**Epidemiology** is the study of the health and illness of populations. The three levels of causation of health problems include individual behavioral level, individual biological level, and political-economic ecological level.

Aims
----

Epidemiologists aim to understand the causes of health problems by looking at the relationship between agents, hosts, and environmental factors that affect health. Using these information, they also design public health interventions to solve various health problems in populations. They regularly evaluate the health of populations, try to identify which populations have greater risk from specific causes of diseases, and evaluate the intervention programs or methods that they create. Epidemiology is the foundation of public health and preventative medicine practices.

Descriptive/analytic epidemiology
---------------------------------

There are two types of epidemiology: descriptive and analytic. Descriptive epidemiology aims to describe the distribution of people who gets sick (compared with those who don't) with regards to time, place, and person (TPP). It can be thought of as answering the questions of when, who, and what respectively. It investigates when a health issue or disease first emerged in a population, who in the population are getting sick, and where the disease/health condition seems to have started and spread from. Analytic epidemiology then uses the data provided by TPP to make and test hypothesis which aim to determine the cause of an outbreak or disease cluster in a population. Epidemiologists evaluate morbidity (people who are sick or injured) and mortality to understand the burden of disease among populations.

Surveillance studies
--------------------

Surveillance studies provide the numerical data and statistics that epidemiologists use. Surveillance studies are done to monitor the health of populations and help with identifying any new health problems or disease that may develop, as well as evaluating the effectiveness of existing health intervention measures in place. These studies also rely on TPP to collect data and present it in simple graphs and tables which are easy to summarize and understand. These studies are especially important in helping guide policy making decisions and helping epidemiologists understand which interventions work and which don't. It is also important for donors as they rely on surveillance data to analyze the usefulness of their investments and whether it is being used effectively or not.

Definition and origin of term
-----------------------------

Original map by John Snow showing the clusters of cholera cases in the London epidemic of 1854
Epidemiology means "the study of what is upon the people". The word derived from the Greek terms *epi* = upon, among; *demos* = people, district; *logos* = study, word, discourse. It applies only to human populations. But the term is used in studies of zoological populations 'epizoology', and plant populations.

History
-------

Hippocrates was the first who looked at the relationships between disease and environmental influences. He drew the distinction between 'epidemic' and 'endemic': diseases that are 'visited upon' a population (epidemic) as contrasted with those that 'live within' a population (endemic).

The Persian physician Avicenna in the 1020s, discovered the contagious nature of tuberculosis and sexually transmitted disease. He noted the distribution of disease through water and soil. Avicenna said that bodily secretion is contaminated by foul foreign earthly bodies before being infected. He introduced the method of quarantine to limit the spread of contagious disease.

### Black death

The Black Death (bubonic plague) reached Al Andalus in the 14th century. Ibn Khatima thought infectious diseases were caused by "minute bodies" which enter the human body and cause disease. Another Andalusian-Arabian physician, Ibn al-Khatib (1313–1374) in his treatise *On the Plague* stated how infectious disease can be transmitted through bodily contact and "through garments, vessels and earrings". Girolamo Fracastoro from Verona suggested these very small, unseeable, particles that cause disease were alive. They were able to spread by air, and multiply. They could be destroyed by fire. He refuted Galen's miasma theory (poison gas in sick people). In 1543, Fracastoro's book *De contagione et contagiosis morbis* suggested personal and environmental hygiene to prevent disease. The development of a sufficiently powerful microscope by Anton van Leeuwenhoek in 1675 provided visual evidence of living particles consistent with a germ theory of disease.

### Great Plague

In 1662 John Graunt analysed the mortality rolls in London before the Great Plague. This gave statistical evidence for and against various theories of disease.

### 19th century

Dr. John Snow investigated the causes of the 19th Century Cholera epidemics. He noticed the significantly higher death rates in two areas supplied by Southwark Water Company. He showed the Broad Street pump was the origin of the Soho epidemic, a classic example of epidemiology He used chlorine in an attempt to clean the water and had the pump handle removed. This stopped the outbreak. It was a major event in the history of public health, and the founding event of the science of epidemiology.
The term 'epidemiology' was first used in 1802 by the Spanish physician Villalba.

