| Course | PU520 Principles of Epidemiology |
|---|---|
| Unit | Unit 9 |
| Paper type | Confounding memo |
| Length | About 810 words, 5 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | Master of Public Health (MPH) |
| Updated | October 2026 |
Free sample paper for PU520 Unit 9
Why the Forty Percent Should Not Go on the Poster: A Confounding Memo Advising a Composite County Health Officer That Health Plan Data Showing Fewer Winter Deaths Among Vaccinated Seniors Cannot Support a Campaign Claim, Because Healthier People Get Vaccinated
[Student Name]
Purdue University Global
PU520: Principles of Epidemiology
Unit 9 Confounding Memo
[Instructor Name]
[Date]
The county, the health plan data and every figure are composite and bracketed for the writer's own data; the published studies cited are real.
Memo
To: [County Health Officer] From: [Epidemiologist] Date: [date] Subject: The [40] percent figure for the flu campaign poster
Bottom Line
The [40] percent figure should not appear on the poster. It almost certainly overstates what flu vaccination does for seniors, because the vaccinated group was healthier before the flu season began. We recommend promoting vaccination with claims the evidence supports.
Background
The figure comes from [68,000] health plan members aged 65 and older followed through one winter. All-cause deaths among the vaccinated occurred at [60] percent of the rate among the unvaccinated, which is where [40] percent comes from.
How the Estimate Moves With Adjustment
Unadjusted rate ratio: [0.60]. Adjusted for age and sex: [0.63]. Also adjusted for chronic conditions: [0.68]. Also adjusted for prior hospitalizations and home health use: [0.74]. Each adjustment for a measured difference in health moves the estimate toward no effect. That pattern suggests that differences we could not measure, such as frailty, difficulty with daily activities and being too ill to get to a clinic, would move it further. Every time we account for how sick people were, the vaccine's apparent benefit shrinks, and we cannot account for everything.
The Pre-Season Test
If the vaccine caused the difference, the vaccinated and unvaccinated should have similar death rates before flu begins circulating. In our data, vaccinated seniors had [45] percent fewer deaths in the weeks before the flu season than unvaccinated seniors. The vaccine cannot prevent deaths from flu that is not yet circulating. A published analysis found the same pattern: the apparent benefit of vaccination was largest before the flu season, pointing to vaccine uptake concentrated among older adults who were already in better health (Jackson et al., 2006).
Why the Size Is Implausible
National analyses estimate that influenza accounts for no more than about [10] percent of winter deaths among people aged 65 and older (Simonsen et al., 2005). A vaccine cannot prevent [40] percent of all winter deaths if influenza causes only a small fraction of them.
Why Adjustment Cannot Fix This
The health plan records diagnoses and hospital use, but not the things that most distinguish a senior who gets to a pharmacy for a flu shot from one who does not: whether they can walk, drive or arrange a ride; whether they are in the last months of life; whether they live in a nursing home where vaccination was declined on their behalf. People near the end of life are often not offered vaccination or decline it, and they die at high rates in any season. Because these differences are not in the data, statistical adjustment cannot remove them, however sophisticated the model.
What Would Answer the Question
Randomized trials and studies that confirm influenza with a laboratory test provide better estimates of what the vaccine prevents. Test-negative designs compare vaccination among people with flu-like illness who test positive and those who test negative, which reduces the healthy-user problem because both groups were sick enough to seek care. Those studies show real but more modest benefits, and they measure flu illness and hospitalization rather than all deaths.
How to Explain This to Staff
Staff who see the [40] percent figure in internal reports may ask why it is not being used. The explanation is short: the vaccinated were healthier before flu arrived, so the comparison measures health as much as vaccination. A one-paragraph note will go to program staff so the figure does not appear in other materials.
The Name for This Problem
This is confounding by health status, often called the healthy-user effect: the factors that lead people to get vaccinated, such as good health, mobility and regular contact with doctors, also lower their risk of death (Celentano & Szklo, 2019).
What the Poster Could Say Instead
"Flu can be serious for people 65 and older. A flu shot lowers your chance of getting sick with flu and of being hospitalized for it. Get yours at [location]." This message is supported by studies that confirm flu with a laboratory test, which avoid the healthy-user problem.
Risks of Using the Figure
If the figure were challenged publicly, it could damage trust in the department and in vaccination itself. A defensible, smaller claim protects both.
