧬 Gender vs. sex

• Common confusion: Gender ≠ sex. Sex is biological (male/female with variations); gender is a social construct with a spectrum of identities and personal pronouns.
• Epidemiology conventionally still uses "sex" categories for data reporting (following U.S. Census conventions).
• Gender as a health determinant: women experience unique health issues (pregnancy, childbearing) and different patterns of chronic disease compared to men.

🎂 Age

• Age defines health stages: childhood diseases (polio, measles, chickenpox) vs. elderly diseases (dementia, degenerative disorders).
• Age is complex for data collection because it spans multiple subcategories (children, youth, adults, seniors).
• Example: The excerpt shows childhood mortality from infectious diseases (lower-income countries) and elderly diseases in Europe (chronic/degenerative conditions).

💼 Occupation

• Occupation is not just "job"—includes homemakers, unemployed, underemployed.
• Why it matters: Certain occupations have higher exposure to toxic chemicals and environmental contaminants.
• COVID-19 example: Unemployment rose sharply in 2020, affecting mental health, access to healthcare (loss of insurance), ability to pay rent/buy food, and access to reproductive health services.

💍 Marital status

• Marital status is a legal/social construct; U.S. Census uses four categories: never married, married, widowed, divorced.
• "Married" includes subcategories: married with spouse present, separated, other married (spouse absent for employment/military/distance).
• "Single" can mean never-married, widowed, or divorced; in family/household context, it means only one parent present.

🌍 Race and ethnicity

• Definitions: Race refers to physical differences considered socially significant; ethnicity refers to shared culture (language, ancestry, practices, beliefs). Both are social constructs.
• U.S. Census uses five core categories: American Indian/Alaska Native, Asian, Black/African American, Native Hawaiian/Pacific Islander, White.
• A sixth category, "Some Other Race," was added in 2010 for multiracial, mixed, interracial, or Hispanic/Latino responses.
• The 2020 Census introduced a Diversity Index (DI) to reflect increasing U.S. diversity.

🙏 Religion

• Religion is considered a protective factor (reduces risk of negative health outcomes, e.g., drug use).
• Important distinction: It is religiosity (lifestyle associated with religious practice) rather than religion itself that matters.
• The relationship involves reversed causation and confounding factors; limited clinical trials exist to assess direct causal effects.
• Spirituality (not just religion) is linked to health-related behaviors and psychological well-being.

💰 Socioeconomic status (SES)

• SES relates to social class and income level; higher income → better societal position and health resources.
• Why it matters: SES predicts access to healthcare, healthy foods, and resources to stay healthy.
• Operationalized into three levels: high, middle, low.
• The U.S. Census uses the Gini ratio (index of income concentration, 0 = perfect equality, 1 = perfect inequality) to measure income distribution.
• Example: 2020 data show income disparities by gender in the U.S.

📍 Characteristics of Place

📍 What "place" means

• Place is the "where"—can refer to a location (area, city, state, country) or geospatial coordinates (latitude/longitude).
• Geospatial analysis and GIS (geographic information system) software are widely used in modern epidemiology.
• Dot maps: Represent data as pixels/dots; originated with Dr. John Snow's 1854 cholera map in London (showed infection sources without scale).

🏙️ Urban vs. rural

• Definitions: U.S. Census classifies communities using Metropolitan Statistical Areas (MSAs) and census tracts.
• NCHS uses six levels: large metro central, large metro fringe, medium metro, small metro, nonmetropolitan micropolitan, nonmetropolitan noncore.
• Health differences: Rural populations engage in more negative health behaviors; higher mortality linked to poverty; differences in access/quality of healthcare, infrastructure, and economic conditions.

🗺️ Variation within a country

• Disease rates vary by U.S. region (Pacific, Central, Mountain, Atlantic) and by state.
• Examples:
  • Cancer rates vary across states.
  • Multiple sclerosis rates differ between north and south U.S.
  • Lyme disease is concentrated in Midwest and northern East Coast states.

🌐 Variation across countries

• International differences are significant; cardiovascular disease is found worldwide, but mortality varies.
• COVID-19 example: Differences across countries reflect healthcare system quality, poverty levels, and public health infrastructure.
• The Commonwealth Fund report (2021) ranked 11 high-income countries; the U.S. ranked last overall (11th) except for care process (2nd). The U.S. scored worst on access, administrative efficiency, equity, and health outcomes.

🔍 Other factors explaining variation

• Ethnic groups tend to live in specific areas (e.g., Mormons' frugal lifestyle vs. Las Vegas residents).
• Socioeconomic conditions and poverty levels (e.g., homelessness in San Francisco and New York City; mental health problems in impoverished populations).

