| Course | NU 602 Epidemiology, Research, and Theory |
|---|---|
| Unit | Unit 3 |
| Paper type | Age-standardized rate comparison |
| Length | About 720 words, 5 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MSN |
| Updated | October 2026 |
Free sample paper for NU 602 Unit 3
Two Counties, Reversed: Direct Age Standardization of All-Cause Mortality in a Retirement County and a Young Commuter County
Student Name
Department of Nursing, Herzing University
NU 602: Epidemiology, Research, and Theory
Instructor Name
February 4, 2026
Two Counties, Reversed: Direct Age Standardization of All-Cause Mortality in a Retirement County and a Young Commuter County
The Crude Comparison
Two composite counties report their all-cause deaths for one year. County A, on a lake with many retirees, had 865 deaths in a population of 80,000, a crude death rate of 10.8 per 1,000. County B, a suburban commuter county, had 476 deaths in a population of 95,000, a crude rate of 5.0 per 1,000. On these figures, County A's death rate is more than twice County B's, and a reader would conclude that County A has a serious health problem. That conclusion is premature, because the two counties have very different age structures and death rates rise steeply with age.
The Age Structures
The table shows each county's population and deaths by age group.
| Age group | County A population | County A share | County A deaths | County B population | County B share | County B deaths |
|---|---|---|---|---|---|---|
| 0 to 44 | 30,000 | 37.5% | 15 | 60,000 | 63.2% | 36 |
| 45 to 64 | 25,000 | 31.25% | 100 | 25,000 | 26.3% | 110 |
| 65 and older | 25,000 | 31.25% | 750 | 10,000 | 10.5% | 330 |
| Total | 80,000 | 100% | 865 | 95,000 | 100% | 476 |
What the Age Structures Show
Almost a third of County A's population is 65 or older, compared with about a tenth of County B's. Since most deaths in any population occur among older adults, a county with more older adults will have more deaths per 1,000 people even if each age group is as healthy as elsewhere. The crude rate mixes two things: the risk of death at each age, and the proportion of the population at each age.
Age-Specific Rates
Dividing deaths by population within each age group gives rates per 1,000:
| Age group | County A rate per 1,000 | County B rate per 1,000 |
|---|---|---|
| 0 to 44 | 0.5 | 0.6 |
| 45 to 64 | 4.0 | 4.4 |
| 65 and older | 30.0 | 33.0 |
The Reversal Appears
In every age group, County B's death rate is higher than County A's, by 10% to 20%. The crude comparison pointed in the wrong direction. County A's high crude rate comes from its age structure; within each age group, its residents die at slightly lower rates than County B's residents of the same age. This pattern, in which a comparison reverses once a third variable is taken into account, is an example of confounding by age.
Direct Standardization
To summarize the age-specific rates in a single comparable figure, each county's rates are applied to one standard population. Direct standardization suits this comparison because both counties are large enough to have stable age-specific rates; when a population is small and its age-specific rates are unreliable, indirect standardization, which applies standard rates to the population's own age structure, is the usual alternative (Naing, 2000). Here the standard is the two counties combined: 90,000 people aged 0 to 44, 50,000 aged 45 to 64, and 35,000 aged 65 and older, a total of 175,000. The national 2000 standard population is used for official U.S. rates, and any standard gives a valid comparison as long as both rates use the same one (Klein & Schoenborn, 2001).
Expected deaths if County A's rates applied to the standard: 90,000 times 0.0005 = 45; 50,000 times 0.004 = 200; 35,000 times 0.030 = 1,050; total 1,295. Age-adjusted rate: 1,295 / 175,000 = 7.4 per 1,000.
Expected deaths if County B's rates applied to the standard: 90,000 times 0.0006 = 54; 50,000 times 0.0044 = 220; 35,000 times 0.033 = 1,155; total 1,429. Age-adjusted rate: 1,429 / 175,000 = 8.2 per 1,000.
What Survives the Adjustment
After adjustment, County B's rate is about 10% higher than County A's (8.2 compared with 7.4 per 1,000), a ratio of about 1.10, where the crude rates suggested County A's was more than twice County B's. The adjusted rates are not the counties' actual death rates; they are hypothetical rates that answer the question, what would each county's death rate be if both had the same age structure? That makes them suitable for comparison and unsuitable for other uses, such as estimating how many deaths a county's health department should expect next year, for which the crude rate and the actual population are the right figures (Celentano & Szklo, 2019).
