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Read the following articles and news reports about cancer detection and treatment in the US. Respond to the
following questions. You will need to do your own math for the question on Bayes’ Theorem.
https://www.npr.org/2020/10/26/927743332/how-houston-clinics-are-catching-up-with-backlog-in-mammograms
Respond briefly to the NPR article. How might COVID-19 impact the detection and treatment of other
illnesses? Using the prevalence of breast cancer (1% of women over age 50) Sensitivity (90%) and Specificity
(90%) of mammograms in general, How many women over 50 (per 1000) that have cancer may be
undiagnosed in a year?
There are approximately 60 million women over age 50 in the US. If we lost 6 months of screening time due to
COVID, how many women might have missed or delayed a diagnosis? Use your Bayes’ Theorem information,
the USPTF information, and the population to produce a rough estimate.
Read the Cancer and Work document. What information does this provide about how a cancer diagnosis
effects full time work?
Skim (this is a more challenging document) the Financial Hardship document. Pay special attention to Table 2
and Table 3. Which characteristics are most likely to predict a high out of pocket burden, material financial
hardship, and psychological financial hardship? What conclusions do the authors offer?
What conclusion can you draw from these documents, and your knowledge of insurance, with regard to cancer,
work, finances, and insurance?
Question 1
Respond briefly to the NPR article. How might COVID-19 impact the detection and treatment of other
illnesses?
Question 2
Complete a Bayes’ Theorem Matrix. Using the prevalence of breast cancer (1% of women over age 50)
Sensitivity (90%) and Specificity (90%) of mammograms in general, How many women over 50 (per 1000) that
have cancer may be undiagnosed in an average year?
Question 3
There are approximately 60 million women over age 50 in the US. If we lost 6 months of screening time due to
COVID-19, how many women might have missed or delayed a diagnosis? Use your Bayes’ Theorem
information, the USPTF information, and the population to produce a rough estimate.
Question 4
Read the Cancer and Work document. What information does this provide about how a cancer diagnosis
effects full time work?
Question 5
Skim (this is a more challenging document) the Financial Hardship document. Pay special attention to Table 2
and Table 3. Which characteristics are most likely to predict a high out of pocket burden, material financial
hardship, and psychological financial hardship? What conclusions do the authors offer?
Question 6
What conclusion can you draw from these documents, and your knowledge of insurance, with regard to cancer,
work, finances, and insurance? Explain.