| Course | NR 550 Population Health Disparities: Local, National, and Global Perspectives |
|---|---|
| Week | 3 |
| Paper type | Comparator analysis |
| Length | About 1,120 words, 2 pages |
| Format | APA 7 student paper |
| School | Chamberlain University |
| Program | MSN, Population Health |
| Updated | September 2026 |
Free sample paper for NR 550 Week 3
Comparator Analysis: One East Side Asthma Emergency Visit Rate Set Against Six Yardsticks, Each Sourced at the Same Depth, and Why the Gap Ranges From Threefold to Thirteenfold
Student Name
College of Nursing, Chamberlain University
NR 550: Population Health Disparities: Local, National, and Global Perspectives
Instructor Name
Month Day, Year
Comparator Analysis: One East Side Asthma Emergency Visit Rate Set Against Six Yardsticks, Each Sourced at the Same Depth, and Why the Gap Ranges From Threefold to Thirteenfold
The Rate Being Compared
The rate stays fixed throughout this analysis: 260.7 emergency department visits for asthma per 10,000 children aged 0 to 17 per year, among children living in six east side census tracts of our county, pooled over 2021 to 2023, from the state emergency department discharge dataset. The local profile established this figure and its limits. What this analysis changes is what the rate is placed against. A disparity is a distance between two figures, and the distance depends as much on the second figure as on the first (Braveman, 2006).
Where Each Yardstick Comes From
A reference figure is only a yardstick if it measures the same thing over the same kind of unit. Each of the six below was checked for source, years, age range, outcome definition and geographic level.
Rest of the county. Same dataset, same years, same age range and definition, the 46 tracts outside the east side combined: 39.7 per 10,000.
Whole county. Same dataset and definition, all 52 tracts: 78.0 per 10,000. It includes the east side's own children.
Lowest-rate area in the county. The rural townships, same dataset and definition: 20.0 per 10,000.
The state. The state health department's published rate for the same years, age range and diagnosis definition, from the same discharge dataset: 58.0 per 10,000.
Similar neighborhoods elsewhere in the state. A group of 31 tracts in two other cities in the state, selected by the state's data office because they share the east side's housing age and child poverty profile, same dataset and definition: 151.0 per 10,000.
The east side's own past. The same six tracts in 2016 to 2018, same dataset and definition: 292.0 per 10,000.
One candidate yardstick was rejected. A national estimate of childhood asthma emergency visits from a federal survey of hospital visits uses a sample of hospitals, a different age grouping and a different way of identifying asthma. Placed beside our count-based rate, it would appear to be a comparison, but the two figures are not measured the same way.
The Answers Side by Side
Table 1 gives the distance from the fixed rate to each yardstick, both as a ratio and as an absolute difference.
Distance from the east side rate (260.7 per 10,000) to six reference figures
Table 1
| Yardstick | Reference rate | Ratio | Difference per 10,000 |
|---|---|---|---|
| Rest of county | 39.7 | 6.6 | 221.0 |
| Whole county | 78.0 | 3.3 | 182.7 |
| Lowest county area (rural) | 20.0 | 13.0 | 240.7 |
| State | 58.0 | 4.5 | 202.7 |
| Similar neighborhoods in the state | 151.0 | 1.7 | 109.7 |
| East side, 2016 to 2018 | 292.0 | 0.89 | -31.3 |
What Each Distance Answers
Each yardstick answers a different question, and the choice should follow the question rather than the size of the gap it produces.
Rest of the county (6.6 times) answers: how much more often do east side children use the emergency department for asthma than other children in the same county, served by the same hospitals and health department? This is the most direct measure of the local disparity, and it is the one I would use to argue for local action.
Whole county (3.3 times) understates the gap, because the east side is part of the county average. It is useful only for describing how far the east side stands from the figure the county usually reports.
Lowest area (13 times) answers what the east side's rate could be if it matched the county's best-off area. It is the largest figure and the most rhetorically striking, but it compares with rural townships whose housing, air and access differ in many ways. It sets an aspiration rather than a realistic target.
The state (4.5 times) places the local problem in context for state-level funding arguments.
Similar neighborhoods (1.7 times) is the most revealing yardstick. Neighborhoods with the same housing age and poverty still have a much lower rate, which suggests that something specific to our east side, beyond its housing and poverty profile, is contributing. That finding points the structural analysis toward local factors, such as the truck route and access to care, rather than only toward housing.
The east side's own past (0.89) shows that the rate has fallen by about 11% in five years. Without this yardstick, the analysis could not tell whether things are improving; with it, the question becomes why the decline is not faster.
Ratios and Differences
Ratios and absolute differences can tell different stories. The east side's ratio to the state is 4.5, and its absolute excess over the state is about 203 visits per 10,000 children per year, which for 7,800 children means about 158 excess visits a year. Absolute differences translate most directly into workload and cost; ratios travel better across places with different baseline rates. Reporting both prevents either from being used selectively.
Conclusion
The same east side rate is threefold, fourfold, sevenfold or thirteenfold higher depending on the comparison. For the purpose of planning local action, the rest of the county is the right yardstick: it is sourced identically and shares the same health system. The comparison with similar neighborhoods elsewhere in the state is the most useful for understanding causes, and the comparison with the east side's own past is essential for judging progress. Choosing geographic comparisons deliberately, rather than taking whichever figure a source supplies, is part of the method of disparity monitoring (Krieger et al., 2003).