In 1847 Hungarian physician Ignaz Semmelweis brought down infant mortality at a Vienna hospital by disinfection. Unfortunately, disinfection did not become widely practiced until British surgeon Joseph Lister 'discovered' antiseptics in 1865 after Louis Pasteur's work. 

### 20th century

In the early 20th century, mathematical methods were introduced into epidemiology by Ronald Ross and others. In 1954 came the results of a study led by Richard Doll. This gave very strong statistical support to the suspicion that tobacco smoking was linked to lung cancer.

Important concepts/terms
------------------------

There are several very key terms that epidemiologists use when discussing population health and disease outbreaks. Some of the key concepts that are important to understand when discussing epidemiology:

* **Cases**: refers specifically to those people who are sick with a disease/health condition or injured
* **Epidemic / Outbreak**: is the occurrence of a disease among a population that is in higher than what is expected for that given time and place
* **Endemic**: a disease or health condition that is present in the population at all times during the year
* **Pandemic**: a disease that spreads across various regions; also refers to global outbreaks that spreads over multiple continents
* **Cluster**: refers to group of *cases* in a specific time and place that's more than expected
* **Population at risk**: refers to those within a population who are particularly susceptible to a certain disease or health condition

It is important to note that an endemic disease or cluster can become an epidemic. An example of this would be malaria; although malaria is endemic to certain regions in South America, Africa, and South Asia, during certain years or times it can become an epidemic with higher number of cases then usual present in the population. In addition, it is also possible for an epidemic or outbreak to progress and become a full-fledged pandemic.

Calculating Disease Rates
-------------------------

Rates refers to the number of cases occurring during a specific period of time and depends on the population size at that time. Calculating disease rates helps epidemiologists to compare health issues among different populations. The general calculation for determining disease rate is to divide the number of cases or health condition by the number of population at risk during a specific period of time, and multiplying that by 100. However, disease rates can also be differentiated into two different types: prevalence rate and incidence rate.

Prevalence rate refers to the number of both old and new cases in a population during a specific time period, which is divided by the total number of cases in the population. Prevalence rates are useful when dealing with investigations relating to chronic diseases, which last for more than 3 months. Incidence rate refers to the number of new health related conditions or cases which is divided by the population at risk. Incidence rates are important in studies involving acute diseases, where symptoms of a disease peak and subside within days or weeks and generally lasts less than 3 months.

Types of Epidemiological Studies
--------------------------------

Epidemiological studies makes use of both experimental and observational studies.

### Experimental studies

Experimental studies are ones where the epidemiologist can control and manipulate different variables throughout the experiment. It usually involves a placebo treatment/group. This type of study is used when epidemiologists are trying to determine the cause of a health issue/disease or evaluating the effectiveness of a cure or interventions. 

### Observational studies

Observational studies include descriptive and analytical studies; descriptive studies investigate epidemiological cases with regards to TPP while analytical study investigates hypothesis regarding relationships between health issues and risk factors. While descriptive studies answer questions of when, who, and where, analytical studies try to answer the question of how a population is affected by a disease and why they are affected. Observational studies do not manipulate any variables and often uses comparison groups for analysis. This type of study is often done in an attempt to discover the links between exposure to certain risk factors and health outcomes. Some examples of observational studies include cohort studies, case-control studies, and cross-sectional studies.

* **Cohort study**: participants are categorized based on exposure to disease, risk factor, or presence of a health condition and are observed over time to see if they develop symptoms of the disease
* **Case-control study**: Those individuals who are identified as cases (has the disease or health condition) are compared with those who don't have it.
* **Cross - sectional study**: provides a "one-shot" picture of a group at a certain point in time; participants are selected based on a specific characteristic or because they belong to a certain population/group and are examined to see how the disease/health condition has affected their group.

Process of Epidemiological Investigations
-----------------------------------------

Investigating an outbreak is a very involved multi-step process which ranges from first establishing the existence of an outbreak to communicating the findings of the investigation with the scientific community as well as the general population. The following is a rough sequence of the process of these investigations.

