| Course | NR 503 Population Health, Epidemiology and Statistical Principles |
|---|---|
| Week | 2 |
| Paper type | Epidemiologic measures write-up |
| Length | About 1,330 words, 3 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN |
| Updated | October 2026 |
Free sample paper for NR 503 Week 2
Three Measures for a Hip Fracture Prevention Program in Two Composite Counties: Osteoporosis Prevalence, Nursing Home Fall Incidence per Resident-Day, and a Crude Rate That Reversed on Age Adjustment
Student Name
College of Nursing, Chamberlain University
NR 503: Population Health, Epidemiology and Statistical Principles
Instructor Name
Month Day, Year
Three Measures for a Hip Fracture Prevention Program in Two Composite Counties: Osteoporosis Prevalence, Nursing Home Fall Incidence per Resident-Day, and a Crude Rate That Reversed on Age Adjustment
The Setting and the Comparisons
A regional health department serves two counties of the same size, 120,000 residents each, and is planning a hip fracture prevention program. Hollis County is a retirement destination with a large older population; Marren County is a younger county with a university and a manufacturing base. The figures in this paper are composite, built to illustrate the measures, and do not describe real counties.
The planning group asked three different questions, and each needs a different measure. How many older women in the clinic network need a bone-health follow-up now? How often do residents of the county's largest nursing home suffer an injurious fall? And which county has the greater underlying risk of hip fracture? This paper takes each question in turn, starting from the comparison it has to support, because the comparison decides the measure (Centers for Disease Control and Prevention [CDC], 2012).
Entry One: Prevalence, a Standing Total
The comparison. The first question is about workload today: how many women already carry the diagnosis and need follow-up. That is a question about existing cases at one moment, which is prevalence.
The measure. On January 1, 2025, the Hollis County clinic network had 9,200 women aged 65 and older on its active patient panels, and 1,840 of them had osteoporosis recorded on their problem lists. Point prevalence is 1,840 divided by 9,200, or 20.0%.
What it counts. The numerator includes every woman with the diagnosis on that date, whether it was made last week or fifteen years ago. The denominator is every woman in the age group on the panels, with or without the condition. The figure says nothing about how quickly new cases arise. It is also a prevalence of recorded diagnosis, not of disease: women who were never screened, or whose results were never added to the problem list, are counted as not having it. For a workload question, that is the right number; for a question about how common osteoporosis is among older women in Hollis, it is an undercount of unknown size.
Entry Two: Incidence, With Person-Time Because the Group Moved
The comparison. The second question compares the nursing home's injurious fall rate with published benchmarks and with its own rate next year after a prevention bundle. That is a question about new events arising over time, which is incidence.
Who could still become a case. The home has 120 beds, but the people in them changed constantly during 2024. Across the year, 212 different residents stayed there. Some were long-stay residents present all 366 days; many were short-stay rehabilitation patients who stayed two or three weeks after a hospital discharge. A resident who had already fallen remained at risk of falling again, so the event counted is each injurious fall, not each resident who fell.
Why a simple proportion fails. There were 46 injurious falls. Dividing by 212 residents gives 21.7 per 100 residents, but that figure treats a woman present for 10 days as contributing the same exposure as a man present all year. If next year's census has more short stays, the proportion would fall even if nothing about safety changed.
Person-time. The home's census records give 38,400 resident-days for 2024, counting every day each resident was in a bed. The incidence rate is 46 falls divided by 38,400 resident-days, or 1.20 injurious falls per 1,000 resident-days. That figure can be compared with next year's rate regardless of how the mix of short and long stays changes, which is exactly the comparison the home needs (Lash et al., 2021).
Labeled. The measure is an incidence rate of injurious fall events, per 1,000 resident-days, for calendar year 2024, counting repeat falls by the same resident. A reader who wants the share of residents who fell at least once would need a different count, and should not read that from this figure.