The comparisons also stay at the level of areas on purpose. Rose (1985) observed that the causes of differences between populations can differ from the causes of differences between individuals within a population. A rate compared across places asks why one place has more emergency visits than another, which is a different question from why one child has more than a classmate, and it calls for different answers.
A last practical point concerns presentation. When this analysis goes to the county board, the table will be shown in full rather than reduced to a single headline ratio. Board members will then see that the size of the gap depends on the comparison, and they can judge for themselves which question each ratio answers. A single number chosen for impact would invite the reasonable objection that a different comparison makes the problem look smaller.
That openness also protects the analysis from charges of exaggeration.
References
Braveman, P. (2006). Health disparities and health equity: Concepts and measurement. Annual Review of Public Health, 27, 167-194. https://doi.org/10.1146/annurev.publhealth.27.021405.102103
Krieger, N., Chen, J. T., Waterman, P. D., Soobader, M.-J., Subramanian, S. V., & Carson, R. (2003). Choosing area based socioeconomic measures to monitor social inequalities in low birth weight and childhood lead poisoning: The Public Health Disparities Geocoding Project (US). Journal of Epidemiology and Community Health, 57(3), 186-199. https://doi.org/10.1136/jech.57.3.186
Rose, G. (1985). Sick individuals and sick populations. International Journal of Epidemiology, 14(1), 32-38. https://doi.org/10.1093/ije/14.1.32
The Chamberlain NR 550 Week 3 instructions
The third week asks you to compare a local health indicator with appropriate benchmarks. Instructions commonly request the local rate, comparison rates from other levels or places, calculated differences and an interpretation of what the comparisons show. Keep the measured rate fixed and vary only the comparison. Source every yardstick separately, checking that it uses the same outcome definition, age range, years and kind of unit; reject any that do not match. Present all the distances together, as both ratios and absolute differences. Explain which question each comparison answers, and choose the right one for your purpose rather than the one with the largest gap. Include your area's own past rate if you can, since that shows change. Disparity measurement literature, in APA 7, supports the choices.
Inside the NR 550 Week 3 example
The analysis opens by fixing the east side rate and citing work on how disparities depend on comparisons. A section sources six yardsticks, each with its rate, and explains why a national survey-based estimate was rejected. Table 1 shows each yardstick's rate, the ratio and the absolute difference. A section explains what each distance answers: the rest of the county for local action, the county average as an understatement, the lowest area as an aspiration, the state for funding arguments, similar neighborhoods as evidence of local causes and the past rate as evidence of change. A section explains ratios versus differences, converting the state difference into about 158 excess visits a year. The conclusion names the right yardstick for each purpose.
Reading the NR 550 Week 3 grading rubric
Comparator analyses are typically marked on appropriate choice and sourcing of benchmarks, accurate calculations, clear presentation, thoughtful interpretation and awareness of how comparisons shape conclusions. Holding one rate fixed while changing the yardstick shows understanding of how disparities are constructed. Checking that each benchmark is measured the same way, and rejecting one that is not, demonstrates rigor. Presenting ratios and differences together prevents selective reporting. Explaining which question each comparison answers, and choosing by purpose rather than by size, shows mature judgment. Including the area's own past adds evidence of change. Citations to disparity measurement work in APA 7 support the analysis.
NR 550 Week 3 help: mistakes that cost points
Analyses often compare a local rate with whatever state or national figure is easiest to find, without checking whether it is measured the same way. Check definitions, years, ages and sources. Another weakness is reporting only the most dramatic ratio. Show all comparisons together. Some students use the county average as the comparison for a subarea that is included in it, which shrinks the gap. Avoid presenting ratios without absolute differences, or the reverse. Explain what each comparison means rather than listing numbers. Include a past rate if available, since a disparity that is narrowing calls for different action from one that is widening. Double-check every calculation, because a comparator paper stands or falls on its arithmetic.
Get a NR 550 Week 3 example written to your instructions
Send the yardstick list your classroom requires, or say you have not settled on one and a defensible set gets picked and justified inside the example. A custom analysis is written to your prompt and returned inside 24 to 48 hours, the first one free, with a source shown under every reference figure.
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NR 550 Week 3 questions, answered
How many yardsticks should I use?
Check your prompt, since several classrooms fix the number. Where it is left open, three is common and the count matters less than the kinds. Three neighboring towns are one yardstick used three times. A containing area, a matched set and a best achieved result are three genuinely different questions, and only the second arrangement can show a gap changing size when the reference changes.
What makes a like-for-like set defensible?
Published criteria. The set is built on characteristics you state in advance, such as population size, age make-up and whether the places are urban or rural, with the list printed so a reader can dispute it. A set assembled from whatever sits within a short drive is not matched on anything, and a marker who notices that treats every distance built on it as untested.
Should I carry the biggest gap forward?
Not automatically. The largest distance often comes from the flimsiest pairing, either because the reference is a place nothing here resembles or because one of the two figures rests on very few events. Carry the gap you can defend under questioning, and if that is not the biggest one, say in a line why the biggest was set aside. That sentence usually earns more than the extra size would have.
Where can I find a free NR 550 Week 3 sample paper?
The six-yardstick comparator analysis is shown above as the NR 550 Week 3 sample, complete on this page. If your indicator is different, tell us the rate and its source and a comparison against matched benchmarks can be written.
Which comparison should NR 550 Week 3 use?
The one that fits the question. The sample uses the rest of the county for local action, similar neighborhoods to probe causes and the area's own past to judge progress.