1. **Establish that there is an outbreak**. Epidemiologists look at data (TPP) and surveillance studies to determine if there have been similar cases, like the one being investigated, in the past or if it is a completely new type of disease or health condition.
2. **Prepare for field work**. Once an outbreak has been established, epidemiologists take preparations, arrange materials/equipment for travel to investigate the outbreak at its place of origin and other locations where it may have spread to.
3. **Verify the diagnosis**. Researchers review all laboratory/clinical findings and interview patients to get a better sense of what they are dealing with and to confirm their initial diagnosis of an outbreak.
4. **Define/identify case**. Epidemiologists must come up with a precise and standard definition of what a case is or what a case looks like because that will be used to determine who is a case and who isn't.
5. **Descriptive epidemiology**. Then next step here is to describe the outbreak in terms of time, person, and place (TPP).
6. **Develop a hypothesis**. Epidemiologists must formulate a hypothesis about cause/risk factors of the disease, then evaluate the hypothesis and refine it as needed.
7. **Implement necessary control & preventative measures**. This may include things like social distancing, wearing masks, frequently washing hands, as well as isolation and quarantine.
8. **Communicate research/investigation findings**. Epidemiologists must determine which information is important and how findings will be communicated. They must also determine who the audience is that needs to know the information (is it something only health care workers need to be on the lookout for or should the general public also be made aware?)

Professions In Epidemiology
---------------------------

Epidemiology is a multidisciplinary subject. It includes public health care workers and scientists from fields such as chemists, biologists, geneticists, and anthropologists. They may work in hospital and research settings, as well as for government organizations such as the Centers for Disease Control and Prevention (CDC). The Epidemic Intelligence Service is a subgroup within the CDC specializing in epidemiology. Epidemic Intelligence Service officers are field workers who investigate outbreaks in the US and other countries. Their work aids in understanding causes of outbreaks and quickly stopping spread of diseases from one place to another; notably, they have contributed to helping during various pandemics in the past, such as with smallpox, polio, and Ebola.

### Medical Anthropology & Epidemiology

Cultural anthropologists have been a very helpful resource in bridging the gap between different countries/cultures and the epidemiological investigators. They help with the development and implementation of preventative/control measures in countries in a manner that will not conflict with societal beliefs or values, which may get in the way of treatment or stopping the spread of an outbreak.

Medical anthropology looks at biological, social, cultural, and linguistic anthropology to understand how they influence health and well-being, experience and distribution of illness, as well as prevention of treatment. For example, they investigate how culture affects research studies, are able to pick up on seemingly "irrelevant" yet important small details that an epidemiologist might miss, and they are able to provide qualitative data whereas epidemiology only focuses on quantitative data. Epidemiology has largely ignored cultural factors when looking at the causes of diseases/health conditions. Medical anthropology has contributed to the field by showing how culture and social factors play a big role in people's willingness to follow public health guidelines/interventions or even accept treatment for their illnesses.

Epidemiological investigations may lead to a reductionist or limited point of view, medical anthropology provides a more holistic view of the problem and examines the issue from different angles to best understand and help the populations in need. Another critical contribution of medical anthropology has been with critical qualitative data. It provides rich sources of information on understanding the population's subjective experiences. It provides qualitative data explaining why a particular intervention or treatment may have failed among a particular population. Their main contributions have been helping develop epidemiological surveys by taking into account word choice and the "social suitability" of questions. Medical anthropologists have played a large role in helping locals better understand the objectives of epidemiologists and helped them become more receptive of their investigations. Medical anthropology helps explain health phenomenon and create and test hypothesis relating to such explanations. While epidemiology is good at understanding numerical patterns and biological causes for disease/health conditions, it is unable to fully explain all the factors that underlie certain diseases/health conditions. This is a gap that medical anthropology has been able to pick up on and complement by providing rich qualitative data which looks at health and diseases from a holistic perspective.

### Notable works

Some notable mentions of medical anthropologists who have worked and contributed to epidemiology and public health in general include Dr. Jim Kim and Paul Farmer. Dr. Jim Kim served as the president of the World Bank while Paul Farmer is a physician and medical anthropologist who focuses on infectious diseases and treatment. These men co-founded the Partners in Health program, which provided free health care to the poorest populations in countries like Haiti, Peru, and Rwanda. Other notable mentions are Amber Wutich and Alexandra Brewis,. Both are professors and researchers at Arizona State University. They focus on health impacts caused by resource scarcity, specifically relating to lack of access to clean water in developing countries. Wutich is a director of the Global Ethnohydrology study which looks at water knowledge and management in ten countries, while Brewis researches the impact of culture on human biology.