Limits and Next Steps
Three age groups are broad, and within the 65-and-older group, County A's retirees may be younger on average than County B's older residents, which could hide some remaining confounding by age; finer age groups would give a more reliable adjustment. A 10% difference in adjusted rates between populations of this size could also arise from chance variation in a single year, so several years of data should be combined before concluding that County B has a real excess. If the excess holds, the next question is which causes of death drive it, which cause-specific, age-adjusted rates would answer.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
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.
Naing, N. N. (2000). Easy way to learn standardization: Direct and indirect methods. Malaysian Journal of Medical Sciences, 7(1), 10-15.
Reading the NU 602 Unit 3 instructions
Unit 3 asks you to compare rates between two populations with different age structures using standardization. Expect to calculate crude rates, present age-specific rates, choose a standard population, compute adjusted rates by direct or indirect standardization and interpret the result. Many versions ask you to explain why the crude and adjusted comparisons differ. Check whether your section specifies direct or indirect standardization; direct is usual when age-specific rates are available for both groups. The 2000 US standard population is the common choice for American data. Show your table and arithmetic so the grader can follow each step. Interpretation matters as much as calculation, so explain what the adjusted figure means and what it does not tell you.
How the NU 602 Unit 3 example is put together
The comparison opens with the crude rates, which appear to show County A doing worse. A table of age structures follows, and a short paragraph explains why a county with more older residents will have more deaths regardless of health. Age-specific rates are calculated in a second table, and the reversal is stated plainly: County B is higher in every group. The standardization section explains the method in two sentences, then applies each county's rates to the standard population and sums expected deaths. Adjusted rates and their ratio are reported. The section on what survives adjustment interprets the 10 percent difference. Limits close the piece, noting broad age bands and residual differences within the oldest group that could still affect the comparison.
Reading the NU 602 Unit 3 rubric
The Herzing rubric for a standardization exercise usually covers crude rate calculation, age-specific rates, the standardization method, interpretation and presentation. Crude and age-specific criteria reward correct arithmetic with clear units. The method criterion gives full credit when the standard population is named and applied correctly, with expected deaths shown. Interpretation is often weighted heavily, looking for an explanation of why crude and adjusted results differ and what the adjusted ratio means. Limits may be scored within interpretation. Presentation points go to readable tables and consistent rounding. References in APA 7 should include a methods source, such as Naing's guide or the Gordis text.
Common NU 602 Unit 3 mistakes, and how to avoid them
In Unit 3 the most frequent error is applying the standard population incorrectly, for example using one county's population as weights for the other. Another is reporting adjusted rates without explaining that they are only for comparison and are not real rates. Students often skip age-specific rates and jump to adjustment, which hides the reversal. Expected deaths are sometimes not shown, so the grader cannot check the sum. Choosing an unusual standard population without explanation causes confusion. Interpretation that says County B is unhealthier without qualification overstates the result. Finally, address limits such as broad age groups, because adjustment only removes differences in the age bands you used.
Get a NU 602 Unit 3 example written to your instructions
Send the Unit 3 instructions and the rubric from your NU 602 classroom, plus the two populations your section compares and any table it provides. We write a custom example that reports the crude figures first, displays the strata, adjusts to a named standard with the weighting shown and interprets what remains, returned in 24 to 48 hours. The first custom sample is free.
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NU 602 Unit 3 questions, answered
Direct or indirect standardization for this one?
Follow your instructions where they specify. Where the choice is yours, direct standardization suits populations whose stratum-specific rates you actually hold, and indirect suits situations where those rates are unstable or missing and you have expected counts instead. Say which you used and why the data pushed you there, since that sentence often carries more credit than the computation itself.
Which standard population should I weight against?
Use whichever your instructions or your data source name, and if nothing is specified, take a published standard your reader will recognize rather than inventing weights. Cite it. The absolute adjusted values shift when the standard changes, so the number means nothing to anybody who cannot see which population you weighted against and over what age bands.
Does adjustment prove the populations really differ?
Only on the variable you adjusted for. Age standardization removes age as an explanation and leaves every other difference exactly where it was, including smoking, deprivation, occupational exposure and access to diagnosis. A residual gap is a finding worth reporting and it is not a cause, so name the confounders still in play and say how they would likely bend the result.
Where can I find a free NU 602 Unit 3 sample paper?
This page shows the whole NU 602 Unit 3 two-county comparison for free, crude and age-specific rates, the standardization and the interpretation. If your data or method differ, a custom comparison can be worked from them.
What is the difference between direct and indirect age standardization?
Direct standardization applies each population's age-specific rates to one standard population. Indirect standardization applies standard rates to each population's age structure, useful when its own rates are unstable. This sample uses the direct method.