🦠Epidemiology Unit 1 – Introduction to Epidemiology

Key Concepts and Definitions

• Epidemiology studies the distribution and determinants of health-related states or events in specified populations and applies this knowledge to control health problems
• Incidence rate measures the occurrence of new cases of a disease or condition in a population over a specified period of time
  • Calculated as the number of new cases divided by the population at risk during a given time period
• Prevalence measures the proportion of a population that has a disease or condition at a specific point in time
  • Includes both new and existing cases
• Odds ratio (OR) compares the odds of an event occurring in one group to the odds of it occurring in another group
  • Often used in case-control studies to estimate the strength of an association between an exposure and an outcome
• Relative risk (RR) measures the probability of an event occurring in an exposed group compared to the probability of the event occurring in a non-exposed group
  • Commonly used in cohort studies and clinical trials
• Attributable risk (AR) estimates the proportion of disease cases in a population that can be attributed to a specific exposure
• Confounding occurs when a third variable is associated with both the exposure and the outcome, potentially distorting the true relationship between them
• Effect modification happens when the association between an exposure and an outcome varies depending on the level of a third variable

Historical Context and Development

• John Snow's investigation of the 1854 London cholera outbreak demonstrated the importance of epidemiological methods in identifying the source of disease (contaminated water from the Broad Street pump)
• The Framingham Heart Study, initiated in 1948, has been crucial in identifying major risk factors for cardiovascular disease (smoking, high blood pressure, and high cholesterol)
• The establishment of the Epidemic Intelligence Service (EIS) by the Centers for Disease Control and Prevention (CDC) in 1951 has played a significant role in investigating disease outbreaks and training epidemiologists
• The development of modern epidemiological methods, such as the randomized controlled trial (RCT), has revolutionized the evaluation of medical interventions and public health strategies
• The emergence of new infectious diseases (HIV/AIDS, SARS, and COVID-19) has highlighted the ongoing importance of epidemiology in detecting, monitoring, and controlling disease outbreaks
• Advances in technology, such as electronic health records and big data analytics, have expanded the scope and potential of epidemiological research

Measures of Disease Frequency

• Incidence proportion (cumulative incidence) measures the proportion of a population at risk that develops a disease over a specified period of time
  • Calculated as the number of new cases divided by the total population at risk
• Incidence rate (person-time incidence rate) measures the occurrence of new cases of a disease per unit of person-time at risk
  • Allows for varying follow-up times among individuals in a study
• Prevalence can be further classified as point prevalence (at a specific point in time) or period prevalence (over a specified period of time)
• Mortality rate measures the frequency of deaths in a defined population over a specified period of time
  • Can be cause-specific (e.g., cancer mortality rate) or all-cause (overall mortality rate)
• Years of potential life lost (YPLL) quantifies the impact of premature mortality by calculating the total number of years lost due to deaths occurring before a specified age (often 75 years)
• Disability-adjusted life years (DALYs) combine the impact of premature mortality and disability to measure the overall burden of disease in a population
• Age-standardized rates allow for comparisons of disease frequencies across populations with different age structures by adjusting for the confounding effect of age

Study Designs in Epidemiology

• Observational studies, such as cohort and case-control studies, are used to investigate associations between exposures and outcomes without the researcher intervening
  • Cohort studies follow a group of individuals over time to compare disease incidence between exposed and unexposed groups
  • Case-control studies compare the exposure history of individuals with a disease (cases) to those without the disease (controls)
• Experimental studies, such as randomized controlled trials (RCTs), involve the researcher assigning participants to different intervention groups to assess the efficacy or effectiveness of a treatment or preventive measure
• Cross-sectional studies measure the prevalence of a disease and the prevalence of an exposure in a population at a single point in time
  • Useful for generating hypotheses but cannot establish temporal relationships between exposures and outcomes
• Ecological studies compare disease frequencies and exposures at the population level rather than the individual level
  • Prone to ecological fallacy, where associations observed at the group level may not hold true at the individual level
• Hybrid designs, such as case-cohort and nested case-control studies, combine elements of different study designs to improve efficiency and reduce costs