Population-based health management
----------------------------------

Epidemiological practice and the results of epidemiological analysis make a significant contribution to health management

* Assess the health states and needs of a target population
* Implement and evaluate interventions
* Provide care for members of that population

Modern population-based health management is complex. Epidemiological practice and analysis is a core component. This task requires the forward looking ability to guide how a health system responds to current health issues, and how a health system can respond to future potential population health issues.

Additional Bibliography
-----------------------

* Last JM (2001). "A dictionary of epidemiology", 4th edn, Oxford: Oxford University Press. 5th. edn (2008), edited by Miquel Porta  Archived 2013-04-29 at the Wayback Machine
* Morabia, Alfredo. ed. (2004) A History of Epidemiologic Methods and Concepts. Basel, Birkhauser Verlag. Part I.
* Rothman, Kenneth, Sander Greenland and Timothy Lash (2008). "Modern Epidemiology", 3rd Edition, Lippincott Williams & Wilkins. ISBN 0781755646, ISBN 978-0781755641

Related pages
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* Centers for Disease Control and Prevention
* World Health Organization

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* Outline
* Index

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| Descriptive statistics |
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| Continuous data | 

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| Center | 
* Mean
	+ arithmetic
	+ geometric
	+ harmonic
	+ cubic
	+ generalized/power
* Median
* Mode

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| Dispersion | 
* Variance
* Standard deviation
* Average absolute deviation
* Coefficient of variation
* Percentile
* Range
* Interquartile range

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| Shape | 
* Central limit theorem
* Moments
	+ Skewness
	+ Kurtosis
	+ L-moments

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| Count data | 
* Index of dispersion

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| Summary tables | 
* Grouped data
* Frequency distribution
* Contingency table

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| Dependence | 
* Pearson product-moment correlation
* Rank correlation
	+ Spearman's ρ
	+ Kendall's τ
* Partial correlation
* Scatter plot

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| Graphics | 
* Bar chart
* Biplot
* Box plot
* Control chart
* Correlogram
* Fan chart
* Forest plot
* Histogram
* Pie chart
* Q–Q plot
* Run chart
* Scatter plot
* Stem-and-leaf display
* Radar chart
* Violin plot

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| Data collection |
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| Study design | 
* Population
* Statistic
* Effect size
* Statistical power
* Optimal design
* Sample size determination
* Replication
* Missing data

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| Survey methodology | 
* Sampling
	+ stratified
	+ cluster
* Standard error
* Opinion poll
* Questionnaire

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| Controlled experiments | 
* Scientific control
* Randomized experiment
* Randomized controlled trial
* Random assignment
* Blocking
* Interaction
* Factorial experiment

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| Adaptive Designs | 
* Adaptive clinical trial
* Up-and-Down Designs
* Stochastic approximation

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| Observational Studies | 
* Cross-sectional study
* Cohort study
* Natural experiment
* Quasi-experiment

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| Statistical inference |
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| --- | --- |
| Statistical theory | 
* Population
* Statistic
* Probability distribution
* Sampling distribution
	+ Order statistic
* Empirical distribution
	+ Density estimation
* Statistical model
	+ Model specification
	+ L*p* space
* Parameter
	+ location
	+ scale
	+ shape
* Parametric family
	+ Likelihood (monotone)
	+ Location–scale family
	+ Exponential family
* Completeness
* Sufficiency
* Statistical functional
	+ Bootstrap
	+ U
	+ V
* Optimal decision
	+ loss function
* Efficiency
* Statistical distance
	+ divergence
* Asymptotics
* Robustness

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| Frequentist inference | 

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| Point estimation | 
* Estimating equations
	+ Maximum likelihood
	+ Method of moments
	+ M-estimator
	+ Minimum distance
* Unbiased estimators
	+ Mean-unbiased minimum-variance
		- Rao–Blackwellization
		- Lehmann–Scheffé theorem
	+ Median unbiased
* Plug-in