⏰ Characteristics of Time

⏰ What "time" means

• Time is the "when"—completes the picture of a health event alongside person and place.
• Example: "COVID-19 pandemic time" immediately evokes 2020–2021.
• Graphically, time is the X-axis, disease is the Y-axis.

🔄 Cyclic fluctuations (cyclic variations)

Cyclic fluctuations: oscillations in disease frequency over long periods (years) about a secular trend line; increases and decreases occur in unfixed, variable periods.

• How to distinguish from seasonal: Cyclic fluctuations have no fixed period (e.g., pertussis peaks every 3–5 years); seasonal trends occur in fixed, known periods (e.g., winter flu).
• Example: Pertussis (whooping cough) shows cyclic peaks every 3–5 years.

🌨️ Seasonal trends

• Seasonal events recur in fixed, known periods (winter/summer, day of week, month, quarter).
• Reported as seasonal series or time series.
• Example: Flu activity peaks between December and February, lasting as late as May.

📈 Secular trends

• "Secular" (from Latin saeculum) refers to long periods of time (usually years).
• Influenced by population immunity, poverty levels, and access to preventive health services.
• Example: Salmonellosis data from 1968–1980 show long-term trends.

🔗 Clustering

Clustering: cases of a disease closely grouped in time and place.

• Can occur with diseases like cancer; may be linked to environmental exposures.
• Example: Geographic clusters of U.S. counties with high or low breast cancer rates.

🎯 Summary and Significance

🎯 Why person, place, and time matter

• These three variables form the backbone of descriptive epidemiology.
• They help identify associations and health determinants that explain health phenomena and illness.
• Similar to journalism's "who, where, when"—but with multiple sub-variables and layers of complexity.
• Each main category (person, place, time) contains numerous subcategories that require detailed analysis.
• The combination of all three dimensions provides a complete picture of health events and enables pattern recognition for public health interventions.

🧭 Overview

🧠 One-sentence thesis

Epidemiological study designs provide structured frameworks—ranging from simple descriptive case reports to complex randomized clinical trials—that guide investigators in choosing the most appropriate, efficient, and scientifically rigorous method to answer specific research questions about health phenomena in populations.

📌 Key points (3–5)

• Two broad categories: Descriptive studies (generate hypotheses, describe problems) vs. Analytic studies (analyze relationships, test hypotheses).
• Observational vs. Experimental: Observational studies (cross-sectional, case-control, cohort) observe without intervention; experimental studies (clinical trials) actively manipulate exposure or treatment.
• Common confusion—temporality: Cross-sectional studies capture data at one point in time and cannot establish cause-effect order; cohort studies follow subjects over time and can address temporality.
• Resource trade-offs: Descriptive and case-control studies are quick and inexpensive; cohort studies and clinical trials are costly and time-intensive but provide stronger evidence for causality.
• Selection depends on purpose: Use descriptive designs for unknown/rare diseases and hypothesis generation; use cohort or clinical trials when seeking causal relationships and when resources permit.

📚 Descriptive study designs

📝 Case studies and case series

Case studies/case series: reports of one or a few cases of a disease, typically used when little information exists or the disease is very rare.

• Purpose: Share information with the scientific community about new, rare, or unusual cases.
• Limitation: Cases are not representative of the general population, so findings cannot be generalized.
• Benefit: Opens opportunities for future research and hypothesis generation.
• Why limited use in epidemiology: The medical model reports individual patients; epidemiology focuses on populations at large.

Example: A physician reports three patients with an unusual symptom pattern—this case series alerts others but cannot prove causation or prevalence.

🌍 Ecologic studies

Ecologic studies: the unit of analysis is the group or population, not the individual; correlations are obtained between exposure rates and disease rates among different groups.

• Key characteristic: Data collected at the group level (e.g., countries, states, communities).
• Ecologic fallacy: Inferences true at the group level may not hold at the individual level.
  • Example: Data across countries showed moderate alcohol use increased coronary heart disease (CHD) risk at the population level, but individual-level studies found moderate consumption protective—what is true for the group is not necessarily true for individuals.
• Don't confuse: "Ecologic" refers to group-level analysis, not necessarily environmental exposures (though environmental issues can be studied this way).

Advantages:

• Quick, simple, less costly than other designs.
• Faster to complete.
• Useful for generating hypotheses, especially for diseases of unknown etiology.

Disadvantages:

• Ecologic fallacy limits individual-level conclusions.
• Imprecise measurement of exposure and disease.

Common uses:

• Geographical comparisons: Compare prevalence/incidence across areas.
• Time trends: Study fluctuations in chronic disease incidence over time.
• Migrants: Identify genetic vs. environmental factors by comparing first/second generation ethnic groups.
• Occupation and social class: Examine how morbidity/mortality associate with jobs and socioeconomic status.

