| Course | NR 518 Population Health and Epidemiology in Nursing Practice |
|---|---|
| Week | 3 |
| Paper type | Measures of association exercise |
| Length | About 1,170 words, 3 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN |
| Updated | September 2026 |
Free sample paper for NR 518 Week 3
Measures of Association Exercise: Dry Versus Wet Cutting Among 400 Screened Countertop Workers, Carried From a Prevalence Ratio of 4.0 Through to 33 People and 165 in the County
Student Name
College of Nursing, Chamberlain University
NR 518: Population Health and Epidemiology in Nursing Practice
Instructor Name
Month Day, Year
Measures of Association Exercise: Dry Versus Wet Cutting Among 400 Screened Countertop Workers, Carried From a Prevalence Ratio of 4.0 Through to 33 People and 165 in the County
The Data and What They Are
This exercise uses a practice dataset built for teaching, modeled on the kind of screening a county health department might run among engineered stone fabrication workers. It is not taken from any published study, and its numbers should not be quoted as findings. Four hundred workers from 30 shops in one county attended free screening, including a chest CT. Each was asked which cutting method they mainly used over the past five years: dry cutting, with no water at the blade, or wet cutting, with water fed to the blade to suppress dust. Silicosis was defined by the county's outcome definition from Week 2.
The design matters for naming the measures. The screening happened once and found people who already had disease, so the proportions are prevalences, not risks of new disease over time. The measure of association is therefore a prevalence ratio, not a risk ratio, although it is calculated the same way. Treating it as a risk ratio would imply that we watched people develop disease, which this screening did not do.
The Two-by-Two Table
Table 1 shows the screening results.
Table 1
Silicosis at Screening by Main Cutting Method, Practice Dataset
| Main cutting method | Silicosis | No silicosis | Total | Prevalence |
|---|---|---|---|---|
| Dry cutting | 44 | 176 | 220 | 20.0% |
| Wet cutting | 9 | 171 | 180 | 5.0% |
| Total | 53 | 347 | 400 | 13.25% |
Note. Illustrative data constructed for this exercise; not from a published study.
The Measures, Each Carried to People
Prevalence ratio. 20.0% divided by 5.0% gives 4.0, with a 95% confidence interval of about 2.0 to 8.0. Workers who mainly dry-cut had four times the prevalence of silicosis of those who mainly wet-cut. The interval is wide because only nine wet-cutting workers had disease, but even its lower end, 2.0, would be a doubling. On its own, 4.0 says how much more likely, not how many people.
Prevalence difference. 20.0% minus 5.0% is 15.0 percentage points. This is the first measure that points toward people: among every 100 dry-cutting workers, about 15 more had silicosis than would be expected if they had worked like the wet-cutting group.
Excess cases among the exposed. 15.0 percentage points of 220 dry-cutting workers is 33 people. If the association were causal and nothing else differed between the groups, about 33 of the 44 dry-cutting workers with silicosis would not have had it had they worked wet. Equivalently, the attributable fraction among the exposed is (4.0 minus 1) divided by 4.0, or 75%.
Population attributable fraction. The prevalence in all 400 workers is 13.25%. Subtracting the wet-cutting prevalence of 5.0% and dividing by 13.25% gives about 62%. In people: 53 workers had silicosis, and about 20 would be expected if all 400 had worked like the wet-cutting group, so about 33 cases, the same 33 as above, are associated with dry cutting across the whole screened group.
Odds ratio, for comparison. The odds ratio is 4.75, with a confidence interval of about 2.2 to 10.0. It is larger than the prevalence ratio because silicosis is not rare in this group. Reporting the odds ratio as if it were a ratio of prevalences would overstate the association by nearly 20%, which is why the prevalence ratio is the measure to lead with here.
From the Screened Group to the County
A program needs a county number, not a screened-group number. Suppose the county's industry survey estimates 2,000 engineered stone fabrication workers, of whom 55%, or 1,100, mainly dry-cut. If the 15-percentage-point difference held for them, moving all 1,100 to wet cutting would be associated with about 165 fewer workers carrying silicosis at a future screening. That is the number a program director can plan around: roughly 165 people, spread across dozens of small shops.
