| Course | NU713 Epidemiology and Social Determinants of Population Health |
|---|---|
| Unit | Unit 6 |
| Paper type | Disparities analysis |
| Length | About 1,110 words, 7 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | Doctor of Nursing Practice (DNP) |
| Updated | September 2026 |
Free sample paper for NU713 Unit 6
Narrower in Absolute Terms, Wider in Relative Ones: A Disparities Analysis of Heart Failure Admissions Across Tract Poverty Fifths in Composite Brennard County, 2015 and 2024
[Student Name]
Purdue University Global
NU713: Epidemiology and Social Determinants of Population Health
Unit 6 Disparities Analysis
[Instructor Name]
[Date]
Brennard County, its populations and admissions are composite; figures are bracketed placeholders, computed consistently so every summary measure can be checked.
Data and Methods
Admissions are hospitalizations of county adults for heart failure, defined by AHRQ's Prevention Quality Indicator 08, from the state discharge file for [2015] and [2024] (Agency for Healthcare Research and Quality, 2024). Tracts were ranked by the share of residents below the poverty line and grouped into fifths, each fifth holding about a fifth of the county's tracts. Adult populations come from American Community Survey estimates for each year. Rates are per 100,000 adults.
The Rates
In [2015], the least poor fifth had [66,000] adults and [150] admissions, a rate of [227.3]. The second fifth had [62,000] adults and [205] admissions, a rate of [330.6]. The middle fifth had [60,000] adults and [258] admissions, a rate of [430.0]. The fourth fifth had [58,000] adults and [284] admissions, a rate of [489.7]. The poorest fifth had [54,352] adults and [338] admissions, a rate of [621.9]. The county total was [1,235] admissions.
In [2024], the least poor fifth had [67,000] adults and [104] admissions, a rate of [155.2]. The second had [62,500] adults and [141] admissions, [225.6]. The middle had [60,300] and [181], [300.2]. The fourth had [58,200] and [221], [379.7]. The poorest had [53,680] and [252], [469.4]. The county total was [899].
Every fifth improved, which is the first finding and should not be lost in what follows. The least poor fell by about [32] percent and the poorest by about [25] percent.
Four Summary Measures
Rate difference. The poorest minus the least poor fifth was [394.6] per 100,000 in 2015 and [314.2] in 2024. In absolute terms, the gap narrowed by about [80] per 100,000.
Rate ratio. The poorest divided by the least poor was [2.74] in 2015, with a 95 percent confidence interval of about [2.26 to 3.32], and [3.02] in 2024, interval [2.41 to 3.80]. In relative terms, the gap widened, although the intervals overlap, so the widening could be due to chance.
Slope index of inequality. This measure uses all five fifths, not just the extremes, by regressing each fifth's rate on its position in the cumulative population ranked by poverty, from least to most poor. It estimates the absolute difference in rates between the hypothetical least and most poor ends of the distribution. It was [472.7] per 100,000 in 2015 and [388.5] in 2024, telling the same story as the rate difference, but using the whole gradient.
Excess admissions. This is the number of admissions that would not have occurred if every fifth had the least poor fifth's rate. It was [552] in 2015 and [431] in 2024. Yet as a share of all admissions, excess admissions rose from [44.7] to [47.9] percent.
Fewer people were admitted because of the gap in 2024 than in 2015, yet the gap accounted for a larger share of all the admissions that remained.
Why the Measures Disagree
The measures disagree because of arithmetic, not error. When rates fall in every group, a smaller proportional drop in the poorest group can narrow the absolute gap while widening the relative one. Here, the least poor fifth fell by about [72] per 100,000, a drop of about [32] percent; the poorest fell by about [152] per 100,000, a larger absolute drop but a smaller proportional one, about [25] percent. The absolute gap closed because the poorest fell by more admissions per 100,000; the relative gap widened because the least poor fell by a larger share.
This pattern is common. An overview of measures of socioeconomic inequality in health showed that absolute and relative measures can move in different directions and that no single measure captures every aspect of inequality (Mackenbach & Kunst, 1997). Harper and colleagues argued further that deciding to report an absolute or a relative measure is itself a value judgment, and that reporting only one can lead to opposite conclusions about whether inequality is improving (Harper et al., 2010).
Which Conclusion Each Measure Supports
If the county's goal is to reduce the burden of heart failure admissions among its poorest residents, the absolute measures show progress: fewer excess admissions and a narrower rate difference. If the goal is to make the risk of admission independent of neighborhood poverty, the relative measures show that the county is further from that goal than before. A report that said only "the gap narrowed" or only "the gap widened" would each be accurate and misleading.
What Might Explain the Pattern
The analysis describes the change; it does not explain it. Three possibilities are worth testing in later work. First, improvements in outpatient heart failure care over the decade, such as wider use of guideline-directed medicines and nurse-led follow-up after discharge, may have reached the least poor tracts first, which is what the theory of fundamental causes would predict for any new protective technology. Second, the county's two largest cardiology practices opened offices in the western suburbs during the period, making follow-up easier for residents there. Third, the poorest tracts may have gained from a transitional care program at the safety-net hospital, which could explain their large absolute drop. Each explanation could be checked with data the county already holds, such as readmission rates by tract and the location of cardiology visits.
