Six kidney failure figures, from incidence per million to a death rate among people on dialysis, worked with every denominator named for MN505's second unit. Searches like "mn 505 unit 2 assignment example", "mn505 unit 2 sample" and "mn505 unit 2 example" land here.
Rates of Treated Kidney Failure in One County: A Six-Problem Calculation Set With the Base Named First
[Student Name]
Purdue University Global
MN505: Epidemiology and Health Promotion
Unit 2 Assignment
[Instructor Name]
[Date]
The county and all figures are composites built so the arithmetic can be checked. No real county's data are shown.
Supplied Table
County, calendar year 2025. Mid-year population: 410,000 (201,000 men, 209,000 women). Adults: 318,000, of whom 36,900 have diagnosed diabetes and 281,100 do not. Residents who began treatment for kidney failure during the year: 142 (68 with diagnosed diabetes, 74 without; 83 men, 59 women). Residents living with treated kidney failure on December 31: 1,236 (894 on dialysis, of whom 752 in-center and 142 at home, plus 342 with a working transplant). Mid-year dialysis population: 870. Deaths during the year among people on dialysis: 176.
Conventions
Every problem follows four lines: what is being asked, the operation in words, the figures placed into it, and a labeled result with one sentence on its use. A rate has three parts that must match: a numerator of events, a denominator of the population that could have those events, and a time period (Centers for Disease Control and Prevention, 2012). Ratios compare two separate quantities and have no time period. Results are rounded to one decimal place at the final step only.
Problem 1: Incidence of Treated Kidney Failure
Asked: how often did county residents begin treatment for kidney failure? Operation: new cases during the year divided by the mid-year population, times 1,000,000. Figures: 142 / 410,000 x 1,000,000. Result: 346.3 per million per year. Use: kidney failure is rare, so rates are usually expressed per million, as in national reporting (United States Renal Data System, 2023); new starts drive prevention planning.
Problem 2: Point Prevalence
Asked: how many residents were living with treated kidney failure at year end? Operation: cases on December 31 divided by the population, times 1,000,000. Figures: 1,236 / 410,000 x 1,000,000. Result: 3,014.6 per million on December 31. Use: prevalence drives how many dialysis chairs and transplant follow-up visits the county needs.
Problems 1 and 2 share the same base, the whole county, and answer different planning questions. Incidence tells a planner how many people will start treatment next year; prevalence tells her how many are already in treatment and need services now. Prevalence is much larger because people live for years with treated kidney failure (Celentano & Szklo, 2019).
Problem 3: Incidence by Diabetes Status
Asked: how much higher is incidence among adults with diagnosed diabetes? Assumption: every new start was an adult. Operation: new starts in each group divided by the adults in that group, times 100,000. Figures: with diabetes, 68 / 36,900 x 100,000 = 184.3 per 100,000; without diabetes, 74 / 281,100 x 100,000 = 26.3 per 100,000. Rate ratio: 184.3 / 26.3 = 7.0. Result: adults with diagnosed diabetes began treatment for kidney failure at about seven times the rate of adults without it. Use: supports targeting kidney screening to adults with diabetes.
Problem 4: Mortality Among People on Dialysis
Asked: how often did people on dialysis die during the year? Operation: deaths divided by the mid-year dialysis population, times 1,000. Figures: 176 / 870 x 1,000. Result: 202.3 deaths per 1,000 dialysis patients per year. Use: a population-specific mortality rate. Dividing the same 176 deaths by the whole county would give 429.3 per million, a figure that describes the county's burden but says nothing about the risk of a person on dialysis.
Problem 5: Home-to-In-Center Ratio
Asked: how many people dialyze at home for each person dialyzing in a center? Operation: home dialysis count divided by in-center count. Figures: 142 / 752. Result: 0.19, or about 1 person at home for every 5.3 in a center. Use: a ratio compares two separate groups; it is not a proportion of the whole. The proportion of dialysis patients at home is a different figure: 142 / 894 = 15.9 percent.
Problem 6: Men and Women
Asked: do men begin treatment more often than women? The count ratio, 83 men to 59 women, is 1.41, but it means little until each count sits over its own population. Operation: sex-specific incidence. Figures: men, 83 / 201,000 x 1,000,000 = 412.9 per million; women, 59 / 209,000 x 1,000,000 = 282.3 per million. Rate ratio: 412.9 / 282.3 = 1.46. Result: men began treatment at about 1.5 times the rate of women. Use: here the count ratio and the rate ratio are close because the county has similar numbers of men and women; in a population where one sex greatly outnumbered the other, the count ratio could mislead badly.
Checks on the Arithmetic
Three checks confirm the set is internally consistent. The diabetes and non-diabetes groups add to all adults: 36,900 plus 281,100 equals 318,000. The new starts by diabetes status add to all new starts: 68 plus 74 equals 142, as do the starts by sex: 83 plus 59. The year-end total splits into dialysis and transplant: 894 plus 342 equals 1,236. A set that failed any of these checks would signal an error in the supplied table or in a problem's base before any rate was interpreted.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
Centers for Disease Control and Prevention. (2012). Principles of epidemiology in public health practice: An introduction to applied epidemiology and biostatistics (3rd ed.). U.S. Department of Health and Human Services.
United States Renal Data System. (2023). 2023 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.
How this MN505 Unit 2 example is structured
Each problem runs through the same four lines: what is being asked, the operation spelled out, figures dropped into it, and a labeled result with a sentence on what it could be used for. Order follows the base. The first two problems share the whole county, which lets the set show that incidence and prevalence answer different planning questions from the same population: new starts drive prevention, while the year-end total drives how many dialysis chairs the county needs. Problem 3 changes the base twice, dividing the 68 diabetic starts by adults with diabetes and the remaining 74 by the 281,100 adults without it, and assumes every new start was an adult. Problem 4 narrows the base to people on dialysis. The last two problems are ratios, and the set explains why 83 men to 59 women means little until each count sits over its own sex-specific population.
Get an MN505 Unit 2 example written to your instructions
For a Unit 2 set built on a different disease or county, the same four-line layout carries over. Share the data table, the numbered questions and the rubric your section uses. The free first custom sample returns within 24-48h, following those instructions exactly, each problem with its base stated in words and one line on the decision it informs. 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 2 questions, answered
Why is kidney failure reported per million rather than per 100,000?
Convention, driven by how rare new cases are. At a few hundred per million, a per-100,000 figure would carry awkward decimals, so national registry reports use the larger base. Either multiplier is arithmetically correct as long as it is stated. What matters in the set is consistency within a comparison, since a rate per million set beside one per 100,000 invites a tenfold misreading.
Can incidence be split by diabetes status when diabetes causes only some of the cases?
Yes, provided the split is by whether the new patient had diagnosed diabetes, not by the cause listed on a form. The example counts 68 new starts among adults with diabetes and places them over all adults with a diagnosis. That yields a rate for the group, which is what planners need. Attributing cause is a separate and harder task that the set does not attempt.
How does a ratio differ from a proportion in these problems?
A proportion places a part over the whole it belongs to, so 131 home patients out of 884 on dialysis is 14.8 percent. A ratio compares two separate groups, so 131 to 753 is roughly one home patient for every 5.7 in centers. Neither contains time. A rate adds a period and a population at risk, which is why only some figures in the set earn that name.