# wk3 scenario assgn

## Description

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, 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.

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.

Read Chapters 14 and 15 in Gordis Epidemiology.

Read “Multicausality: Confounding,” by Schoenbach (2004), located on the Epidemilog.net website.

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http://www.epidemiolog.net/evolving/Multicausality-Confounding.pdf

Read “Weak Associations in Epidemiology: Importance, Detection, and Interpretation,” by Doll, from Journal of Epidemiology (1996).

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Read “Causal Inference Based on Counterfactuals,” by Hofler (2005), located on the BioMed Central website.

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View “The Relationship Between Incidence and Prevalence,” by Patwari (2013), located on the YouTube website.

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