✂️ Cross-sectional studies

Cross-sectional studies: draw a single sample from the target population and assess current exposure and disease status at one point in time.

• Analogy: Think of the population as a pie; a cross-sectional study examines one slice (section) that represents the whole.
• What they measure: Prevalence (not incidence), because data is collected at a single time point.
• Temporality problem: Cannot determine which came first—exposure or disease—because both are measured simultaneously.

Advantages:

• Less costly and fast to complete.
• Useful for calculating prevalence for public health reports.
• Help design and locate health services in a community.
• Generate hypotheses and research questions.

Disadvantages:

• Cannot address temporality or establish cause-effect relationships.

Example: An Australian cross-sectional study examined screen sedentary time (SST) vs. non-screen sedentary time (NSST) in adolescents and found SST contributes to fatness, but active lesson breaks and active transport (NSST factors) also matter—this snapshot identified associations but not causal pathways.

Don't confuse: Cross-sectional studies can be descriptive or analytic depending on whether they simply describe prevalence or analyze associations between variables.

🔬 Analytic observational designs

🔍 Case-control studies

Case-control studies: identify individuals with the disease ("cases") and similar individuals without the disease ("controls"), then compare past exposures retrospectively.

• Key feature: Selection based on disease status (outcome); then look backward in time to assess exposure.
• Data source: Medical records, disease registers, databases—data already collected.
• What they measure: Prevalence, not incidence (because exposure and disease already occurred).
• Single point in time: Data collection happens once, but it reflects past events.

How cases and controls are selected:

• Cases: Identified using defined criteria (symptoms, lab tests, imaging).
• Controls: Similar individuals from the same clinic, hospital, population, or community; matched on key characteristics (demographics) but without the disease.
• Comparison: Assess whether cases had higher exposure rates than controls.

Common uses:

• Find prevalence of diseases in the community.
• Study rare diseases (since you start with known cases, not a large population).
• Design subsequent studies based on findings.

Example: Investigators compared office workers with windows vs. without windows (case-control design). Workers without windows (cases) experienced more sleep disturbances and less physical activity; those with windows (controls) had more light exposure and longer sleep duration.

Don't confuse: "Exposed" in case-control studies is retrospective—cases already have the disease, and you look back to see if they were exposed; this differs from cohort studies where you start with exposure and follow forward to disease.

📈 Cohort studies

Cohort studies: start with individuals free of the disease, classify them by exposure status, and follow them over time to observe who develops the disease.

• Gold standard for causality: Considered the prototype for investigating causal relationships.
• What they measure: Incidence (new cases over time).
• Temporality: Cohorts address the cause-effect timing problem because exposure is measured before disease develops.

Types of cohorts:

Type	Data collection timing	Description
Prospective cohort	Future (classic cohort)	Start now, follow forward; most common meaning of "cohort study"
Retrospective cohort	Past	Use already-collected data; resembles case-control but follows cohort logic
Ambispective cohort	Past + present + future	Combines retrospective, current, and prospective data

Nested case-control:

• Investigators can take data from inside a cohort and design a case-control study—it is "nested" because it comes from within the cohort.

Advantages:

• Can study multiple diseases or multiple exposures.
• Calculates incidence.
• Addresses temporality and avoids logical errors about cause-effect order.

Disadvantages:

• Highly expensive (costs millions over years).
• Long duration, especially for diseases that take years to develop.
• Cannot be used for rare diseases (sample would be too small or take too long to accumulate cases).

🏥 Famous U.S. cohort examples

🫀 Framingham Heart Study

• Location: Framingham, Massachusetts.
• Start: 1948; enrolled over 5,000 adults.
• Collaboration: U.S. National Heart, Lung, and Blood Institute and Boston University.
• Design: Multiple exposures and outcomes measured repeatedly every few years; spouses, children, and grandchildren later enrolled.
• Impact: Responsible for much knowledge about heart disease, stroke, and intergenerational lifestyle effects; over 3,500 publications.
• Limitation: All participants were white/Caucasian—introduced confounding by race, a health determinant linked to income, socioeconomic status, and access to services.

🖤 Bogalusa Heart Study

• Location: Bogalusa, Louisiana (biracial black/white rural community).
• Start: 1972; founder Dr. Gerald Berenson (Tulane University).
• Purpose: Study cardiovascular risk in children and adolescents, especially African Americans.
• Major findings:
  • Confirmed Framingham findings but added new variables.
  • Discovered cardiovascular disease in children (e.g., an 8-year-old African American child with atherosclerotic deposits).
  • Led American Heart Association to recommend 2% milk for children over age 1 (instead of whole milk).
  • Identified obesity, hypertension linked to kidney disease, and early-onset diabetes as risk factors.
• End: Taken down after Hurricane Katrina (2005) due to sample damage and lack of funding.

