Descriptive Epidemiology According to Person, Place, and Time Chapter 5 Descriptive Epidemiology According to Person, Place, and Time

Descriptive Epidemiology Allows us to Describe the extent of a public health problem according to person, place, and time Communicate a public health problem using tables and graphs Identify who is at greatest risk for selected health-related states or events Use surveillance methods to monitor whether unusual health related states or events exist and evaluate public health intervention programs Understand how descriptive epidemiology can provide clues as to the causes of disease

Person Descriptive data on person answers the “who” question Descriptors often include Age Sex Race/ethnicity Marital and family status Occupation Education

Place Descriptive data by place addresses the “where” question These data often involve comparisons between or among geographic regions, in groups before and after migration, and between twins raised in different settings

Time Epidemiologic investigations range from hours to weeks, years to decades Short-term disease incubation periods of a few hours can be as important to the epidemiologist as long-term latency periods for chronic diseases spanning decades Another term used occasionally to describe time factors in epidemiology is temporal, which means time or refers to time-related elements or issues

Communicating health data and identifying who is at greatest risk Tables and graphs summarize and describe data in a way that can be easily interpreted

Example

Example

Dependency Ratio Population <15 and 65+ divided by the population 15-64 Multiply by 100 to express as % Interpretation – “There are ___ dependents for every 100 people of working age.”

Dependency Ratio in the United States 2000 – 51% There are 51 dependent people for every 100 people of working age 2025 – 61% There are 61 dependent people for every 100 people of working age 2050 – 67% There are 67 dependent people for every 100 people of working age

Types of Trends Secular trends – represent long-term changes in health-related states or events Short-term trends – usually brief, unexpected increases in health-related states or events Cyclic trends – represent periodic increases and decreases in the occurrence of health-related states or events

Example of a Secular Trend

Surveillance Medical Public health

Public Health Surveillance Implicit in descriptive epidemiology is the notion of public health surveillance What is public health surveillance? Systematic ongoing collection, analysis, interpretation, and dissemination of health data

Why Surveillance Data? Monitor As a result, we identify whether Identify sudden changes in occurrence Follow long-term trends and patterns Identify changes in risk factors As a result, we identify whether A health problem exists The problem is getting worse

Explanations Some explanations for observed changes in the frequency and pattern of cases in a surveillance system, which are not due to changes in risk exposures, include Inconsistent interpretation and application of the case definition Change in the case definition Change in surveillance system/policy of reporting Improved diagnosis (e.g., new laboratory test, increased physician awareness, a new physician in town) Change in diagnostic criteria Change in reporting requirements Change in the population Change in the level and emphasis on active case detection Random events Increased public awareness

Surveillance for Evaluation Health programs may be aimed at Increasing vaccination levels Reducing smoking Increasing fruit and vegetable consumption Increasing physical activity Decreasing obesity Increasing screening

Vaccine Preventable Disease State public health officials monitor vaccine preventable disease rates May signal that the vaccination program is not reaching specific at-risk populations Monitoring rates by racial/ethnic groups; for example, may show that the increasing rate only exists among a given minority group Do barriers exist related to culture, language, and access to care? The vaccination program should then be altered to address these barriers

Value The value of a surveillance program is determined by Whether appropriate actions have been taken to date as a result of information from the surveillance system Whether the information has been used to make decisions and take action Whether monitored prevalence of the outcome variable relates to the level and distribution of services available Whether the information may be used in the future

Causal Insights from Descriptive Epidemiology Plotting health-related states or events over time can give the epidemiologist insights into probable determinants of disease If a disease occurs only in the summer, then that is when the epidemiologist searches for causal factors

Causal Insights from Descriptive Epidemiology Studies involving geographic comparisons of disease frequency between groups, states, countries, migration studies, and twin studies have further yielded important insights into the respective roles genetic and environmental forces have on disease.

Final Thoughts Confounding factors are always a threat in descriptive studies Analytic epidemiologic studies are better for minimizing the threat of confounding Nevertheless, descriptive studies are a good first step in the search for causes of health-related states or events