Chapter 2: Glossary

Question 1

v Epidemiology

Answer 1

Epidemiology is the study of cases, distribution, and control of diseases and other health-related factors in human populations. It involves analyzing patterns of health and illness in populations to understand the causes and effects of diseases. Epidemiology helps in identifying risk factors, developing prevention strategies, and improving public health outcomes.

Answer 2

Climate change refers to long-term changes in temperature, precipitation, and other atmospheric conditions on Earth. It is primarily driven by human activities such as burning fossil fuels, deforestation, and industrial processes. Climate change has wide-ranging impacts on ecosystems, weather patterns, and human health, leading to increased risks of extreme weather events, rising sea levels, and shifts in disease

Answer 3

v Vector-borne disease
Vector-borne diseases are illnesses transmitted to humans through vectors like mosquitoes, ticks, and fleas. These vectors carry pathogens that can cause diseases such as malaria, dengue fever, and Zika virus. Climate change can influence the distribution and prevalence of vector-borne diseases by altering the habitats and behaviors of these disease-carrying vectors.

Answer 4

Adaptive capacity
Adaptive capacity refers to the ability of individuals, communities, or systems to adjust to changing conditions and withstand the impacts of environmental or social stressors. In the context of climate change, adaptive capacity includes the resources, knowledge, and infrastructure needed to cope with and respond to climate-related challenges.

Answer 5

v Data mining
Data mining is the process of extracting and discovering patterns in large datasets using computational techniques. It involves analyzing data from various sources to uncover hidden insights, trends, and relationships. In the context of public health and epidemiology, data mining can help researchers identify correlations between environmental factors, disease outcomes, and population health.

Answer 6

> Data mining
• v Global burden of disease
• The Global Burden of Disease study is a comprehensive assessment of mortality and morbidity from major diseases, injuries, and risk factors worldwide. It provides insights into the health challenges faced by different populations, helping policymakers prioritize interventions and allocate resources effectively to reduce the burden of diseases.

Answer 7

Per capita refers to the average per person or per unit of population. In epidemiology, per capita measurements are used to standardize disease rates and compare health outcomes across different populations. It allows for a fair comparison of disease incidence or prevalence by accounting for differences in population size.

Answer 8

v Susceptibility
Susceptibility in epidemiology refers to the likelihood of an individual, community, or population to be affected by a particular disease or health condition. Susceptibility is influenced by biological factors, genetics, lifestyle choices, and environmental exposures. Understanding susceptibility helps in identifying vulnerable groups and designing targeted interventions.

Answer 9

v Descriptive epidemiology
Descriptive epidemiology focuses on characterizing the distribution of diseases within a population. It involves analyzing patterns of disease occurrence by time, place, and person to identify trends and risk factors. Descriptive epidemiology provides essential data for public health planning and surveillance.

Answer 10

v Analytic epidemiology
Analytic epidemiology examines the relationships between exposures (risk factors) and health outcomes (diseases) to determine causal associations. It involves conducting studies such as case-control and cohort studies to investigate the impact of specific factors on disease occurrence. Analytic epidemiology helps in identifying preventive strategies and interventions.

Question 11

v Sample population

Answer 11

A sample population refers to the individuals included in a study, characterized by factors such as age, sex, gender, race, ethnicity, income level, and geographical location. The results obtained from studying a sample population can be extrapolated to similar populations.

Question 12

Extrapolation

Answer 12

Extrapolation involves extending findings from one sample population to another, which can be challenging.
Understanding the underlying factors driving a particular condition or disease in a specific area is crucial before making extrapolations to ensure the validity of the results.

Question 13

v Disease measurement

Answer 13

In studies focusing on diseases, it is essential to consider how the disease is being measured. Factors such as exposure to certain elements like water quality for waterborne diseases or air quality for airborne diseases play a significant ole in understanding and measuring the disease.

Question 14

Association analysis

Answer 14

aims to find relationships between variables, such as disease (D) and exposure (E). It is important to differentiate between correlation and causation, as well as to determine the statistical significance of any associations found.