💛 San Antonio Heart Study

• Location: San Antonio, Texas.
• Purpose: Identify cardiovascular risk factors in the Latino population.
• Rationale: Findings in white and African American populations cannot be extrapolated to Latinos.
• Major finding: Latinos have the lowest rates of heart attacks among major U.S. ethnic groups after accounting for confounding factors—"the Latino heart is hard to die."
• Note: Not as well known in scientific/teaching communities despite important data.

Historical context and disadvantages:

• Cohorts in the U.S. reflected racial preference and exclusion: Framingham studied only whites; Bogalusa and San Antonio were needed to study people of color.
• This history exemplifies the exclusion of oppressed and indigenous populations.
• Bogalusa and San Antonio studies are underrepresented in medical education and textbooks.

🧪 Experimental designs

🏘️ Community (preventive) trials

Community trials: evaluate the impact of interventions (policies, programs) on a target population or group to produce changes in knowledge, attitudes, practices, or health outcomes.

Process:

1. Determine eligible communities/groups and their willingness to participate.
2. Collect baseline data (demographics, cultural traits, census data, disease rates).
3. Use randomization to select participants.
4. Follow participants over time.
5. Measure outcomes to assess effectiveness or identify weak points.

Advantages:

• Unique in estimating the impact of behavior change or modifiable exposures on disease incidence in a community.
• Assess effectiveness of services and programs.

Disadvantages:

• Considered inferior to clinical trials: selection, intervention delivery, and monitoring are less rigorous.
• Affected by population dynamics (secular trends, mobility, changes in target population).
• Hard to avoid influence of non-intervention forces.

💊 Clinical (therapeutic) trials

Clinical trials: planned experiments that assess the efficacy of a treatment or medical procedure in people; study outcomes in a treated group are compared with outcomes in an equivalent control group, with both enrolled, treated, and followed over the same time period.

Major methods and strengths:

📋 Study protocol

• Extensive, detailed manual outlining major steps and contingencies.
• Specifies what to do if investigators deviate from planned assignments.
• Typically allows no more than three deviations.
• Includes data collection instruments, input procedures, and analysis plans.
• Planned crossovers: Study participant serves as their own control.
• Unplanned crossovers: Participant requests treatment change; too many compromise study quality.

🎲 Randomization

• Statistical method to sort and assign participants.
• Two-stage random selection:
  1. Person assigned a number, randomly picked for study participation.
  2. If selected, randomly assigned to treatment, placebo, or control branch.
• Preferred method to prevent mixing of intervention effects and differences among participants.

🙈 Blinding (controls bias, especially selection bias)

Type	Who is unaware	Description
Single blinding	Participants	Patients don't know which treatment or placebo they receive
Double blinding	Participants + providers	Patients and healthcare providers (doctors, nurses, technicians) don't know
Triple blinding	Participants + providers + analysts	Patients, providers, and data collectors/analysts don't know

🔢 Phases of clinical trials

Before a vaccine, drug, or treatment is licensed for general use:

• Phase I: Tests new vaccine/drug in adult volunteers (usually <100).
• Phase II: Expands testing to 100–200 subjects from the target population.
• Phase III (main test): Assesses efficacy in the target population.

Example: During COVID-19, new vaccines followed these phases before approval.

📊 CONSORT flowchart

• A reporting protocol with a 22-item checklist and flowchart.
• Guides the reporting of randomized trials to ensure transparency and rigor.

Strengths:

• Greatest control over exposure amount, timing, frequency, and observation period.
• Randomization greatly reduces likelihood that groups differ significantly.
• Enhances result validity.

Limitations:

• Ethical dilemmas:
  • How to ensure benefits outweigh risks.
  • How to protect participants' interests, not just investigators'.
  • When to stop a trial if major adverse outcomes occur.
  • How much information to share in the informed consent form.

🎯 Choosing the right design

❓ Key decision factors

Purpose/reason for the research:

• Unknown topic or need to generate hypotheses → Descriptive studies (case reports, ecologic, cross-sectional).
• Find causal relationships → Cohort studies or clinical trials.
• Study rare diseases → Case-control studies (start with known cases).

Resources (financial and expertise):

• Limited budget and time → Cross-sectional or case-control studies.
• Ample funding and long-term commitment → Cohort studies or clinical trials.

What you want to measure:

• Prevalence → Cross-sectional or case-control.
• Incidence → Cohort studies.
• Treatment efficacy → Clinical trials.

🔄 Study design classification summary

Category	Subcategory	Examples	Key use
Descriptive	—	Case reports, case series, ecologic, cross-sectional	Describe problems, generate hypotheses
Analytic	Observational	Cross-sectional (analytic), case-control, cohort	Analyze associations, test hypotheses
Analytic	Experimental	Community trials, clinical trials	Test interventions, establish efficacy

Don't confuse: Cross-sectional studies can be both descriptive (just reporting prevalence) and analytic (examining associations between variables).