Entry Three: Crude or Age-Adjusted, Labeled
The comparison. The third question is which county carries the greater underlying risk of hip fracture, so that the department knows where to look for causes beyond age. That question compares two populations with very different age structures, which is the situation age adjustment was built for (Klein & Schoenborn, 2001).
The crude rates. In 2024, Hollis County recorded 434 hospitalizations for hip fracture among its residents and Marren County recorded 107. With 120,000 residents each, the crude rates are 361.7 and 89.2 per 100,000. Read alone, those rates say Hollis has about four times the hip fracture problem of Marren.
Table 1
Hip Fracture Hospitalizations per 100,000 by Age Group, Composite Hollis and Marren Counties, 2024
| Age group | Hollis residents | Hollis cases | Hollis rate | Marren residents | Marren cases | Marren rate | 2000 standard weight |
|---|---|---|---|---|---|---|---|
| 0 to 44 | 40,000 | 4 | 10.0 | 90,000 | 9 | 10.0 | 0.651532 |
| 45 to 64 | 30,000 | 30 | 100.0 | 22,000 | 26 | 118.2 | 0.222081 |
| 65 and older | 50,000 | 400 | 800.0 | 8,000 | 72 | 900.0 | 0.126387 |
| All ages (crude) | 120,000 | 434 | 361.7 | 120,000 | 107 | 89.2 | 1.000000 |
| Age-adjusted | 129.8 | 146.5 |
Note. Weights are the 2000 U.S. standard population collapsed to three age groups from the NCHS master list (Klein & Schoenborn, 2001). Rates per 100,000 residents. Composite data.
The age-specific rates. Table 1 breaks both counties into three age groups. At every age, Marren's rate is equal to or higher than Hollis's. The crude gap comes almost entirely from Hollis having 50,000 residents aged 65 and older against Marren's 8,000.
The age-adjusted rates. When the rate in each age band is weighted by the 2000 U.S. standard population, the result is an age-adjusted rate of 129.8 per 100,000 for Hollis and 146.5 for Marren. The order has reversed: once the difference in age structure is removed, Marren's underlying rate is about 13% higher.
Labeled, both ways. The two figures answer different questions and must never sit side by side unlabeled. The crude rate is a real count per resident: it describes the burden Hollis's hospitals and rehabilitation services actually carry. The age-adjusted rate is an instrument for comparison and describes no real place; no county has 129.8 hip fractures per 100,000, and a reader who quotes it as a count has misread it.
What the Results License
The three measures support three different decisions, and each should be used only for its own. The prevalence of 20.0% licenses a staffing and outreach plan for bone-health follow-up in the Hollis clinic network. The fall rate of 1.20 per 1,000 resident-days licenses a before-and-after comparison once the prevention bundle is in place, provided the same definition of an injurious fall is used both years.
The age-adjusted comparison licenses less than it seems to. It suggests that something beyond age, whether lower bone-density screening, fewer fall-prevention services, occupational injuries, or differences in who is hospitalized, is raising risk in Marren. It does not show it. Marren's 72 cases among residents 65 and older, and 26 among those aged 45 to 64, are small counts, and a single year's difference of this size could be chance. Before moving prevention money from Hollis to Marren, the department should pool three to five years of data and calculate confidence intervals for the age-adjusted rates.
The crude rates also keep a use. Hollis still has four times as many hip fractures, and whatever Marren's underlying risk, the beds, surgeons and rehabilitation places will be needed in Hollis. The honest recommendation is to plan services by the crude count and to investigate causes by the age-adjusted rate, and to say which number is being used for which purpose in every report.
References
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.
Klein, R. J., & Schoenborn, C. A. (2001). Age adjustment using the 2000 projected U.S. population (Healthy People 2010 Statistical Notes No. 20). National Center for Health Statistics.
Lash, T. L., VanderWeele, T. J., Haneuse, S., & Rothman, K. J. (2021). Modern epidemiology (4th ed.). Wolters Kluwer.