Question 15

v Odds ratio

Answer 15

The odds ratio is a measure used to determine the strength and direction of the association between exposure and disease. A value greater than one indicates a positive association, while a value less than one indicates a negative association.

Question 16

A confidence interval

Answer 16

is a range of values that likely contains the true population parameter with a certain probability. It helps assess the reliability of the observed association between variables and indicates the likelihood that the association is not due to chance.

Question 17

• › Confidence interval

Answer 17

Correlation vs. causation
Understanding the differenoe between correlation and causation is crucial in research. Correlation indicates a relationship between variables, while causation implies that one variable directly influences the other.

Question 18

› Statistical significance

Answer 18

Statistical significance refers to the probability that the observed association between variables is real and not due to chance. A commonly used threshold is a 95% confidence level, but it does not guarantee a causal relationship.

Question 19

v Climate change impacts

Answer 19

Studying the impacts of climate change requires the use of various tools, including future model simulations, socioeconomic pathways, exposure-response functions, and vulnerability assessments. Understanding future health impacts involves considering emission scenarios and potential adaptation strategies.

Question 20

Examples of health studies

Answer 20

discussed include the relationship between bed nets and malaria prevalence, the association between smoking and dementia diagnosis, and the impact of heavy trucks on asthma cases. These studies highlight the importance of careful study design and interpretation of results.

Question 21

Sample population

Answer 21

A sample population refers to the group of individuals included in a study from which data is collected and analyzed.
This group represents a larger population and is used to draw conclusions and make inferences about the characteristics or behaviors of the broader population. Sample populations are essential in research to gather insights and generalize findings to a larger context.

Question 22

Extrapolate

Answer 22

v
Extrapolate means to infer or estimate something by extending or projecting known information or data. It involves making predictions or generalizations based on existing trends or patterns. Extrapolation is commonly used in research to apply findings from a specific sample or context to a broader population or different scenarios.

Question 23

Correlation

Answer 23

is a statistical measure that describes the degree to which two variables change together. It indicates the strength and direction of a relationship between variables. A positive correlation means that as one variable increases, the other also increases, while a negative correlation implies that as one variable increases, the other decreases.
Correlation does not imply causation, meaning that just because two variables are correlated, it does not necessarily mean that one causes the other.

Question 24

Association

Answer 24

in research refers to a relationship between two or more variables or factors. It indicates that changes in one variable are related to changes in another. Establishing an association is crucial in understanding patterns and connections between different elements in a study. Associations can be positive, negative, or neutral, depending on the direction and strength of the relationship observed.

Question 25

> Prevalence

Answer 25

is a measure used in epidemiology to describe the proportion of individuals in a population who have a specific characteristic or condition at a particular point in time. It is often expressed as a percentage and helps in understanding the frequency or extent of a particular trait or disease within a given population.

Question 26

A confidence interval

Answer 26

is a range of values that is used to estimate the true value of a population parameter with a certain level of confidence. It provides a measure of the uncertainty or precision of an estimate derived from sample data. A 95% confidence interval, for example, implies that if the study were repeated multiple times, 95% of the intervals constructed would contain the true population parameter.

Question 27

The p-value

Answer 27

is a statistical measure that helps researchers determine the significance of their results. It indicates the probability of obtaining results as extreme as the observed data, assuming that the null hypothesis is true. A p-value less than a predetermined significance level (often 0.05) suggests that the results are statistically significant and that the null hypothesis can be rejected.

Question 28

Causation

Answer 28

refers to the relationship between cause and effect, where one event (the cause) leads to another event (the effect). Establishing causation requires demonstrating that changes in one variable directly result in changes in another variable. While correlation can indicate a relationship between variables, causation implies a direct influence or causal link between them.

Question 29

A cross-sectional

Answer 29

study is a type of observational research that analyzes data collected from a population or a sample at a specific point in time. It provides a snapshot of the characteristics or conditions of the participants at that moment.
Cross-sectional studies are useful for examining prevalence, associations between variables, and identifying patterns within a population.