---

Note: The excerpt also mentions that study designs are not unique to epidemiology—they are used in social sciences, public health, mathematics, statistics, and medical sciences. Investigators always seek designs that are relatively quick, less expensive, and efficient while meeting scientific rigor requirements.

🧭 Overview

🧠 One-sentence thesis

Epidemiology relies on quantitative measures—counts, ratios, proportions, rates, prevalence, and incidence—to describe disease frequency and assess the health impact on populations, with these calculations forming the foundation for understanding disease burden and guiding public health interventions.

📌 Key points (3–5)

• Epidemiology is quantitative: uses numbers, statistics, and mathematical concepts to describe and interpret health events.
• Core measures build on each other: counts → ratios → proportions → rates → prevalence/incidence, each adding more information.
• Prevalence vs. incidence distinction: prevalence captures all existing cases (snapshot), while incidence captures only new cases over time (movie); incidence requires "population at risk."
• Common confusion—time matters: prevalence can be point (one moment) or period (range); incidence always incorporates time and is essential for tracking disease progression.
• Rates as health indicators: birth, fertility, infant mortality, and maternal mortality rates serve as indicators of population health and guide resource allocation.

🔢 Building blocks of measurement

🔢 Counts

A count: the simplest record of cases, used especially for rare or newly emerging diseases.

• When a disease first appears or is very rare, only counting is possible (e.g., 1 case of Ebola in 1976, 11 cases of anthrax after September 11).
• No statistical analysis needed—just recording the number.
• Example: "5 confirmed cases of a new disease" is a count.

🔢 Ratios

Ratio: one quantity divided by another (a/b), with no required relationship between numerator and denominator.

• Used to compare one group to another.
• Example: breast cancer affects women 100 times more than men → ratio 100:1.
• Helps assess the impact of a health problem across different groups.

🔢 Proportions

Proportion: a ratio in which the numerator is part of the denominator.

• Adds meaning by showing a count relative to the group size.
• Often expressed as a percentage for easier understanding.
• Example: "Greater than 90% of lung cancer cases are attributable to cigarette smoking" shows the proportion of cases linked to an exposure.
• Don't confuse: a proportion is a special type of ratio, not a separate concept—the numerator must be included in the denominator.

🔢 Rates

Rate: a ratio that includes time in the denominator, plus disease frequency, population size, and time period.

• The time dimension distinguishes rates from other measures.
• Components: disease frequency, unit size of population, time period.
• Foundation for prevalence and incidence calculations.

📊 Prevalence: measuring existing disease

📊 What prevalence measures

Prevalence: the number of existing cases of a health problem in a specific population at a certain time.

• Captures all cases (both old and new).
• Requires a clear case definition: standard criteria to identify who has the disease.
• Formula: (number of existing cases / total population) × multiplier

📊 Point vs. period prevalence

Point prevalence: prevalence at one specific moment in time.

• Example: 5 tuberculosis cases identified on March 1st, 2010 out of 500 people = 1 per 100 population on that date.

Period prevalence: prevalence over a range of time.

• Example: 50 tuberculosis cases over 2 years in 500 people = 5 per 100 population over two years.
• Adds more context than a single point in time.

📊 Uses of prevalence

• Estimate frequency of exposure or chronic disease.
• Describe the burden of a health problem (its impact on the community).
• Determine allocation of health resources (hospital beds, facilities, personnel).
• Provides a "snapshot" of disease at a given time.

🎬 Incidence: measuring new disease

🎬 What incidence measures

Incidence: the number of new cases in a population at risk over a specific time period.

• Formula: (number of new cases / population at risk) × multiplier × time period
• Population at risk: only those who could develop the disease (excludes those who already have it).
• Example: 45 new tuberculosis cases (excluding 5 existing) among 45 at-risk people = 88.8 per 100 population.

🎬 The waterfall analogy

• Incidence = drops feeding the pool (new cases flowing in).
• Prevalence = total water in the pool (all cases, new and old).
• Incidence shows the "movie" of disease progression over time, not just a snapshot.

🎬 Why incidence matters

• Measures disease progression and accumulation.
• Essential for tracking infectious disease control (can record by seconds, minutes, hours, days, weeks, months, years).
• Used for chronic disease survival rates and disability years.
• Helps assess possible etiological causes (what causes the disease).