That figure rests on assumptions that must be stated. The screened workers may differ from those who stayed away; sicker workers may have come forward, or the most exposed may have avoided screening for fear of losing work. Dry-cutting shops may also differ in other ways, such as ventilation and respirator use. Published work supports the direction of the finding: in a California case series, fewer than half of workers with silicosis reported using water suppression (Fazio et al., 2023), and in interviews with Los Angeles fabrication workers, 68% described regularly dry cutting (Fazio et al., 2025). But this practice dataset cannot establish cause.
Checks Before the Number Is Used
Before the county acts on these figures, three checks would make them more trustworthy. The first is years of work. Dry cutting was more common in the past, before wet methods spread, so dry-cutting workers may simply have worked longer, and silicosis rises with cumulative exposure. Splitting the table by years in the industry, under 10 and 10 or more, would show whether the prevalence ratio holds within each group. The second is shop. If most dry-cutting workers came from a few large shops with poor ventilation, the association may belong to those shops rather than to the cutting method, and a comparison within shops would help. The third is misclassification. Workers were asked about their main method over five years, and many have used both; if people misremember in the same way whatever their disease status, the ratio is likely to be pulled toward 1.0, which would mean the true association is stronger than the one shown. Each check changes how confidently the 165 can be quoted.
What the Exercise Shows
The prevalence ratio of 4.0 would make a striking headline. By itself it cannot tell a program whether to act, because it says nothing about how many people are affected. Carried through, the same data say that about 33 of 400 screened workers, and perhaps 165 across the county, carry a disease associated with a practice that can be changed. That number, not the ratio, is what the prevention plan later in this course is built on. Silicosis cannot be reversed once established, so those numbers also describe people whose outcome is already set; the value of the change lies in the workers who have not yet developed it.
The limitations above also shape how the result should be presented to the board. The prevalence ratio belongs in a footnote; the headline should be the count of workers, stated as an estimate with its assumptions beside it. Surveillance across California has already shown that most confirmed silicosis cases in recent years were associated with engineered stone (Flattery et al., 2025), so the county's figures add local scale to a pattern that is established, rather than discovering it.
References
Fazio, J. C., Gandhi, S. A., Flattery, J., Heinzerling, A., Kamangar, N., Afif, N., Cummings, K. J., & Harrison, R. J. (2023). Silicosis among immigrant engineered stone (quartz) countertop fabrication workers in California. JAMA Internal Medicine, 183(9), 991-998. https://doi.org/10.1001/jamainternmed.2023.3295
Fazio, J. C., Garcia, S. R., Torres, I. R., Gandhi, S. A., Cummings, K. J., Buhr, R. G., Riley, K., Ponce, N., Bustamante, A. V., & Glenn, B. (2025). Silica hazards in engineered stone countertop production: Worker experiences and challenges in Los Angeles. American Journal of Industrial Medicine, 68(10), 867-880. https://doi.org/10.1002/ajim.70010
Flattery, J., Woolsey, C., Fazio, J. C., Gandhi, S. A., Heinzerling, A., Harrison, R. J., & Cummings, K. J. (2025). Silicosis surveillance in California, 2019-2024: Tracking an epidemic. American Journal of Public Health, 115(11), 1913-1921. https://doi.org/10.2105/AJPH.2025.308225
Reading the NR 518 Week 3 instructions
The Week 3 exercise usually supplies or asks you to build a two-by-two table and calculate measures such as relative risk, odds ratio, risk difference and attributable risk, then interpret them. Some sections add confidence intervals or the population attributable fraction. The calculations themselves are routine; the grade rests on interpretation. Say which measure fits the design, what each number means in plain language and, most importantly, what it means in people. Label any constructed data as illustrative. Show your arithmetic so a reader can follow it, and connect the result to a real decision. Your section may specify the measures required and whether a table template must be used.
