Universal celiac antibody screening for 7,500 composite third-graders is weighed in MN505 Unit 5 against a prevalence near 1 percent, and found to generate far more referrals than diagnoses. Searches like "mn 505 unit 5 assignment example", "mn505 unit 5 sample" and "mn505 unit 5 example" land here.
Screening Every Third-Grader for Celiac Disease: A Test Performance and Program Analysis
[Student Name]
Purdue University Global
MN505: Epidemiology and Health Promotion
Unit 5 Assignment
[Instructor Name]
[Date]
The pediatric group, county and program are composites written as a model document. Test characteristics are illustrative round values.
The Proposal
A composite pediatric group serving a county of about 7,500 third-graders a year proposes a blood test for celiac disease for every child at the third-grade well visit, regardless of symptoms. The group argues that celiac disease is common, often silent and easily detected with a single antibody test, and that early diagnosis will prevent growth problems and later complications. This analysis tests that proposal in two steps: first, what the test will produce in this population, and second, whether finding silent disease earlier helps the children found.
What Belongs to the Test
Sensitivity and specificity are properties of the assay and travel with it into any setting. For this analysis, the tissue transglutaminase IgA antibody test is taken to have a sensitivity of 92 percent, meaning it detects 92 of every 100 children who have celiac disease, and a specificity of 96 percent, meaning it correctly clears 96 of every 100 children who do not. These are round illustrative values within the range reported for the test in children (Husby et al., 2020).
What Belongs to the Population
Prevalence is a property of the population, not the test. Among unselected children in the United States, celiac disease affects roughly 1 percent. That estimate is uncertain, varies by ancestry and region and may be slightly higher in some studies, so the analysis uses 1.0 percent as a working figure and returns to its sensitivity later.
The Two-by-Two Table
Working from 7,500 children rather than from percentages keeps every false positive visible as a child.
Children with celiac disease: 7,500 x 1.0 percent = 75. Detected (true positives): 75 x 0.92 = 69. Missed (false negatives): 6.
Children without celiac disease: 7,425. Correctly cleared (true negatives): 7,425 x 0.96 = 7,128. Flagged falsely (false positives): 297.
Total positive results: 69 + 297 = 366. Total negative results: 6 + 7,128 = 7,134.
Predictive Values
Positive predictive value: 69 / 366 = 18.9 percent. Negative predictive value: 7,128 / 7,134 = 99.9 percent.
About four of every five children who test positive would not have celiac disease, even though the test is 96 percent specific, because the disease is rare in the population being screened. The negative result is highly reassuring, but it is the positive result that drives what happens next, and that is where the program's costs lie.
Positives as Workload
Each of the 366 positive children would be referred to pediatric gastroenterology for confirmation, which usually includes repeat serology and, for most, an upper endoscopy with small-bowel biopsy. That is 366 referrals a year from one county's third grade, and about 366 / 69 = 5.3 children referred for every confirmed case. Each referral means a missed school day, parental time off work, sedation for endoscopy in many cases, and anxiety for families of the 297 children who turn out not to have the disease. A regional gastroenterology service that currently sees a few hundred new referrals a year would see its volume change substantially from this one program. Workload is treated here as an outcome in its own right, because a screening program is judged by what it asks of families and services as well as by what it detects.
Where the Same Test Earns Its Place
The same test performs very differently where celiac disease is common. Among 400 children with type 1 diabetes, in whom celiac disease affects roughly 6 percent, there would be 24 cases. The test would detect about 22 and falsely flag about 15 of the 376 without disease, giving a positive predictive value of 22 / 37 = 59.5 percent. Three in five positives would be true. This is why guidelines support testing children at higher risk, such as those with type 1 diabetes or a first-degree relative with celiac disease, while the case for testing all children is weaker (Husby et al., 2020).
Judging the Program
Wilson and Jungner (1968) proposed principles that a screening program should meet. Several are met here: celiac disease is an important condition, a suitable test exists, and treatment, a gluten-free diet, is available. Others are not clearly met. The natural history of silent celiac disease found by screening is not well understood; some children with positive antibodies and mild changes may never develop symptoms. There must be an agreed policy on whom to treat, and for asymptomatic children with borderline findings there is not. Most important, early treatment should improve outcomes compared with waiting for symptoms, and evidence for that in screen-detected children is limited.
