Tuberculosis screening of new hospital staff is analyzed in this HS340 Unit 8 example, which shows why most positives are false at 2 percent prevalence and how retesting changes that. Searches like "hs 340 unit 8 assignment example", "hs340 unit 8 sample" and "hs340 unit 8 example" land here.
What a finished HS340 Unit 8 screening test analysis looks like
The analysis fills about four pages and centers on two tables. The first applies the assay's estimated sensitivity of 80 percent and specificity of 97 percent to 5,000 hires, 100 of them infected. It finds 80 of those 100, misses 20, and flags 147 of the 4,900 uninfected workers, so 227 people test positive and only 35.2 percent of them carry the infection. Negative predictive value reaches 99.6 percent. The second table takes the 227 positives through an independent repeat test: 64 true positives test positive again, while only about 4 of the 147 false positives do, lifting predictive value to about 94 percent. A paragraph on cost follows, since serial testing gives up sensitivity, dropping it to 64 percent overall. One assumption carries the second table, and the last section names it.
How a HS340 Unit 8 example is structured
Order matters because the analysis builds one idea on another. Test properties are stated first, with a note that sensitivity for latent infection cannot be measured directly, since no reference standard exists, and is usually estimated from people with active disease. The first table then turns those properties into counts of people, which makes the false positives visible as colleagues rather than as a percentage. Predictive values follow, computed along the rows of the table, and the example states the question each one answers for a worker holding a result. The second table comes only after the problem is clear. It shows the trade that retesting makes: far fewer false alarms, a real loss of sensitivity. The closing section questions independence, since a worker whose immune response sits near the cutoff may test positive twice for the same reason.
Properties the assay brings
Sensitivity of 80 percent and specificity of 97 percent, each defined in a sentence. The analysis flags that the sensitivity figure borrows from patients with active tuberculosis, the nearest available reference.
Five thousand hires as counts
One hundred infected, 4,900 not. The assay finds 80 and misses 20; it also flags 147 uninfected workers, which the example converts into 147 occupational health visits and chest x-rays.
What a positive means here
Of 227 positive results, 80 are true, so positive predictive value is 35.2 percent. For a low-risk new hire, a positive result is more likely wrong than right.
The repeat test as a filter
Retesting only the positives keeps 64 true cases and about 4 false ones. Predictive value climbs to roughly 94 percent while overall sensitivity falls to 64 percent.
An assumption worth doubting
The second table treats the two tests as independent. A borderline immune response near the assay's cutoff could repeat, so the analysis calls its 94 percent an upper estimate.
Where marks go in HS340 Unit 8
Screening analyses in introductory courses are usually graded on building the table, computing the four measures, and interpreting predictive value for the setting described. Table marks depend on disease status defining the columns and test result the rows, and on totals that reconcile to 5,000. Measure marks need each formula applied along the right margin; predictive values read down the columns instead of across the rows are the error graders in many HS340 sections see most. Interpretation carries the largest share. Spelling out the odds that one positive new hire is actually infected, and what the 147 false positives cost the employer, is how the example claims it. Deductions also follow analyses that treat 97 percent specificity as proof of accuracy, that ignore the sensitivity lost to retesting, or that recommend treatment for every positive.
Get a HS340 Unit 8 example written to your instructions
Sensitivity, specificity and prevalence from the HS340 Unit 8 scenario, the questions as set and the rubric are all the desk requires. Each table is built in whole people, every measure computed along its proper margin, and predictive value explained for the person holding a result. The first custom sample is free and comes back in 24-48 hours.
HS340 Unit 8 questions, answered
Why is a positive result more likely wrong than right in this example?
Because so few of the people tested are infected. At 2 percent prevalence, 97 percent specificity still produces 147 false positives among 4,900 uninfected workers, while 80 percent sensitivity finds only 80 true ones among 100. False positives outnumber true ones almost two to one. In a group where infection is common, the same assay would give a far more reliable positive.
Does the analysis recommend testing every positive a second time?
It recommends a second test for positives in workers with no known risk factors, which matches the direction of United States guidance for health care personnel updated in 2019. For hires with risk factors, such as birth in a country where tuberculosis is common, a single positive is more credible. The example sorts that recommendation by risk rather than applying one rule to everyone.
Where do sensitivity and specificity figures for an assay come from?
From validation studies, which compare the test against the best available reference. For latent tuberculosis there is no perfect reference, so sensitivity is often estimated in people with confirmed active disease and specificity in low-risk groups unlikely to be infected. Citing the source of each figure, and noting that it is an estimate, strengthens any screening analysis.