How the NR 518 Week 3 example is put together
The example states plainly that its data are a teaching dataset, then explains why a single screening yields prevalence rather than risk. Table 1 gives the two-by-two table with prevalences. Each measure follows in turn, and each is carried toward people: the ratio to a doubling at its lower bound, the difference to 15 more cases per 100, the excess to 33 workers, and the attributable fraction back to the same 33. The odds ratio is shown for comparison with the reason it runs higher. A county section scales the difference to 1,100 dry-cutting workers. A checks section names years of work, shop and misclassification, and published studies are cited for direction only.
NR 518 Week 3 rubric: what full points look like
Rubrics for the measures exercise usually award most points to correct calculation and correct interpretation. Graders check that the measure matches the design, for example prevalence ratios for cross-sectional data, and that each figure is explained in words. Confidence intervals, when required, should be interpreted, not just reported. The interpretation criterion rewards converting ratios into numbers of people and discussing public health meaning. Discussion of confounding or bias often earns credit under critical thinking. Clear tables and shown arithmetic help graders follow the work. Any source used to support interpretation should be cited, and constructed data should never be presented as published findings.
Common NR 518 Week 3 mistakes, and how to avoid them
The biggest mistake is stopping at the ratio and adding a comment that the exposure is important. Carry the ratio through to a difference and a count of people. Another is calling every ratio a relative risk when the data are cross-sectional; name the measure the design supports. Students also misread the odds ratio as a ratio of probabilities when the outcome is common, which inflates the result. Label constructed data clearly and never attribute it to a study. Show each calculation. Finally, discuss at least one alternative explanation, such as longer employment in the exposed group, and say how it could be checked, since a number without that discussion is easy to overstate.
Get a NR 518 Week 3 example written to your instructions
Send the sheet your section issued with its figures in place and a custom NR 518 Week 3 exercise is written to it, returned inside 24 to 48 hours, the first one free. If the population size you need for the conversion is missing from the sheet, say what setting you are in and a defensible figure is used and labeled.
More NR 518 papers and MSN samples
- NR 518 Week 1: Discussion Post, Exposure and Group
- NR 518 Week 2: Case Definition Exercise
- NR 518 Week 4: Study Design Comparison
- NR 518 Week 5: Outbreak Investigation Write-up
- NR 518 Week 6: Risk Factor Analysis
- NR 518 Week 7: Prevention Program Plan
- NR 518 Week 8: Population Health Paper
- NR 516 Week 3: Article Summary of a Flushing Trial
- NR 513 Week 3: Philosophy of Practice Statement
- NR 510F Week 3: Gap Analysis for the New NP Role
- NR 510 Week 3: Practice Barrier Brief
NR 518 Week 3 questions, answered
Which measure should I use when the sheet lets me choose?
Ask who is going to read it. A ratio answers whether an exposure is worth investigating and travels well in a sentence. A difference answers how much illness is on the table and is what anybody planning a response needs. Where the sheet allows both, give both, and name the one your recommendation rests on, because they can rank two exposures in opposite orders.
The exposure in my item is not something anybody could remove. What then?
Say so, and the item becomes a different kind of answer rather than a failed one. Age and family history place people rather than offer anything to act on, and a measure attached to one of those tells a planner where to look instead of what to change. Finish the row by naming the modifiable exposure that travels with it, which is what a later week will build on.
How much interpretation does each row need?
Two sentences usually carries it. One saying what the figure means for the group named, in words somebody outside the class would follow, and one saying what it implies for doing something. Rows that stop at the number are the commonest submission in this week and they leave the part carrying the points untouched, since the arithmetic is the part a calculator already did.
Where can I find a free NR 518 Week 3 sample paper?
The dry and wet cutting calculations above are the complete NR 518 Week 3 sample, table and interpretation included, and reading them costs nothing. Send the dataset your section provides and a worked version can be prepared for you.
Which measure should I report first in NR 518 Week 3?
Report the measure the design supports, then carry it to people. For a one-time screening, the sample leads with a prevalence ratio and immediately converts it to a difference and a count of affected workers.