Data Collection and Analysis Methods

• Questionnaires and interviews are commonly used to collect information on exposures, outcomes, and potential confounding factors
  • Standardized questionnaires ensure consistent data collection across participants
  • Validation studies can assess the accuracy and reliability of self-reported data
• Medical records, such as electronic health records (EHRs), provide valuable information on diagnoses, treatments, and outcomes
  • Data quality and completeness may vary across different healthcare settings
• Biomarkers, such as blood tests or genetic markers, can provide objective measures of exposures or disease states
  • Must be validated for their sensitivity, specificity, and predictive value
• Data management involves organizing, cleaning, and preparing data for analysis
  • Data cleaning identifies and corrects errors, inconsistencies, and missing values
• Statistical analysis methods, such as regression models, are used to estimate the strength and direction of associations between exposures and outcomes
  • Confounding can be addressed through stratification, matching, or multivariable regression
• Sensitivity analyses assess the robustness of study findings to changes in assumptions or analytical approaches

Causality and Risk Factors

• The Bradford Hill criteria provide a framework for assessing the strength of evidence for a causal relationship between an exposure and an outcome
  • Criteria include strength of association, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment, and analogy
• Sufficient cause model posits that a disease occurs when a sufficient cause (a combination of component causes) is present
  • Component causes may be necessary (always present in a sufficient cause) or unnecessary (interchangeable with other component causes)
• Counterfactual model compares the potential outcomes of an individual under different exposure scenarios
  • Used to define causal effects and estimate measures such as the average treatment effect (ATE)
• Directed acyclic graphs (DAGs) visually represent the causal relationships between variables and can guide the selection of confounders for adjustment
• Gene-environment interactions occur when the effect of an environmental exposure on a health outcome varies depending on an individual's genetic background
  • Can help identify high-risk subgroups and tailor preventive interventions
• Lifecourse epidemiology examines how exposures and experiences throughout life, from conception to adulthood, influence the development of chronic diseases
  • Critical periods and accumulation of risk are key concepts in this approach

Public Health Applications

• Disease surveillance systems monitor the occurrence and distribution of diseases in a population to detect outbreaks and guide public health actions
  • Passive surveillance relies on healthcare providers and laboratories to report cases
  • Active surveillance involves public health officials actively seeking out cases
• Screening programs aim to identify individuals with asymptomatic or early-stage disease to enable early intervention and improve outcomes
  • Criteria for effective screening include high prevalence, accurate and acceptable tests, and effective treatments
• Outbreak investigations use epidemiological methods to identify the source and mode of transmission of a disease outbreak and implement control measures
  • Steps include establishing a case definition, identifying and interviewing cases, generating hypotheses, and implementing control measures
• Health impact assessments (HIAs) evaluate the potential health effects of a proposed policy, program, or project on a population
  • Can inform decision-making and promote health equity
• Epidemiology informs the development and evaluation of public health interventions, such as vaccination programs, health education campaigns, and environmental regulations
  • Randomized controlled trials and quasi-experimental designs can assess the effectiveness of interventions

Current Challenges and Future Directions

• The increasing availability of big data, such as electronic health records and social media data, presents opportunities and challenges for epidemiological research
  • Requires advanced analytical methods and data management strategies
  • Raises concerns about data privacy and security
• The COVID-19 pandemic has highlighted the importance of rapid data sharing and collaboration among epidemiologists worldwide
  • Preprint servers and open data initiatives have accelerated the dissemination of research findings
• Climate change and environmental degradation pose significant threats to human health, requiring epidemiologists to study the health impacts of these global challenges
  • Research on the health effects of air pollution, extreme weather events, and changes in vector ecology is increasingly important
• Health disparities and social determinants of health are major focus areas for epidemiological research
  • Investigating the root causes of health inequities and developing interventions to address them is crucial for promoting health equity
• Advances in genomics and precision medicine are transforming epidemiology by enabling the study of individual-level risk factors and tailored interventions
  • Integrating genomic data with traditional epidemiological methods can provide new insights into disease etiology and prevention
• The growing recognition of the interconnectedness of human, animal, and environmental health has led to the emergence of the One Health approach
  • Epidemiologists collaborate with professionals from diverse fields to address complex health challenges at the human-animal-environment interface