🔍 Comparing prevalence and incidence

Feature	Prevalence	Incidence
Numerator	All cases (old + new)	Only new cases
Denominator	Total population	Population at risk
Time	Point or period	Always includes time intervals
Metaphor	Snapshot	Movie/video
Use	Disease burden, resource planning	Disease progression, causation

Common confusion: Prevalence can be calculated without detailed time tracking (point prevalence), but incidence always requires time periods because it tracks new case development.

🗺️ Adding location and specificity

🗺️ Geographic and spatial reporting

Both prevalence and incidence can be reported by:

• Country: prevalence of type 2 diabetes in the United States
• Region: prevalence of asthma in school children in a region
• Continent: prevalence of malaria in Africa
• Spatial: prevalence of depression in urban settings

🗺️ Combining variables

Multiple factors can be combined:

• Time + location: "obesity prevalence in the U.S. in 2019"
• Time + location + age: "obesity prevalence among school children K-12 in the U.S. in 2019"
• Maps often use prevalence data to show geographic distribution of health problems.

📈 Rates as health indicators

📈 Crude vs. specific rates

Crude rates: summary rates for the total population without specifics.

• Quick overview but lacks detail.
• Signals need for intervention but not where to target.

Specific rates: calculated for particular characteristics (age, gender, ethnicity).

• Preferred when available because they show who is affected.
• Example: age-specific mortality rate, gender-specific disease rate.

Adjusted rates: eliminate confounding factors for more accurate comparisons (advanced topic, not covered in detail in this excerpt).

📈 Common rate calculations

General formula: (total cases / total population or population at risk) × multiplier

Crude Death Rate: (total deaths during year / total population) × 100,000

Sex-specific mortality rate: separate calculations for males and females

Cause-specific mortality rate: (deaths from specific cause / population at midpoint) × 100,000

Case fatality rate: measures deaths among those who have the disease

👶 Maternal and child health indicators

👶 Birth and fertility rates

Crude Birth Rate: (live births in period / mid-population of period) × 1,000

• Only live births in numerator.
• U.S. birth rates have been declining.

Fertility Rate: (live births in year / women aged 15-44 at midyear) × 1,000 women aged 15-44

• Focuses on women of reproductive age.
• U.S. fertility dropped to 1.64 in 2020, the lowest ever recorded.

👶 Fetal and infant mortality

Fetal Death Rate: (fetal deaths ≥20 weeks / live births + fetal deaths ≥20 weeks) × 1,000

• Denominator includes both live births and fetal deaths.
• Deaths ≤20 weeks are considered miscarriage or abortion, not fetal death.

Late Fetal Death Rate: uses ≥28 weeks gestation instead of ≥20 weeks.

Fetal Death Ratio: (fetal deaths ≥20 weeks / live births) × 1,000

• Denominator includes only live births, not fetal deaths.

Infant Mortality Rate: (infant deaths 0-365 days / live births during year) × 1,000

• Highly recognized indicator of overall population health.
• U.S. rate: 5.6 per 1,000 live births (2019); Minnesota: 4.47 per 1,000.
• Reflects quality of care for pregnant women and children.

👶 Neonatal and perinatal measures

Neonatal Mortality Rate: (infant deaths <28 days / live births) × 1,000

Postneonatal Mortality Rate: (infant deaths 28-365 days / live births minus neonatal deaths) × 1,000

• Requires neonatal deaths to be calculated first.

Perinatal Mortality Rate: (late fetal deaths ≥28 weeks + infant deaths ≤7 days / live births + fetal deaths) × 1,000

Perinatal Mortality Ratio: (late fetal deaths ≥28 weeks + infant deaths ≤7 days / live births) × 1,000

• Ratio is more practical when fetal death data is insufficient.

👶 Maternal mortality

Maternal Mortality Rate: (deaths from childbirth causes / live births) × 100,000

• Strong indicator of healthcare conditions, especially for pregnancy and childbirth.
• Reflects poverty levels and care quality for women.
• U.S. baseline (2018): 17.4 per 100,000; Healthy People 2030 target: 15.7 per 100,000.
• Developed countries generally have lower rates than developing countries.

📐 Additional specific measures

📐 Proportional Mortality Ratio (PMR)

PMR (%): (mortality from specific cause / mortality from all causes) × 100

• A percentage, not a rate (multiplier is 100).
• Shows relative importance of a specific cause of death, not risk of dying.
• Used to create "top 10 causes of death" reports for policymakers and healthcare providers.

📐 Key reminders

• Always state the unit of analysis (e.g., per 1,000 live births, per 100,000 population).
• Use midpoint population when specified (e.g., mid-year = end of June).
• Different multipliers by convention: 1,000 for birth/infant rates, 100,000 for maternal mortality.
• Don't confuse: these calculations may seem simple but require careful attention to which population goes in the numerator vs. denominator.

