Epidemiology: Definitions and Characteristics

• Epidemiology is reviewed including history, philosophy, and applications.

Key Definitions

• Determinants: Risk factors, either individual or collective, causally related to a health condition or outcome.
  • Examples include biological agents (bacteria, viruses), chemical agents (toxic pesticides, carcinogens), and less specific factors like stress or lifestyle.
• Exposure: Contact with a disease-causing factor, or the amount of such factor affecting a group or individuals.
• Outcome: All possible results stemming from exposure to a causal factor.
• Endemic: A disease or condition regularly occurring within an area or community.
• Outbreak: Sudden increase in disease frequency, in relation to time, place, and population.
• Epidemic: A sudden increase in the number of disease cases or health-related behavior in a specific area.
• Pandemic: Exponential growth rate of cases, where each day sees more cases than the last.

Key Characteristics

• Focus on population health and disease occurrence.
• Distribution: Considers who (person), when (time), and where (place).
  • Disease occurrence varies among populations, with some subgroups more affected.
• Determinants: Addresses why and how (causes and risk factors).
• Outcomes: Considers all possible results from exposure to causal factors.
• Quantification: Involves counting illness cases or health outcomes, using statistics.
  • Statistics are used to describe the occurrence of health outcomes and measure their association with exposures.

Control of Health Problems

• Epidemiology aids in health promotion, alleviation of adverse health outcomes, prevention of disease, and health policy development.

Interdisciplinary Approach

• Epidemiology is interdisciplinary: It involves mathematics and biostatistics, history, sociology, demography, behavioral sciences, and law.

Descriptive vs. Analytical Epidemiology

• Descriptive epidemiology: Describes the amount and distribution of health and disease in a population.
• Analytical epidemiology: Examines causal hypotheses related to exposures.

History of Epidemiology

• Hippocrates: A Greek authority who moved away from superstitious reasons for disease outbreaks.
• Paracelsus: Founder of toxicology, contributed the concept of the dose-response relationship.
• Edward Jenner: Developed a method for smallpox vaccination in 1796.
• John Snow:
  • An English anesthesiologist who developed key epidemiological methods still used today.
  • Considered the father of epidemiology.
  • Believed that cholera was transmitted by contaminated water and demonstrated this association.

Snow's Experiment

• Snow's natural experiment was conducted during cholera epidemics in London in the mid-1800s.
• He observed a severe cholera outbreak concentrated near the Broad Street water pump, formulating a hypothesis.
• He compared cholera death rates in households served by two rival water companies, a "natural experiment".
• Cholera death rate was 8.5 times higher in houses supplied by one company.

Uses of Epidemiology

• Historical Use: Describes a shift in morbidity and mortality patterns from infectious diseases to chronic diseases.
• Community Health Use: Diagnoses community health, measuring ill-health dimensions like incidence, prevalence, disability, and mortality.
• Health Services Uses: Studies health service operations to improve them, including operations research and disease management to reduce healthcare costs.
• Risk Assessment Use: Evaluates the probability of adverse events in a population over time.
  • Risk factor is an exposure associated with a disease or adverse health outcome.
  • Risk assessment: Provides quantitative measurements of risk to health.
• Disease Causality Use: Searches for health and disease causes by computing the experiences of groups.

Data Presentation

• Population is a collection of people who share common observable characteristics.
• Parameter: Variable describing a population characteristic, demonstrated with numbers.
  • Example: Average age of the population.
• Sample: Subgroup from the population.
• Statistics: Numbers describing a sample.
• Representativeness: Matches the sample characteristics with those of the population.

Measurement Scales

• Nominal scale: Quantitative scale with unordered categories (e.g., race, religion, gender).
• Ordinal scale: Categorical data that can be ordered (ranked data) (e.g., educational attainment, self-perception of health)
• Interval scale: Continuous data with equal intervals but no true zero point (e.g., Fahrenheit, IQ).
• Ratio scale: Interval scale with a true zero point (e.g., Kelvin temperature).

Data Charts

• Bar chart: Shows frequency of cases for categories of qualitative or discrete quantitative variables.
• Histograms: Display frequency distributions for grouped categories of continuous variables.
• Line graph: Points are joined by a line, demonstrating comparisons among subgroups.
• Pie chart: Shows proportion of cases according to several categories, demonstrating relative importance of each subcategory.

Variables with Tendency

• Mode: Most frequently occurring number in a dataset.
• Median: Middle point of a ranked dataset.
• Mean: Arithmetic mean or average age.

Variables with Variation

• Range: Difference between highest and lowest values in a data group.
• Midrange: Arithmetic mean of highest and lowest values.
• Variance: Degree of data variability.
• Standard deviation: Square root of the variance.

Bivariate Association

• Pearson correlation coefficient is used with continuous variables.
• Scatter plots are used for graphically displaying relationships between variables.

Epidemiological Measures

• Incidence rate: Rate at which new events (usually new disease cases) occur, or other health outcomes.
  • In 2019, there were 30,635 new HIV diagnoses in the US among people 13+.
• Cumulative incidence: Proportion of a population developing a disease during a time period.
  • 120 out of 25,000 female lawyers had heart attacks during the first year of research.
• Incidence density: Variation of the incidence rate used when time periods of observation vary, and have been estimated directly.
• Attack rate: Incidence proportion used when disease occurrence in an at-risk population increases greatly over a short period.
• Prevalence: Proportion of a population with a condition at a time, expressed as a percentage or per 1,000/100,000.
  • Prevalence = (Number of existing cases / Total population) x 10n
  • If a town has 1,000 people with diabetes in a population of 50,000, the prevalence is 2%.

Factors Influencing Prevalence

• Increased prevalence:
  • Longer disease duration (chronic diseases like diabetes).
  • Improved diagnosis and reporting.
  • Migration of cases into an area.
  • Increased survival without cure.
• Decreased prevalence:
  • Shorter disease duration (fast-recovering diseases).
  • High mortality rates from the disease.
  • Migration of healthy people into an area.
  • Effective prevention and treatment.

Crude, Specific and Adjusted Rates

• Crude Rates: Overall rate of an event in a population without considering factors like age or sex.
  • Calculation: (Total deaths in a year/Total population) x 1,000
  • For instance, a country has 500,000 deaths in a population of 50 million which will equate to 10 deaths per 1,000.
• Specific Rates: Focus on a particular subgroup, such as age or sex.
  • Age Specific Death Rate (ASDR) calculation is (Deaths in a specific age Group/Total population in that age group) X 1,000
    • if 200 deaths occur in the age bracket of 50-60 within a population of 10,000, ASDR would be 20 deaths per 1,000 people.
  • Sex Specific Death Rate calculation is (Deaths in a specific sex/Total population of that sex) X 1,000
    • for instance, if 300 deaths occur in a population of 100,000 males, the sex specific death rate would be 3 deaths per 1,000.
• Adjusted Rates: Accounts for variances in population demographics (i.e age) to draw fair comparisons between groups.
  • Age adjusted mortality rates allow for comparison between two populations with different age distributions, such as an elderly population and a younger one.

Formulae for Epidemiological Rates and Calculations

• Crude Death Rate (CDR) = (Total deaths in a year/Total population) x 1,000
• Crude Birth Rate (CBR) = (Total births in a year/Total population) x 1,000
  • for instance, if a country with a population of 100 million has 1,000,000 births, then CBR would be 10.
• Case Fatality Rate (CFR): Determines the severity of disease via the proportion of diagnosed cases that result in death.
  • Calculation (Number of deaths due to a specific disease/Total diagnosed cases of the disease) X 100
    • If 500 out of 2,000 with a certain disease die, the CFR would be 25%.