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Questions and Answers
What was Durkheim attempting to operationalize in his inquiry about death?
What was Durkheim attempting to operationalize in his inquiry about death?
Which of the following resources should be consulted to gain an understanding of the early history of epidemiology?
Which of the following resources should be consulted to gain an understanding of the early history of epidemiology?
What is a key difference in epidemiological research that should be understood according to the outcomes outlined?
What is a key difference in epidemiological research that should be understood according to the outcomes outlined?
Which chapter of 'Epidemiology for the Uninitiated' is essential for understanding the basic principles of epidemiology?
Which chapter of 'Epidemiology for the Uninitiated' is essential for understanding the basic principles of epidemiology?
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In preparing notes on psychiatric epidemiology, which topic is most relevant to consider?
In preparing notes on psychiatric epidemiology, which topic is most relevant to consider?
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What is one of the primary objectives of an epidemiological study?
What is one of the primary objectives of an epidemiological study?
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Which line of investigation addresses the reliability of measured variables in a study?
Which line of investigation addresses the reliability of measured variables in a study?
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When assessing associations for causality, which factor involves the consideration of external influences affecting both exposure and outcome?
When assessing associations for causality, which factor involves the consideration of external influences affecting both exposure and outcome?
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What is an essential step in investigating suspected risk factors for illness?
What is an essential step in investigating suspected risk factors for illness?
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What type of study focuses on understanding the prevalence of diseases to inform healthcare policy?
What type of study focuses on understanding the prevalence of diseases to inform healthcare policy?
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What does a risk ratio of 2.35 indicate?
What does a risk ratio of 2.35 indicate?
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Which outcome measure is used to validate symptoms of depression in the given hypothesis?
Which outcome measure is used to validate symptoms of depression in the given hypothesis?
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What does an odds ratio of 3.56 suggest about individuals with exposure compared to those without?
What does an odds ratio of 3.56 suggest about individuals with exposure compared to those without?
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Which analysis step is essential for understanding if an association is causal?
Which analysis step is essential for understanding if an association is causal?
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If the risk is calculated as 0.3, what does this indicate regarding exercise and depression?
If the risk is calculated as 0.3, what does this indicate regarding exercise and depression?
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What is the primary purpose of using a cross-sectional survey in this type of study?
What is the primary purpose of using a cross-sectional survey in this type of study?
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What does an interpretation below one indicate in terms of risk?
What does an interpretation below one indicate in terms of risk?
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Which of the following concepts refers to incorrectly assuming a cause-and-effect relationship?
Which of the following concepts refers to incorrectly assuming a cause-and-effect relationship?
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What is the primary focus of epidemiology?
What is the primary focus of epidemiology?
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What does descriptive epidemiology primarily measure?
What does descriptive epidemiology primarily measure?
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What is the definition of prevalence in an epidemiological context?
What is the definition of prevalence in an epidemiological context?
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Which of the following aspects is NOT a key line of investigation when critiquing a research study?
Which of the following aspects is NOT a key line of investigation when critiquing a research study?
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How is the frequency of depression calculated in a population of 20 individuals where 4 are affected?
How is the frequency of depression calculated in a population of 20 individuals where 4 are affected?
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What is the primary purpose of analytical epidemiology?
What is the primary purpose of analytical epidemiology?
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What is meant by 'distribution' in the context of epidemiology?
What is meant by 'distribution' in the context of epidemiology?
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In the context given, how many people would be considered at risk for depression in the population in 2024?
In the context given, how many people would be considered at risk for depression in the population in 2024?
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What is a common example of selection bias in studies?
What is a common example of selection bias in studies?
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Which type of bias involves an observer influencing the outcome based on their beliefs?
Which type of bias involves an observer influencing the outcome based on their beliefs?
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Recall bias occurs when:
Recall bias occurs when:
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What defines a confounder in a study?
What defines a confounder in a study?
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Which method can help deal with confounding in studies?
Which method can help deal with confounding in studies?
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What is social desirability bias?
What is social desirability bias?
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How might selection bias specifically affect case-control studies?
How might selection bias specifically affect case-control studies?
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What type of bias is characterized by people recalling past events more negatively if they have depression?
What type of bias is characterized by people recalling past events more negatively if they have depression?
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What is the primary method for addressing confounding in a study?
What is the primary method for addressing confounding in a study?