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| Interval estimation | 
* Confidence interval
* Pivot
* Likelihood interval
* Prediction interval
* Tolerance interval
* Resampling
	+ Bootstrap
	+ Jackknife

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| Testing hypotheses | 
* 1- & 2-tails
* Power
	+ Uniformly most powerful test
* Permutation test
	+ Randomization test
* Multiple comparisons

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| Parametric tests | 
* Likelihood-ratio
* Score/Lagrange multiplier
* Wald

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| Specific tests | 

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* *Z*-test (normal)
* Student's *t*-test
* *F*-test

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| Goodness of fit | 
* Chi-squared
* *G*-test
* Kolmogorov–Smirnov
* Anderson–Darling
* Lilliefors
* Jarque–Bera
* Normality (Shapiro–Wilk)
* Likelihood-ratio test
* Model selection
	+ Cross validation
	+ AIC
	+ BIC

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| Rank statistics | 
* Sign
	+ Sample median
* Signed rank (Wilcoxon)
	+ Hodges–Lehmann estimator
* Rank sum (Mann–Whitney)
* Nonparametric anova
	+ 1-way (Kruskal–Wallis)
	+ 2-way (Friedman)
	+ Ordered alternative (Jonckheere–Terpstra)

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| Bayesian inference | 
* Bayesian probability
	+ prior
	+ posterior
* Credible interval
* Bayes factor
* Bayesian estimator
	+ Maximum posterior estimator

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| * Correlation
* Regression analysis
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| Correlation | 
* Pearson product-moment
* Partial correlation
* Confounding variable
* Coefficient of determination

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| Regression analysis | 
* Errors and residuals
* Regression validation
* Mixed effects models
* Simultaneous equations models
* Multivariate adaptive regression splines (MARS)

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| Linear regression | 
* Simple linear regression
* Ordinary least squares
* General linear model
* Bayesian regression

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| Non-standard predictors | 
* Nonlinear regression
* Nonparametric
* Semiparametric
* Isotonic
* Robust
* Heteroscedasticity
* Homoscedasticity

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| Generalized linear model | 
* Exponential families
* Logistic (Bernoulli) / Binomial / Poisson regressions

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| Partition of variance | 
* Analysis of variance (ANOVA, anova)
* Analysis of covariance
* Multivariate ANOVA
* Degrees of freedom

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| Categorical / Multivariate / Time-series / Survival analysis |
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| Categorical | 
* Cohen's kappa
* Contingency table
* Graphical model
* Log-linear model
* McNemar's test
* Cochran-Mantel-Haenszel statistics

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| Multivariate | 
* Regression
* Manova
* Principal components
* Canonical correlation
* Discriminant analysis
* Cluster analysis
* Classification
* Structural equation model
	+ Factor analysis
* Multivariate distributions
	+ Elliptical distributions
		- Normal

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| Time-series | 

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| General | 
* Decomposition
* Trend
* Stationarity
* Seasonal adjustment
* Exponential smoothing
* Cointegration
* Structural break
* Granger causality

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| Specific tests | 
* Dickey–Fuller
* Johansen
* Q-statistic (Ljung–Box)
* Durbin–Watson
* Breusch–Godfrey

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| Time domain | 
* Autocorrelation (ACF)
	+ partial (PACF)
* Cross-correlation (XCF)
* ARMA model
* ARIMA model (Box–Jenkins)
* Autoregressive conditional heteroskedasticity (ARCH)
* Vector autoregression (VAR)

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| Frequency domain | 
* Spectral density estimation
* Fourier analysis
* Wavelet
* Whittle likelihood

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| Survival | 

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| Survival function | 
* Kaplan–Meier estimator (product limit)
* Proportional hazards models
* Accelerated failure time (AFT) model
* First hitting time

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| Hazard function | 
* Nelson–Aalen estimator

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| Test | 
* Log-rank test

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| Applications |
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| Biostatistics | 
* Bioinformatics
* Clinical trials / studies
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* Medical statistics

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| Engineering statistics | 
* Chemometrics
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* Probabilistic design
* Process / quality control
* Reliability
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| Social statistics | 
* Actuarial science
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* National accounts
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| Spatial statistics | 
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