The US Preventive Services Task Force reached the same conclusion in 2017, finding the evidence insufficient to assess the balance of benefits and harms of screening asymptomatic people for celiac disease (US Preventive Services Task Force, 2017). A strict gluten-free diet is also a real burden for a child who felt well before the diagnosis.
If prevalence were higher than 1 percent, the positive predictive value would rise somewhat; at 2 percent it would be about 32 percent, still leaving two false positives for each true one. The conclusion does not depend on the exact prevalence estimate, since even a doubled prevalence leaves most positive results false.
What Would Change the Conclusion
Three developments would strengthen the case for universal screening. The first would be trial evidence that children with screen-detected celiac disease who start a gluten-free diet have better growth, bone health or quality of life than similar children diagnosed only when symptoms appear. The second would be a two-step strategy that lowers false positives, for example repeating the antibody test or adding a second antibody before referral, which would reduce the 297 false positives substantially at the cost of some extra blood draws. The third would be clear agreement on how to manage children with positive antibodies and mild biopsy changes. Until at least the first of these exists, the program would impose certain costs for an uncertain benefit.
Conclusion
Universal third-grade screening would produce 366 positives a year, about four in five of them false, and would refer more than five children to gastroenterology for every case confirmed, for a benefit that current evidence cannot demonstrate. The group's resources would be better spent on testing children with symptoms or with known risk factors, where the same test performs far better. That targeted approach would also let the group gather its own data on how many children it finds, how many are referred and how families experience the process, which could inform a future decision if the evidence on screen-detected disease improves.
References
Husby, S., Koletzko, S., Korponay-Szabó, I., Kurppa, K., Mearin, M. L., Ribes-Koninckx, C., Shamir, R., Troncone, R., Auricchio, R., Castillejo, G., Christensen, R., Dolinsek, J., Gillett, P., Hróbjartsson, A., Koltai, T., Maki, M., Nielsen, S. M., Popp, A., Størdal, K., ... Wessels, M. (2020). European Society Paediatric Gastroenterology, Hepatology and Nutrition guidelines for diagnosing coeliac disease 2020. Journal of Pediatric Gastroenterology and Nutrition, 70(1), 141-156. https://doi.org/10.1097/MPG.0000000000002497
US Preventive Services Task Force. (2017). Screening for celiac disease: US Preventive Services Task Force recommendation statement. JAMA, 317(12), 1252-1257. https://doi.org/10.1001/jama.2017.1462
Wilson, J. M. G., & Jungner, G. (1968). Principles and practice of screening for disease (Public Health Papers No. 34). World Health Organization.
How this MN505 Unit 5 example is structured
The analysis separates what belongs to the test from what belongs to the population, and it does so before any predictive value appears. Sensitivity and specificity are stated first as properties the assay carries into any setting. Prevalence follows as a property of the third-grade population, sourced and hedged. Only then are the cells filled, working from 7,500 children rather than from percentages, so each false positive is visible as a child. The predictive values come next, and the analysis stays on the positive result because it drives the referral load. Workload is treated as an outcome in its own right. The type 1 diabetes row shows the same test performing far better where the disease is common. A final section asks the harder question the arithmetic cannot answer: whether finding silent celiac disease early changes anything for a child, which is where the evidence is thinnest.
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Screening prompts differ in the test, the condition and the population, so the table is always rebuilt from yours. Include the figures your Unit 5 assignment provides, its questions and your rubric. A first custom sample, free of charge and written to those instructions, is ready in 24-48h with the cells, predictive values and a judgment on the program. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
MN505 Unit 5 questions, answered
Why does a test with 96 percent specificity still produce so many false positives?
Because 4 percent of a large group is a large number. With 7,425 children free of celiac disease, a 4 percent false-positive rate flags 297 of them, while only 75 children have the disease at all. When the condition is rare, even a small error rate applied to the healthy majority outnumbers the true cases. That is the central point of the unit.
Should the analysis recommend against screening?
It should recommend what the evidence supports, and the example stops short of a flat no. It argues that universal screening of third-graders is not justified on current evidence, cites the task force's 2017 finding of insufficient evidence, and supports continued targeted testing in higher-risk groups. A nuanced conclusion tied to specific numbers tends to score better than a verdict in either direction.
Where does the prevalence figure for a screening analysis come from?
Ideally from studies in a population like the one being screened, cited with the year. Where the prompt supplies a figure, the analysis uses it and says so. Where it does not, a defensible estimate from published screening studies is labeled an assumption, and a short sensitivity check shows how the predictive value would change at a higher or lower prevalence.