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Which statement best describes residual confounding?
Which statement best describes residual confounding?
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What is a potential consequence of measurement bias in a study?
What is a potential consequence of measurement bias in a study?
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How can reverse causality be effectively addressed in research?
How can reverse causality be effectively addressed in research?
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What characterizes confounding in a study?
What characterizes confounding in a study?
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When assessing confounding factors, which social aspect is particularly significant?
When assessing confounding factors, which social aspect is particularly significant?
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Why is randomisation often not feasible in studies?
Why is randomisation often not feasible in studies?
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What can be inferred about confounding after adjustment using multivariable methods?
What can be inferred about confounding after adjustment using multivariable methods?
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Study Notes
Session 5: 11th Oct - pt1
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Introduction to Epidemiology:
- Led by Maddie Davies-Kellock
- Covers major study designs and their strengths/weaknesses
- Explains how to critique research studies (chance, bias, confounding)
- Includes reading list: PSBS0002: Core Principles of Mental Health Research, University College London
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Preliminary Slides:
- What is Epidemiology?: The study of the distribution and determinants of disease frequency in human populations (MacMahon & Pugh, adapted by Hennekens & Buring)
- Includes studying the distribution & determinants of health-related states or events in populations to control health problems.
- Disease in Human Population: Focuses on the study of the distribution and determinants of disease frequency.
Epidemiology - Page 2
- Frequency: Quantification of disease existence/occurrence (incidence/prevalence), rate/risk.
- Determinants: Identifying risk factors scientifically
- Distribution: Who gets the disease? Comparisons between groups or over time
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What is the frequency of depression in the population?:
- Total population: 20
- Prevalence of depression in 2023: 4/20 = 20%
- People at risk of depression in 2024: 20 - 4 = 16
- Incidence of depression in 2024: 2 / 16 = 12.5%
- Why do we need epidemiology?: Alleviate suffering, plan health services, identify high-risk groups, improve health & well-being, identify causes, prevention.
- History of Epidemiology: Hippocrates as the father of modern medicine, John Snow's cholera outbreak research.
- Psychiatric Epidemiology: Emile Durkheim, 1858-1917, French sociologist.
Epidemiology - Page 3
- History of Epidemiology: Hippocrates, John Snow (cholera outbreaks). Populations, statistics, disease surveillance
- Frequency: Death rates from cholera (1853-54)
- Distribution: Data from London water companies linked to cholera deaths (1853-54)
Epidemiology - Page 4
- Preliminary Slides
- Introduces the topic of epidemiology
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Prep Notes:
- Readings on Psychoses, Ethnicity, and Socio-Economic Status
- Readings on Airs, Waters, Places and the environment.
- Readings on the Environment and Disease: Association or Causation?
- Outcomes: Be familiar with the history of epidemiology, core principles, critique research methods (chance, bias, confounding, reverse causality), discuss whether associations are causal
- Seminal Monograph "Suicide" (1897): Durkheim attempted to operationalize an outcome, in particular looking at suicide rates related to social control (differing between Catholics and Protestants).
Epidemiology - Page 5
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Investigating Causes of Illness:
- Frequency: How often a disease occurs
- Distribution: Where and when a disease occurs (populations)
- Determinants: Factors that cause or are related to a disease.
- Hypothesis Example: Physical activity reduces the likelihood of depression
- Outcome: Depression diagnosis/validated measure
- Exposure: Physical activity (self-reported, data from technolgy)
- Study design: Cross-sectional survey
Epidemiology - Page 6
- Risk Ratio/Odds Ratio Calculation: Data is presented in tables/graph. Formulas for calculation explained
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Epidemiological Reasoning:
- Identify the question systematically collect data from individuals/population, systemically analyze data, assess threats to validity in a systematic way, causal inference interpretation.
Epidemiology - Page 7
- Chance, Bias, Confounding, Reverse Causality
- Association does not mean causation
- Chance: Statistical possibilities of sampling errors, p-values, confidence intervals.
- Bias: Selection bias or measurement bias. Systematic errors due to sampling techniques/data collection procedures and measuring variables/intertidal. Observer bias, recall bias (in particular how different memories can bias your interpretation). Social desirability bias is also mentioned, being how people self-report/report the data.
- Confounding: Alternative explanations for the observed association/relationship. Confounding variable has relationship with both exposure and outcome. Need to consider both to fully understand the relationship of two variable.
- Reverse Causality: Outcome may cause the exposure. Need to evaluate the possibility of this in case-control studies. (not important in cross-sectional studies)
- Sampling/Population/Statistics: Relationship between sample and population. Sample represents the larger population. Statistical formulas/methods used to determine.
- Why does chance matter?: Everyone cannot be tested in a study, need samples, quantification of sampling errors
- What is the population we want to infer values/estimates?: Involves considering the value from the sample related to the population as a whole.
Epidemiology - Page 8
- Statistical Inference: p-values and confidence intervals help understand sampling variation (random errors); Type 1 error – false positive; Type 2 error - false negative
- Confidence Intervals (CI): Give a range of values likely to contain the true population value – wide (low power) vs narrow (high power) CI
- Using Confidence Intervals (CI) and p-values to understand uncertainty; sampling, how to interpret p-values, error interpretation.
- Outcome/Exposure/Risk Ratio Example provided.
Epidemiology - Page 9
- More on 95% Confidence Intervals; interpretation of confidence intervals: width of CI; relation to errors; importance of sample size. The relationship of clinical significance and statistical significance
- Statistical significance: p<0.05 or <0.01
- Clinical significance: Practical implications/importance in practice
- Wider confidence intervals indicate lower certainty due to small sample sizes; smaller confidence intervals indicate higher certainty
- Bias: Systematic errors in study design/conduct potentially bias results; different types of bias in study
- Introducing bias: Systemic errors during the design and conduct of an observational study introduce errors; importance of recognizing the likely sources of systematic errors so that valid conclusions can be drawn.
Epidemiology - Page 10
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Bias:
- Selection bias: Procedures used to select participants in a study.
- Measurement bias (information bias): Errors arising from the measurement of exposure, outcome, or other factors.
- Observer bias (a type of measurement bias): Errors arising from observer expectations.
- Recall bias: Errors arising from people's memories of past events.
- Social desirability bias: Errors arising from participants' desire to present themselves in a positive light.
Epidemiology - Page 11
- Confounding: Alternative explanation for the association between an exposure and an outcome.
- A confounder is a "third" variable related to both the exposure and outcome. It can distort the observed relationship between exposure and outcome/confounder.
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Methods for Dealing with Confounding:
- Randomisation: The best method, if possible
- Adjusting using multivariable methods.
- Residual confounding.
- Bias vs Confounding: Confounding occurs even with ideally designed studies; bias introduced by investigators
- Reverse Causality: Exposure causes the outcome; alternative explanation. Example: (physical activity reduces risk of depression)
Epidemiology - Page 12
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Dealing with Chance, Bias, Confounding:
- Chance: How significant is the chance, addressed in design stage
- Bias: Methods to address/minimise at various study phases
- Confounding: Address at the design stage
- General Methods for Addressing Chance, Bias, and Confounding:
- Sample Size: Larger increases confidence about results.
- Random sampling: Helps avoid bias, ensures representative sample.
- Restriction: Restricts participants to specific subgroups (e.g., age, gender).
- Matching: Matches participants in different groups on potentially confounding characteristics.
- Adjustment: Statistical techniques to account for confounders, statistical modeling (regression).
- You cannot/should not adjust for biases after study is conducted because biases/errors are systematic, and therefore should be controlled/avoided during the study to make it as valid as possible.
Epidemiology - Page 13
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What is a cause (Bradford-Hill):
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If taken away, reduces disease incidence; multiple causes (multifactorial).
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Models of Causation: Elements of causality.
- Neither necessary nor sufficient
- Disease can occur without the cause
- People without the cause can get the disease
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Bradford Hill Considerations: Evaluating if something is a cause of disease (temporality, strength, dose-response, consistency, specificity, coherence, plausibility, analogy).
Epidemiology - Page 14
- Experimental Evidence: Supports or refutes the hypothesis of causation.
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Summary:
- Epidemiology : Science of population health
- Non-causal explanations for population health associations (chance, selection bias, measurement bias, confounding, reverse causality)
- Strategies for proving causation.
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Test your knowledge on fundamental concepts in epidemiology with this quiz. Explore important questions related to historical figures, research methodologies, and key principles in the field. Perfect for students of public health and aspiring epidemiologists.
