TOPIC: Measuring Morbidity: Prevalence and Incidence
Read the scenario below and complete the assignment as instructed.
Scenario
In Community X (population 20,000), an epidemiologist conducted a prevalence survey in January of 2012 and reported an HIV prevalence of 2.2%. Over the next 12 months, the department of health reported an additional 50 new HIV cases between February 2012 and January 2013. The total population stayed constant at 20,000.
Part 1
How many people had HIV in January 2012? Present or describe the formula you used to arrive at your answer.
Calculate the incidence rate assuming no HIV-related deaths over the 12-month period. Present or describe the formula you used to arrive at your answer. Be sure to clearly indicate the numerator and denominator used in your calculation and include an appropriate label for the rate.
In a summary of 200-250 words, interpret the results and discuss the relationship between incidence and prevalence. Discuss whether or not the epidemiologist should be concerned about these new HIV infections, assuming a previous incidence rate of 0.5 per 1,000 person-years prior to this updated risk assessment.
Part 2
A rapid test used for diagnosing HIV has a sensitivity of 99.1% and a specificity of 90%. Based on the population prevalence of 2.2% in 2012, create a 2×2 table showing the number of true positives, false positives, false negatives, and true negatives. Calculate the positive predicative value and negative predictive value for this test. Refer to the “Creating a 2×2 Contingency Table” resource for guidance.
In 200-250 words, discuss whether or not the epidemiologist should recommend this test as part of a universal HIV screening program. Provide rationale for your recommendation applying the positive and negative predictive values. Present or describe the formula you used to arrive at your answer.
STUDY MATERIALS
Read Chapters 14 and 15 in Gordis Epidemiology.
Read “Association or Causation: Evaluating Links Between ‘Environment and Disease,'” by Lucas and McMichael (2005), located on the World Health Organization website. URL: https://www-ncbi-nlm-nih-gov.lopes.idm.oclc.org/pmc/articles/PMC2626424/pdf/16283057.pdf
Read “Weak Associations in Epidemiology: Importance, Detection, and Interpretation,” by Doll, from Journal of Epidemiology (1996). URL: https://www.jstage.jst.go.jp/article/jea1991/6/4sup/6_4sup_11/_pdf
Read “Causal Inference Based on Counterfactuals,” by Hofler (2005), located on the BioMed Central website. URL: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-5-28
Read “Multicausality: Confounding,” by Schoenbach (2004), located on the Epidemilog.net website. URL: http://www.epidemiolog.net/evolving/Multicausality-Confounding.pdf
View “Sensitivity and Specificity – Explained in 3 Minutes,” by Martin (2014), located on the YouTube website. URL: https://www.youtube.com/watch?v=FnJ3L-63Cf8
View “The Relationship Between Incidence and Prevalence,” by Patwari (2013), located on the YouTube website. URL: https://www.youtube.com/watch?v=1jzZe3ORdd8
Use the “Creating a 2×2 Contingency Table” resource to assist with the completion of the Measuring Morbidity: Prevalence and Incidence assignment, as needed.Creating a 2×2 Contingency Table
Creating a 2×2 contingency table is very useful in calculating a variety of public health measurements, including sensitivity and specificity, negative and positive predictive value, risk ratios, attack rate ratios, and odds ratios.
A 2×2 table is actually a 3×3 table when you include the rows and columns for the totals. If you are setting up a table to measure the sensitivity and specificity of a test or its negative and positive predictive values, you should put the test results on the y-axis (rows) and the actual presence of disease on the x-axis (columns).
Disease
No Disease
Total
Test (+)
(a)
(b)
a + b
Test (-)
(c)
(d)
c + d
Total
a + c
b + d
a + b + c + d
The highlighted section is where you will enter the data for each corresponding cell. You can set up the table switching the rows and columns but you will generally see them set up in this format with test results on the y-axis and disease on the x-axis.
Setting up a table to measure the association of a risk factor or exposure is similar, with the outcome or disease on the x-axis and the presence of the risk factor or exposure on the y-axis.
Disease
No Disease
Total
Exposure (+)
(a)
(b)
a + b
Exposure (-)
(c)
(d)
c + d
Total
a + c
b + d
a + b + c + d
Note: You can set up the table differently but you will need to be cognizant of which numbers you are putting in your numerator and denominator for the measure you are calculating. For example:
Exposure (+)
Exposure (-)
Total
No Disease
(b)
(d)
b + d
Disease
(a)
(c)
a + c
Total
a + b
b + c
a + b + c + d
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