Next Steps
I can draft alternative poster text with the communications team this week and review it with you before printing.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
Jackson, L. A., Jackson, M. L., Nelson, J. C., Neuzil, K. M., & Weiss, N. S. (2006). Evidence of bias in estimates of influenza vaccine effectiveness in seniors. International Journal of Epidemiology, 35(2), 337-344. https://doi.org/10.1093/ije/dyi274
Simonsen, L., Reichert, T. A., Viboud, C., Blackwelder, W. C., Taylor, R. J., & Miller, M. A. (2005). Impact of influenza vaccination on seasonal mortality in the US elderly population. Archives of Internal Medicine, 165(3), 265-272. https://doi.org/10.1001/archinte.165.3.265
PU520 Unit 9 instructions, in plain terms
Unit 9 in PU 520 typically asks students to explain a confounding problem to a decision-maker in memo form. Instructions usually require a clear recommendation, a plain explanation of the data, evidence of confounding, the name and mechanism of the bias, and an alternative course of action. Put the bottom line first, since the reader may decide on it alone. Keep background to one paragraph. Show how the estimate changes with adjustment, because that pattern introduces unmeasured confounding naturally. Use a test that a true effect could not explain, such as a comparison before the exposure could act. Check plausibility against known disease burden. Cite methodological and published studies in APA style, and offer a better message or next step so the memo solves a problem rather than only blocking one.
How the PU 520 Unit 9 example is put together
The sample opens with a header, subject line and bottom line, with a margin note on why the recommendation leads. Background describes the health plan data. A short table shows the rate ratio moving toward one with each adjustment, and a highlighted line notes that the benefit shrinks whenever sickness is accounted for. The pre-season comparison, flagged as the strongest evidence by a margin note, cites a published study with the same pattern. A plausibility check uses national mortality data. A section on why adjustment cannot fix the problem carries a third note. Better study designs, staff guidance, the name of the bias, alternative poster wording, risks and next steps close the memo.
Reading the PU520 Unit 9 grading rubric
PU 520 memo rubrics generally award points for a clear recommendation, concise background, accurate evidence of confounding, a correct explanation of the mechanism, plausibility reasoning, an alternative course of action, appropriate tone for a decision-maker, sources and presentation. Recommendation points go to a bottom line stated first. Evidence points reward showing how estimates change with adjustment. Mechanism points depend on naming the confounder and how it works. Plausibility points require checking against known burden. Alternative points go to a defensible replacement message. Memos that use a pre-exposure comparison to expose the bias tend to receive the strongest grades, because no real effect could produce it.
PU520 Unit 9 help from the desk
A common weakness in confounding memos is hiding the recommendation in the middle, by explaining confounding abstractly or by blocking a proposal without offering an alternative. Others overstate certainty in the opposite direction, implying the vaccine does nothing. State the recommendation first. Describe the data briefly. Show the estimate changing as measured differences are adjusted, then explain what remains unmeasured. Use a comparison that a true effect could not produce, and test plausibility against what is known about the disease. Name the bias in plain words. Offer a message the evidence supports, and make clear that the problem lies in the data's design, not in the value of vaccination.
Get a PU520 Unit 9 example written to your instructions
Whatever association your PU520 Unit 9 memo has to challenge, the desk can find the confounder the design leaves standing and explain it to the reader your prompt names. Include the scenario, any data table and the rubric. A first custom sample follows free in 24-48 hours, bottom line first and jargon kept out of the reader's way. 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.
More PU520 papers and MPH samples
- PU520 Unit 1: Counts Versus Rates West Nile Post
- PU520 Unit 2: Asthma Frequency Measures Problem Set
- PU520 Unit 3: Freight Corridor Association Exercise
- PU520 Unit 4: Case-Control Study Design Critique
- PU520 Unit 5: Community Dinner Outbreak Investigation
- PU520 Unit 6: Chlamydia Surveillance Trend Report
- PU520 Unit 7: Screening Test Appraisal
- PU520 Unit 8: Coffee Study Bias Seminar Reflection
- PU520 Unit 10: Epidemiologic Profile
- PU508 Unit 9: Opinion Leader Diffusion Case Analysis
- PU501 Unit 9: Syringe Services Policy Scan
PU520 Unit 9 questions, answered
Where can I find a free PU520 Unit 9 sample paper?
The complete PU520 Unit 9 confounding memo is here, advising against a 40 percent flu vaccine claim based on health plan data.
What is the healthy-user effect?
Confounding that arises because people who get preventive care tend to be healthier to begin with.
Why is the pre-season comparison important?
Vaccination cannot prevent flu deaths before flu circulates, so lower deaths then reveal confounding.
Why is 40 percent implausible?
Influenza causes only a small share of winter deaths among seniors.
How long is the PU520 Unit 9 memo?
About two pages, bottom line first.