Reading the NR 503 Week 2 instructions
The Week 2 write-up has you calculate and interpret epidemiologic measures for a population health question. Count on supplying the question each measure answers, the calculation, what the measure counts, its limits and APA references, often with a table. Match each measure to the question it answers before calculating. Define the numerator and denominator in words. Use person-time when people enter and leave the group during the period. Show age-specific rates when comparing populations with different age structures, and label crude and adjusted figures. Say what decision each figure supports and what it does not. Name the standard population you used. Round only at the final step so the reversal is not lost to rounding.
Inside the NR 503 Week 2 example
The setting section describes the department, the two counties and the three questions. Entry one calculates osteoporosis prevalence among 9,200 women aged 65 and older on January 1, 2025, as 20.0%, and explains that it counts every existing case. Entry two explains why 46 falls divided by 212 residents misleads and calculates 1.20 per 1,000 resident-days from census records. Entry three gives crude rates of 434 and 107 hospitalizations, then Table 1's age-specific rates showing Marren equal or higher at every age, and the age-adjusted rates using Klein and Schoenborn's standard. The last section explains that prevalence supports staffing, incidence supports benchmarking and the adjusted comparison suggests where to look for causes, while crude counts still guide beds and surgeons.
Reading the NR 503 Week 2 grading rubric
Epidemiologic measures rubrics in NR 503 usually reward correct selection of measures, accurate calculation, clear definitions, appropriate interpretation, labeling and APA. Correct selection matters most, and the write-up earns it by tying each measure to the question it answers. Calculations are credited because each is shown with its numerator and denominator. Person-time scores for explaining why a simple proportion fails. Labeling earns points because crude and age-adjusted figures never appear side by side unlabeled. Interpretation is credited when each figure licenses one decision. The CDC's epidemiology primer and Lash and colleagues support the methods. Three references complete APA. The table of age-specific rates lets a grader check the reversal without trusting the prose.
Common NR 503 Week 2 mistakes, and how to avoid them
A crude rate laid next to an age-adjusted one, unlabeled, is the error this week is aimed at, and it often survives into the conclusion. Another loss is dividing events by the number of people when they were present for very different lengths of time. Students also calculate correctly but never say what decision the figure supports, or use prevalence to answer a question about new cases. Some calculation by hand is usually expected. Person-time becomes necessary when people enter and leave during the period. A standard population lets two groups with different ages be compared fairly. Send your data and an NR 503 measures write-up can match each question to its measure.
Get a NR 503 Week 2 example written to your instructions
Send the worksheet or the paper instructions your section issued, with any data table it points to, and a custom NR 503 Week 2 measure write-up is built to it and back inside 24-48h, the first one free. Tell us which measures your prompt names and the sample defends those rather than a general set.
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NR 503 Week 2 questions, answered
Do I have to calculate anything by hand?
Usually some of it, though the arithmetic stays light. What the week weighs is the choice underneath each calculation: which people belonged in the denominator, how long they were watched, and whether the resulting figure can be compared with the one beside it. A write-up with clean arithmetic and an undefended denominator marks below one that reasons carefully and rounds badly.
When does person-time become necessary?
Whenever people were not all under observation for the same length of time. Somebody who moved away in March, somebody who joined in July and somebody who died contributed different amounts, and a plain head count treats all three as full members of the group. Say in one sentence why the group did not stay intact, then count time instead of people.
What is a standard population for?
It is a borrowed age structure applied to two places so that a difference between their rates cannot be caused by one of them simply being older. The resulting figure is useful for comparison and describes no actual population, which is why an age-adjusted rate should never be quoted as the number of events that happened somewhere.
Where can I find a free NR 503 Week 2 sample paper?
This page has the complete Week 2 write-up with osteoporosis prevalence, fall incidence per resident-day, crude and age-adjusted hip fracture rates, Table 1, the decisions each supports and three references.
Why does the county comparison reverse in the NR 503 Week 2 write-up?
Hollis has far more older residents, so its crude rate is higher, but at every age Marren's rate is equal or higher, and the age-adjusted rate is 146.5 per 100,000 in Marren against 129.8 in Hollis.