🧭 Overview

🧠 One-sentence thesis

Odds ratios and relative risk are the two most common statistical measures of association in epidemiology, used to quantify the relationship between risk factors and health outcomes, with each method suited to different study designs and research questions.

📌 Key points (3–5)

• What these measures do: Both odds ratio (OR) and relative risk (RR) quantify the association between exposure to a risk factor and a health event, expressed as probabilities.
• Study design connection: Odds ratio works best for case-control studies (comparing cases to controls), while relative risk is more appropriate for cohort studies (assessing causation over time).
• Interpretation threshold: OR or RR = 1.0 means no association; >1.0 suggests the exposure is a risk factor; <1.0 suggests the exposure may be protective.
• Common confusion: Odds ratio and relative risk use the same 2×2 table layout but different formulas—OR compares odds of exposure between cases and controls, while RR compares disease rates between exposed and unexposed groups.
• Practical applications: These measures are essential for disease outbreak investigations, including attack rates, secondary attack rates, and case fatality rates.

📊 Understanding risk assessment fundamentals

📊 What is risk?

Risk: the probability that something will occur (happen) in a certain period of time.

• In mathematical terms, valid probability must be greater than zero and less than one.
• Risk is not just "how many people got sick" but "the likelihood of getting sick."

🔍 What is risk assessment?

Risk assessment: a series of techniques used to assess, or evaluate risk.

• Goes beyond simple probabilities to include contributing factors.
• Incorporates risk factors: factors associated with the health event but not necessarily the cause.
• Example: Smoking may be a risk factor for lung cancer—risk assessment quantifies how strong that association is.

🎯 Measures of association

• The results of odds ratio or relative risk calculations are called measures of association.
• They assess the possible association of a risk factor with a health event (disease or not).
• These are the most common measures in epidemiology because they provide a platform to conduct research studies used to assess risk.

🧮 The 2×2 table foundation

🧮 Why use a 2×2 table?

• The 2×2 table assists with the visualization of risk probabilities.
• Called "2×2" because it has two rows (horizontal) and two columns (vertical).
• By convention, cells are filled with letters (a, b, c, d) used in calculation formulas.

📐 Standard layout with labels

	Exposed	Non-exposed	Totals
Yes (diseased)	a	b	a+b
No (not diseased)	c	d	c+d
Totals	a+c	b+d	a+b+c+d

Cell meanings:

• a = number of cases with the exposure
• b = number of controls with the exposure
• c = number of cases without the exposure
• d = number of controls without the exposure

🎲 Odds Ratio (OR)

🎲 Definition and concept

The odds is the probability that an event will happen divided by the probability that it won't happen.

• Odds ratio compares the odds of exposure to the factor of interest among cases to the odds of exposure to the factor among controls.
• Most commonly used for case-control studies, where cases (those with disease) are compared to controls (those without disease).

🧪 The formula

Odds ratio = (a × d) / (b × c)

• This equals: odds of exposure (cases) divided by odds of exposure (controls).
• Uses the four cell values from the 2×2 table.

💡 Worked example: Smoking and lung cancer

A study found 100 cases of lung cancer and 100 controls. From 130 smokers total, 90 had lung cancer.

Complete 2×2 table:

	Diseased (lung cancer)	Non-diseased	Total
Exposed (smoked)	90	40	130
Not exposed (no smoke)	10	60	70
Total	100	100	200

Calculation:

• OR = (90 × 60) / (40 × 10) = 5,400 / 400 = 13.5
• Since OR = 13.5 (much greater than 1.0), smoking is a significant risk factor for lung cancer.

📈 Relative Risk (RR)

📈 Definition and purpose

Relative risk shows how much more likely (or less likely) it is for people exposed to a factor to develop a disease compared to people not exposed to the factor.

• Used to assess causation, not just association.
• Commonly used for cohort studies that follow groups over time.
• More moderate (lower) results than odds ratio—considered more credible because it doesn't exaggerate.

🧪 The formula

Relative Risk = [a / (a+b)] / [c / (c+d)]

• This is the ratio of disease among people exposed to the factor versus those not exposed.
• Key difference from OR: uses the row totals in the calculation.

💡 Same example: Smoking and lung cancer

Using the same data as the OR example:

Calculation:

• RR = (90/130) / (10/70) = 0.692 / 0.142 = 4.8
• Since RR = 4.8 (greater than 1.0), smoking is a significant risk factor.
• Note: RR of 4.8 is lower than OR of 13.5, providing a more conservative estimate.

⚖️ Don't confuse: OR vs RR totals

• Both use the same 2×2 table categories.
• Main difference: relative risk uses the totals (row sums) for computation; odds ratio does not.

