Two settings, one test: the PU520 Unit 7 screening test appraisal example shows predictive value collapsing at low prevalence and judges whether community screening meets accepted program criteria. Searches like "pu 520 unit 7 assignment example", "pu520 unit 7 sample" and "pu520 unit 7 example" land here.
What a finished PU520 Unit 7 screening test appraisal looks like
Two-by-two tables anchor the appraisal, one per setting, each built for a notional 10,000 people so the counts read as whole persons. In the community table, 50 people have the infection; the test finds 48 and misses 2, while 199 of the 9,950 uninfected test positive. In the clinic table, 800 are infected, 768 are detected, and 184 false positives appear among 9,200. Below each table the example computes sensitivity, specificity, both predictive values and the positive likelihood ratio of 48, showing the arithmetic once. A paragraph compares the settings. The second half of the paper appraises community screening against the Wilson and Jungner principles published by the World Health Organization, and it ends by recommending a two-stage protocol in which every reactive rapid result goes to a confirmatory laboratory test.
How a PU520 Unit 7 example is structured
The appraisal separates properties of the test from properties of the setting, and its order makes that separation visible. Sensitivity and specificity come first because they belong to the test and do not change between tables. Predictive values come second because they depend on prevalence, and computing them twice from the same test is the most direct way to show it. The tables use natural frequencies instead of conditional probabilities, which lets a reader count false positives rather than infer them from a formula. The likelihood ratio follows as the bridge between the two ideas: a property of the test that tells a clinician how far a result should move an estimate. Program criteria come last, since whether to screen is a policy question that the arithmetic informs but cannot settle alone. The confirmatory protocol answers the false-positive burden the tables revealed.
Ten thousand notional people
Each table starts from a round population so every cell is a count of persons. The example notes that the round number is a device for clarity and changes none of the proportions.
Properties of the test
Sensitivity of 96 percent and specificity of 98 percent, calculated from the columns of either table and identical in both. The example says where such figures come from in practice: a validation study against a reference standard.
Properties of the setting
Positive predictive value of about 19 percent in the community and 81 percent in the clinic. Negative predictive value stays above 99 percent in both, which the example explains without treating it as praise for the test.
False positives as a workload
In the community table, 199 people receive a positive result that is wrong. The paper converts that into confirmatory tests, follow-up visits and anxiety, the practical cost any program must plan for.
Against the Wilson and Jungner principles
Importance of the condition, a recognizable early stage, an acceptable test, available treatment and cost are each judged in a sentence. Two criteria are met clearly, and one depends on linkage to care the example cannot verify.
Where marks go in PU520 Unit 7
Screening appraisals are typically graded on calculation, on interpretation of predictive value, and on judgment about the program. Calculation marks require the table to be built correctly, with disease status in the columns and test result in the rows, and the example labels both before filling a single cell. The interpretation row is where most points move. Papers that report sensitivity and stop, as though it answered what a positive result means for the person holding it, lose the criterion outright. Deductions also come from predictive values calculated along the wrong margin, from prevalence treated as a property of the test, and from recommendations that ignore the false-positive count the paper itself produced. Program judgment earns credit when criteria are applied to this condition in this population, not recited as a list.
Get a PU520 Unit 7 example written to your instructions
Send the test characteristics and prevalence figures from your PU520 Unit 7 scenario, together with the prompt and grading rubric. The desk builds both tables, computes each measure and appraises the program against whichever criteria your section names. Expect the free first custom sample within 24-48 hours, with the false-positive count stated in whole persons.
PU520 Unit 7 questions, answered
Why do the predictive values change when the test stays the same?
Because predictive value answers a different question from sensitivity. Sensitivity asks how often the test catches disease that is present; positive predictive value asks how often a positive result is correct, and that depends on how many uninfected people are being tested. When disease is rare, even a small false-positive rate applied to thousands of healthy people outnumbers the true cases.
What is a likelihood ratio, and does the appraisal need one?
A positive likelihood ratio is sensitivity divided by one minus specificity, here 0.96 over 0.02, or 48. It says how many times more likely a positive result is in someone infected than in someone who is not. Not every PU520 prompt asks for it, but it links test properties to clinical decisions cleanly, and many graders credit its inclusion.
Should the recommendation be for or against screening?
Either can earn full marks if the reasoning holds. The example supports community screening only with a confirmatory step and linkage to treatment, because its tables show a single rapid test mislabeling about four people for every one it correctly identifies. An appraisal arguing against screening on cost grounds would need to show the numbers behind that claim.