Section 1.6 Defines descriptive epidemiology in words and examples. But you can only really understand the value of descriptive epidemiology is you think about how it pertains to a real situation.
Re-read the following description:
Section 6: Descriptive Epidemiology
The 5W’s of descriptive epidemiology:
What = health issue of concern
Who = person
Where = place
When = time
Why/how = causes, risk factors, modes of transmission
As noted earlier, every novice newspaper reporter is taught that a story is incomplete if it does not describe the what, who, where, when, and why/how of a situation, whether it be a space shuttle launch or a house fire. Epidemiologists strive for similar comprehensiveness in characterizing an epidemiologic event, whether it be a pandemic of influenza or a local increase in all-terrain vehicle crashes. However, epidemiologists tend to use synonyms for the five W’s listed above: case definition, person, place, time, and causes/risk factors/modes of transmission. Descriptive epidemiology covers time, place, and person.
Compiling and analyzing data by time, place, and person is desirable for several reasons.
1. By looking at the data carefully, the epidemiologist becomes very familiar with the data. He or she can see what the data can or cannot reveal based on the variables available, its limitations (for example, the number of records with missing information for each important variable), and its eccentricities (for example, all cases range in age from 2 months to 6 years, plus one 17-year-old.).
2. The epidemiologist learns the extent and pattern of the public health problem being investigated — which months, which neighborhoods, and which groups of people have the most and least cases.
3. The epidemiologist creates a detailed description of the health of a population that can be easily communicated with tables, graphs, and maps.
4. the epidemiologist can identify areas or groups within the population that have high rates of disease. This information in turn provides important clues to the causes of the disease, and these clues can be turned into testable hypotheses.
The figures in Section 1.6, act as examples to show the benefits and limitations of Descriptive Epidemiology.
For example, look at Figure 1.14, Lung Cancer Rates — United States, 1930–1999, and answer the questions at the end.
Figure 1.14 Lung Cancer Rates — United States, 1930–1999
This figure gives us a description of the occurrence of lung cancer over time for males and females. You can see that the two curves are similar but the death rates are higher in males than in females in every year analyzed. Notice that the rate is climbing in both males and females. In the males, the curve seems to reach a peak around 1990, and then trends lower. The rate in the females seems to level off during same time period, but it’s not clear wether it will also begin to fall.
This graph is descriptive only. It doesn’t point to an obvious cause. We already realize that cigarette smoking has been determined to be heavily associated with Lung Cancer deaths, but that’s not shown in this graph. The information that we can get from of a graph like this is limited. It doesn’t give us a cause for Lung Cancer deaths. It’s not giving us a cause/effect relationship, but it still gives us some insight both lung cancer and points us in an interesting direction
If we don’t recognize that the graph is merely descriptive, we might make a false assumption about the role of gender in Lung Cancer deaths. Does being a male put you at a higher risk of contracting or dying from Lung Cancer? Answer yes or no, explain.
The graph doesn’t show us that cigarette smoking is known to be a direct cause of Lung Cancer. We have that info from another source already. We can use the information in this graph, plus our knowledge about the association of smoking with Lung Cancer, to perform deductive reasoning to make a guess about what is going on. We can form a hypothesis, and then use Analytical reasoning in Section 7 to test out hypothesis,
2. Compare the two curves for male vs. female, can you deduce the reason why the two curves are not directly on top of each other?
3. Why does the male curve show a higher death rate?
4. Why does the male curve shoot up earlier in the 20th century than does the female curve?
5. A hypothesis is a testable statement. What hypothesis could you draw from your reasoning?
Section 7 – Analytical Epidemiology
Re-read this section and answer the questions that follow:
As noted earlier, descriptive epidemiology can identify patterns among cases and in populations by time, place and person. From these observations, epidemiologists develop hypotheses about the causes of these patterns and about the factors that increase risk of disease. In other words, epidemiologists can use descriptive epidemiology to generate hypotheses, but only rarely to test those hypotheses. For that, epidemiologists must turn to analytic epidemiology.
The key feature of analytic epidemiology is a comparison group. Questions:
6. Re-read the statement from the reading above: “The key feature of analytic epidemiology is a comparison group.”
What is a comparison group and how does it work?
7-8. Using your hypothesis that you developed in question 5 above, suggest an Analytical study that would allow you to test your hypothesis.
7. Consider the rationale of setting up an Experimental Study. One possible way to do this would be to set up groups of male and female teenagers, teach half them to smoke, get them to commit to smoking for the rest of their lives. Set up a comparison group, get them to commit to not smoking for the rest of their lives. Follow them for the next 25-40 years to chart how many of them die of Lung Cancer every year.
Would this be the most ethical way to test your hypothesis? Answer yes or no, and explain your answer.
8. Instead, consider the setting up an Observational Study. read about and consider the three types of studies: Cohort, Case-control and Crossectional Studies.
Choose one of these three approaches, and briefly describe how you would do it. Be sure to say what your comparison group would be.
Sections 8, 9 and 10. Read these sections and think about these concepts relate to the HIV epidemic.
Now read Section 1.10. Work through the problem in Exercise 1.9, as it applies to Dengue Fever. Check your answers on the link to be sure that you understand how these concepts work.
Challenge: After reading this information to find out what each of these terms mean, now outline the chain of infection for HIV by identifying the reservoir(s), portal(s) of exit, mode(s) of transmission, portal(s) of entry, and factors in host susceptibility for HIV.
2. Portals of exit:
3. Modes of transmission:
4. Portals of entry:
5. Factors in host susceptibility: