Descriptive Epidemiology Lecture

Descriptive Epidemiology

• This lecture is on descriptive epidemiology, covering the topics of place, time, and ecological correlations.
• Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems.

Lecture Objectives

• Identify epidemiological variables (place, time)
• Discuss ecological correlations about aggregate-level data
• Explain the ecological fallacy

Place Variables

• Place variables refer to the locale where people live, work, and visit.
• They include geographic boundaries (street, city, state, region, country).
• Place characteristics may also include environmental factors (rural/urban, domestic/foreign, institutional/noninstitutional).
• Place variables are crucial for understanding the distribution of disease.

International and National Variations

• International: Infectious diseases like polio show international variations in incidence and occurrence.
• Within-country variations: Large countries with in-country variations in disease frequency.
• Environmental factors within a country affect disease prevalence and incidence (climate, latitude, pollution).

Urban-Rural Differences

• Urban and rural areas show variations in morbidity and mortality related to the environment and lifestyle.
• Urban areas have a higher likelihood of diseases spread by person-to-person contact, overcrowding, and inner-city poverty, or linked to urban pollution.
• Agriculture is a primary occupation in rural areas, with farm workers facing hazards like toxic pesticides and injuries from farm equipment.

Localized Patterns of Disease

• Localized disease patterns are specific to environmental conditions in a geographic area.
• Examples include cancer associated with radon gas, arsenic poisoning from water, or the presence of disease vectors.
• A localized dengue outbreak in Texas/Mexico demonstrated a mosquito vector that is specific to the geographic area.

Place Variables: Illustration

• An illustration includes data about age-adjusted breast cancer mortality in different countries between 1958-1959.
• This shows variability between different countries in mortality rates.

Example 1: Victorian Water Pipes

• This demonstrates interconnected water pipes within a city.
• Pipes of different companies were interconnected (not regulated), increasing the risk of contamination from poor hygiene.

Time Variables

• Time variables examine disease occurrences in relation to time since exposure, calendar time, and seasonality.
• Examples are secular trends, cyclic (seasonal) trends, and point epidemics.

Secular Trends

• Secular trends are gradual changes in disease frequency over long time periods.
• Hypertension is an example. While effective treatments exist, about one-third of the U.S. population still has hypertension.
• Comparisons by gender, age, or race/ethnicity showed no remarkable secular change in prevalence in the seven-year period tracked.

Cyclic (Seasonal) Trends

• Cyclic trends involve increases or decreases in disease frequency during a year or several years.
• Pneumonia and influenza mortality peaks in February and decreases in March/April before reaching a lowest level during the early summer.
• Enteroviruses show increasing detection during summer months in recent decades.

Point Epidemics

• A point epidemic indicates a common source of infection, contamination, or exposure affecting a group of people simultaneously.
• An example was the Vibrio outbreak following Hurricane Katrina in 2005.

Epidemic Curves

• Epidemic curves visually display the distribution of illness cases over time.
• They help identify various temporal patterns, including sporadic occurrences, consistent patterns (endemic), sudden outbreaks (point epidemic), and spreading outbreaks (propagating).

Ecological Studies

• Epidemiological studies based on aggregate-level variables are known as ecological studies.
• These data are often incomplete as they don't show the detailed factors contributing to disease occurrence.
• Ecological studies tend towards descriptive analyses due to this data limitation

Ecological Fallacy

• The ecological fallacy (aggregation bias) assumes that an association found at a group (aggregate) level is also true for individual cases.
• This can be viewed as a form of confounding.

Illustrative Example: Dietary Fat and Cardiovascular Disease (Ecological Correlation)

• This example demonstrates an ecological correlation between dietary fat intake and cardiovascular deaths in several countries.
• The graph displays data for cardiovascular deaths per 1000, in relation to percentage of calories from fat, by different countries.

Illustrative Example: William Farr's Study on Cholera and Geographic Altitude

• William Farr's 1852 study examined cholera and geographic altitude in 19th-century London.
• While he studied higher correlation between cholera and altitude as a factor, he failed to note several other contributing factors.
• He misinterpreted the data.

This lecture on descriptive epidemiology focuses on key concepts such as place, time, and ecological correlations. It aims to equip students with the ability to identify epidemiological variables and understand the implications of ecological correlations in health data analysis.