🔢 Interpreting results

🔢 Universal interpretation rules

Result	Meaning	Implication
OR or RR = 1.0 (or close to 1.0)	Risk is similar in exposed and unexposed groups	Exposure is NOT associated with disease
OR or RR > 1.0	Risk is greater in the exposed group	Exposure could be a RISK factor
OR or RR < 1.0	Risk is less in the exposed group	Exposure could be a PROTECTIVE factor

🎯 Practical meaning

• Example: If OR = 13.5, people exposed to the factor have 13.5 times the odds of disease compared to unexposed people.
• Example: If RR = 4.8, exposed people are 4.8 times as likely to develop disease as unexposed people.

🚨 Disease outbreak measures

🚨 Attack Rate

Formula: Attack rate = [ill / (ill + well)] × 100 during a time period

• A public health measure used to control disease by separating the ill from the well.
• Helps understand why some people in a group did not get sick.
• Reflects the concept of relative risk—the relative risk is the ratio of attack rates.

🔄 Types of attack rates

Crude attack rate:

• Uses all people invited/present as the denominator.
• May include people not actually at risk (e.g., those who didn't eat at a food event).

Attack rate (100%):

• More accurate denominator—excludes people not at risk.
• Example: In a picnic outbreak, subtract those who only drank and ate no food.

Food-specific attack rate:

• Calculates attack rate for each specific food item.
• Allows comparison to predict which food caused the outbreak.
• Example: Potato salad attack rate = 90/400 × 100 = 22.5%; Lasagna = 105/400 × 100 = 26.25%.

🔁 Secondary Attack Rate

Formula: [# of new cases in the group − initial case(s)] / [# of susceptible persons in the group − initial case(s)] × 100

Key terms:

• Initial case(s) = Index case(s) + co-primaries

• Index case(s) = the case that first comes to public health authorities' attention

• Co-primaries = cases so close in time to the index case they belong to the same generation

• Measures spread to people exposed to the original diseased group (the exposed group).

• Integrates concepts of both attack rate and prevalence/incidence.

⚰️ Case Fatality Rate (CFR)

Formula: CFR (%) = [# of deaths due to disease X / # of cases of disease X] × 100 during a time period

• Assesses the capacity of an agent or factor to kill the affected population.
• Used to measure virulence—if an agent kills a high number, it is highly virulent.
• Example: Early COVID-19 U.S. data showed CFR = 6.0%; by March 2022 it was 1.2%.

📋 Outbreak investigation protocol

The CDC's systematic process includes 13 steps (cannot be skipped):

1. Prepare for field work
2. Establish the existence of an outbreak
3. Verify the diagnosis
4. Construct a working case definition
5. Find cases systematically and record information
6. Perform descriptive epidemiology
7. Develop hypotheses
8. Evaluate hypotheses epidemiologically
9. Reconsider, refine, and re-evaluate hypotheses as necessary
10. Compare and reconcile with laboratory/environmental studies
11. Implement control and prevention measures
12. Initiate or maintain surveillance
13. Communicate findings

🍔 Common outbreak types

🍔 Foodborne illness

• Food follows a long food production chain from field to table.
• Attention must be paid to different points: planting, packing, distribution, consumption.
• Common bacterial agents: Staphylococcus aureus, Clostridium perfringens, Campylobacter spp., Salmonella spp., E. coli.

Example agents and characteristics:

Agent	Incubation Period	Common Manifestations
Staphylococcus aureus	2–4 hours	Vomiting, gastrointestinal syndrome
Cholera (Vibrio cholerae)	2–3 days	Profuse watery diarrhea (painless)
Campylobacter jejuni	2–5 days	Abdominal pain, diarrhea, fever
Clostridium perfringens	10–12 hours	Diarrhea
Clostridium botulinum	12–36 hours	Neurological symptoms, diarrhea
Salmonella spp.	12–36 hours	Gastrointestinal syndrome

💧 Waterborne diseases

• Some are also foodborne (e.g., cholera transmitted by contaminated water and food).
• Common waterborne diseases: cholera, giardiasis, amebiasis, legionellosis, schistosomiasis.
• Cholera was epidemic in Peru, Mexico, and Central America in the 1990s; recent outbreaks in Africa.

💉 Vaccine-preventable diseases

• Before COVID-19, these caused major outbreaks worldwide.
• Common diseases: diphtheria, pertussis, tetanus, Haemophilus influenzae, hepatitis A and B, measles, rubella, mumps, polio.
• Mostly under control due to child vaccination emphasis, but measles outbreaks still reported in recent years.

🩺 Sexually transmitted diseases

• HIV/AIDS is the most significant example, reaching epidemic proportions since the 1980s.
• Still a serious problem in the U.S. and worldwide, though receiving less attention than COVID-19.
• Reminder: other infectious and non-infectious diseases need attention to prevent continuous health emergencies from re-emergent and newly emerging diseases.