Disease Detectives B/C Question Marathon

[Polka-dotty]

Here is a question:
Describe the difference between Pathogenicity, Virulence, and Infectivity.

[Fyren]

Here is a question:
Describe the difference between Pathogenicity, Virulence, and Infectivity.

Yay, another question to keep the thread going!

my answer (Click to Show Hidden Text)

Pathogenicity and Infectivity refer to the capacity of an agent to cause disease or infection (respectively) in a susceptible host.  Virulence is often interchanged with pathogenicity, but can also be said to refer to the severity of a disease.

[Polka-dotty]

Correct! I think it is your turn to ask a question now.

[labchick]

What is the difference between direct and indirect rate adjustment and what are the pros and cons of each method of adjustment?

[Fyren]

What is the difference between direct and indirect rate adjustment and what are the pros and cons of each method of adjustment?

Do you have an answer? I've never heard of those terms...

[Flavorflav]

I'll answer it tonight if the OP doesn't come back, but I'm not going to post so it'll reverse back to you. Unless someone else wants to stab at it first? It is only for C division, BTW.
Okay. Age adjustment adjusts crude rates for the age breakdown of the population. Direct adjustment is preferred, but requires information about the age breakdown of the disease/event in question. You take the age-specific rates and weight them according to the US Standard Population chart, then add up the weighted rates to get the age-adjusted rate for the whole population. If you don't have the age breakdown for the disease, but only for the local population, you use indirect. Here, you calculate the expected number of cases/events/whatever for each age group by multiplying the rate in the standard population by the size of your study population in that bracket. Then you add up all the brackets to get the total expected, and compare it to the actual. One common use of this is the Standardized Mortality Ratio.

[labchick]

I'll answer it tonight if the OP doesn't come back, but I'm not going to post so it'll reverse back to you. Unless someone else wants to stab at it first? It is only for C division, BTW.
Okay. Age adjustment adjusts crude rates for the age breakdown of the population. Direct adjustment is preferred, but requires information about the age breakdown of the disease/event in question. You take the age-specific rates and weight them according to the US Standard Population chart, then add up the weighted rates to get the age-adjusted rate for the whole population. If you don't have the age breakdown for the disease, but only for the local population, you use indirect. Here, you calculate the expected number of cases/events/whatever for each age group by multiplying the rate in the standard population by the size of your study population in that bracket. Then you add up all the brackets to get the total expected, and compare it to the actual. One common use of this is the Standardized Mortality Ratio.

Yeah, that pretty much covers it. It is only for C division.

[Polka-dotty]

Whewf! I have also never heard of that before. Give a brief description of Selection Bias, Recall Bias, Data Bias, Measurement Bias.

[bwspy]

Whewf! I have also never heard of that before. Give a brief description of Selection Bias, Recall Bias, Data Bias, Measurement Bias.

I had never heard of these before. Here are the wikipedia articles on them:

Selection bias is one of three types of bias that can threaten the validity of a study. Selection bias occurs when study subjects are selected or become part of the study as a result of a third, unmeasured variable which is associated with both the exposure and outcome of interest. For instance, it has repeatedly been noted that cigarette smokers and non smokers tend to differ in their study participation rates. (Sackett D cites the example of Seltzer et al., in which 85% of non smokers and 67% of smokers returned mailed questionnaires.) It is important to note that such a difference in response will not lead to bias if it is not also associated with a systematic difference in outcome between the two response groups.

Information bias is bias arising from systematic error in the assessment of a variable.[50] An example of this is recall bias. A typical example is again provided by Sackett in his discussion of a study examining the effect of specific exposures on fetal health: "in questioning mothers whose recent pregnancies had ended in fetal death or malformation (cases) and a matched group of mothers whose pregnancies ended normally (controls) it was found that 28% of the former, but only 20% of the latter, reported exposure to drugs which could not be substantiated either in earlier prospective interviews or in other health records".[49] In this example, recall bias probably occurred as a result of women who had had miscarriages having an apparent tendency to better recall and therefore report previous exposures.

That is all I could find.

[bwspy]

My turn.

What disease is spreading throughout Guinea right now and (optional) what animal do they suspect is spreading it?