🦠Epidemiology Unit 11 – Epidemiologic Data Analysis

Key Concepts and Terminology

• Epidemiology studies the distribution and determinants of health-related states or events in specified populations
• Incidence refers to the occurrence of new cases of a disease or condition over a specified period
• Prevalence measures the proportion of a population affected by a disease or condition at a given point in time
• Risk factors are characteristics, behaviors, or exposures that increase the likelihood of developing a disease
  • Can be modifiable (smoking) or non-modifiable (age, gender)
• Causal inference establishes a cause-and-effect relationship between an exposure and an outcome
• Validity assesses the extent to which a study measures what it intends to measure
  • Internal validity refers to the accuracy of the study results within the study population
  • External validity refers to the generalizability of the study results to other populations
• Reliability measures the consistency of results when a study is repeated under similar conditions

Data Collection Methods

• Surveys gather information from a sample of individuals through questionnaires or interviews
  • Can be conducted in-person, by telephone, or online
• Medical records provide detailed information on patient health history, diagnoses, and treatments
• Registries systematically collect data on specific diseases or conditions (cancer registries)
• Surveillance systems continuously monitor and collect data on health events (infectious disease surveillance)
• Biological samples (blood, urine) can be collected for laboratory analysis
• Environmental monitoring assesses exposure to potential risk factors (air pollution, water quality)
• Wearable devices and mobile apps enable real-time data collection on health behaviors and outcomes

Study Designs in Epidemiology

• Observational studies observe and analyze relationships between exposures and outcomes without intervention
  • Cohort studies follow a group of individuals over time to assess the incidence of a disease or condition
  • Case-control studies compare individuals with a disease (cases) to those without the disease (controls) to identify potential risk factors
  • Cross-sectional studies assess the prevalence of a disease and associated risk factors at a single point in time
• Experimental studies involve the manipulation of an exposure to assess its effect on an outcome
  • Randomized controlled trials randomly assign participants to intervention and control groups to evaluate the efficacy of a treatment or intervention
• Ecological studies compare disease rates and exposures at the population level (geographical regions)
• Longitudinal studies collect data from the same individuals repeatedly over an extended period
• Retrospective studies look back in time to examine the relationship between an exposure and an outcome

Measures of Disease Frequency

• Incidence rate measures the number of new cases of a disease per population at risk over a specified period
  • Calculated as: (Number of new cases) / (Population at risk × Time period)
• Cumulative incidence measures the proportion of a population that develops a disease over a specified period
  • Calculated as: (Number of new cases) / (Total population at risk)
• Prevalence measures the proportion of a population affected by a disease at a given point in time
  • Point prevalence: (Number of cases at a specific point in time) / (Total population at that point in time)
  • Period prevalence: (Number of cases during a specified period) / (Average population during that period)
• Attack rate measures the proportion of an exposed group that develops a disease over a specified period
• Case fatality rate measures the proportion of individuals with a disease who die from the disease

Descriptive Statistics and Data Visualization

• Measures of central tendency summarize the center or typical value of a dataset
  • Mean: the arithmetic average of a set of values
  • Median: the middle value when a dataset is ordered from lowest to highest
  • Mode: the most frequently occurring value in a dataset
• Measures of dispersion describe the spread or variability of a dataset
  • Range: the difference between the maximum and minimum values
  • Variance: the average squared deviation from the mean
  • Standard deviation: the square root of the variance
• Frequency distributions organize and summarize data by counting the occurrences of each value or group of values
• Histograms display the frequency distribution of a continuous variable using adjacent rectangular bars
• Bar charts compare the frequencies or proportions of categorical variables using parallel bars
• Pie charts represent the proportions of categorical variables as slices of a circular graph
• Scatter plots display the relationship between two continuous variables using points on a coordinate plane

Inferential Statistics and Hypothesis Testing

• Hypothesis testing evaluates the strength of evidence against a null hypothesis in favor of an alternative hypothesis
  • Null hypothesis ($H_0$) assumes no association between the exposure and outcome
  • Alternative hypothesis ($H_A$) proposes a significant association between the exposure and outcome
• P-value measures the probability of observing results as extreme as or more extreme than the current results, assuming the null hypothesis is true
  • A small P-value (typically < 0.05) suggests strong evidence against the null hypothesis
• Confidence intervals estimate the range of plausible values for a population parameter based on the sample data
  • Commonly reported as 95% confidence intervals, which have a 95% probability of containing the true population parameter
• Type I error (false positive) occurs when the null hypothesis is rejected when it is actually true
• Type II error (false negative) occurs when the null hypothesis is not rejected when it is actually false
• Statistical power is the probability of correctly rejecting a false null hypothesis
  • Influenced by sample size, effect size, and significance level

Confounding and Bias

• Confounding occurs when a third variable is associated with both the exposure and the outcome, distorting their relationship
  • Can be addressed through study design (randomization, matching) or statistical analysis (stratification, regression)
• Selection bias arises when the study participants are not representative of the target population
  • Can occur due to non-random sampling, loss to follow-up, or self-selection
• Information bias results from inaccurate or incomplete data collection
  • Recall bias: participants inaccurately remember past exposures or events
  • Measurement bias: systematic errors in measuring exposures or outcomes
• Publication bias occurs when studies with statistically significant results are more likely to be published than those with non-significant results
• Hawthorne effect: participants alter their behavior due to awareness of being observed
• Berkson's bias: hospital-based studies may overestimate associations due to the selection of more severe cases

Interpreting and Reporting Results

• Assess the strength and direction of associations using effect measures (relative risk, odds ratio)
  • Relative risk (RR) compares the risk of an outcome between exposed and unexposed groups
  • Odds ratio (OR) compares the odds of an outcome between exposed and unexposed groups
• Evaluate the precision of effect estimates using confidence intervals
• Consider the clinical or public health significance of the results, not just statistical significance
• Discuss the limitations and potential sources of bias in the study
• Place the findings in the context of existing knowledge and suggest implications for practice or future research
• Use clear and concise language to communicate the results to the target audience
• Follow reporting guidelines (STROBE, CONSORT) to ensure transparency and completeness
• Interpret the results cautiously, avoiding overstatement or causal claims when not warranted by the study design