Limits
The analysis is ecological and compares tracts, not individuals. Poverty rankings may shift between years as neighborhoods change, so a tract in the poorest fifth in 2015 may not be there in 2024. Admissions reflect both disease and access to outpatient care, so a falling rate may reflect better primary care, changes in admission practices, or both. And the confidence interval for the rate ratio shows that the relative widening is not statistically certain.
Recommendation for Reporting
The county's report should present both absolute and relative measures, name the reference group, and include the slope index, which uses all five fifths rather than only the extremes. Reporting excess admissions as a count and as a share makes both the progress and the persistence of the gap visible to decision makers. A single sentence can carry both: heart failure admissions fell in every part of the county, and fewer admissions are attributable to poverty than a decade ago, but the poorest tracts now carry a larger share of the admissions that remain.
References
Agency for Healthcare Research and Quality. (2024). Prevention quality indicators technical specifications.
Harper, S., King, N. B., Meersman, S. C., Reichman, M. E., Breen, N., & Lynch, J. (2010). Implicit value judgments in the measurement of health inequalities. The Milbank Quarterly, 88(1), 4-29. https://doi.org/10.1111/j.1468-0009.2010.00587.x
Mackenbach, J. P., & Kunst, A. E. (1997). Measuring the magnitude of socio-economic inequalities in health: An overview of available measures illustrated with two examples from Europe. Social Science & Medicine, 44(6), 757-771. https://doi.org/10.1016/S0277-9536(96)00073-1
What the NU713 Unit 6 assignment asks for
Unit 6 asks you to analyze a health disparity across a socioeconomic gradient at two points in time. Plan to present rates for every group, compute several summary measures, including at least one absolute and one relative measure, interpret whether the disparity improved, and discuss limits. Around seven pages built around a table and a figure is typical. Faculty reward complete tables that let the reader recompute each measure, definitions of each measure in plain terms, a clear explanation when measures disagree, and a conclusion that does not hide either result. Use a measure that draws on the whole gradient, such as the slope index, not only the two extremes, and report intervals so readers can see which changes might be due to chance.
How the NU 713 Unit 6 example is put together
The data section defines admissions, the poverty fifths and the population source. Rates for all five fifths in both years follow in full sentences, with adults, admissions and rates, and a note that every fifth improved. Four summary measures are then defined and reported: the rate difference, the rate ratio with intervals, the slope index across the gradient and excess admissions as a count and a share, with the highlighted sentence on fewer excess admissions but a larger share. A section explains the arithmetic behind the disagreement, citing an overview of inequality measures and work on value judgments. The paper ties each measure to a goal, proposes three testable explanations, sets out limits and recommends how the county should report.
Reading the NU713 Unit 6 grading rubric
Disparities analyses are usually scored on data presentation, choice and calculation of measures, interpretation, discussion of limits and recommendations. Data presentation earns credit when the table allows every summary measure to be recomputed. Measures are judged on correct calculation and on including both absolute and relative forms, ideally one that uses the full gradient. Interpretation rewards a clear account of why measures disagree and what each implies. Limits are credited when the paper addresses ecological inference, shifting group membership and uncertainty. Recommendations score well when they tell decision makers how to report the disparity in a way that shows both progress and persistence. Figures should label both years and use the same scale.
Common NU713 Unit 6 mistakes, and how to avoid them
A common problem is reporting only a ratio, which can hide absolute progress, or only a difference, which can hide relative stagnation. Another is comparing only the extremes, ignoring the middle groups. Some analyses declare the disparity better or worse without acknowledging that the measures disagree. Others omit confidence intervals, so small changes look meaningful. Many papers confuse describing a change with explaining it. And excess counts are rarely reported as a share, which is where persistence often shows. Present every group, compute at least four measures, explain the arithmetic when they disagree, and write the conclusion so that both results remain visible to the reader.
Get an NU713 Unit 6 example written to your instructions
Supply the outcome and groups your Unit 6 analysis compares, with any years or data your instructor assigned, along with the rubric. The first custom sample, free and prepared to those directions within 24-48h, reports the gap both ways and explains in plain terms wherever the two measures part company. 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.
More NU713 papers and DNP samples
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- NU713 Unit 4: Fundamental Cause Paper, Colon Cancer
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- NU713 Unit 7: Age-adjusted Rate Comparison
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NU713 Unit 6 questions, answered
What is the slope index of inequality?
A measure of the absolute gap across an entire ordered distribution rather than just its two ends. Groups are ranked from most to least advantaged, each is placed at the midpoint of its cumulative population share, and the rate is regressed on that position. The slope estimates the difference between the very top and very bottom of the hierarchy, using every group's data.
Should the analysis say whether the disparity got better or worse?
It should say better by one measure and worse by another, and explain why both hold. Absolute progress means fewer excess admissions; relative stagnation means the poorest tracts are not closing their proportional distance. Instructors generally reward a conclusion that keeps both in view over one that picks whichever measure fits a preferred story.
How do I get hospital admissions data by census tract?
State hospital discharge databases often record patient residence at ZIP or tract level, available through a data request or a public query system with small cells suppressed. Access rules differ by state, and some require an application. For a course assignment, a published county report or your instructor's dataset may be the practical source; state which one you used.
Where can I find a free NU713 Unit 6 sample paper?
This page carries the full NU713 Unit 6 disparities analysis, with rates for all five poverty fifths in two years, four summary measures, why they disagree, which goal each serves, testable explanations, limits and reporting advice, with three references. Your own admissions data can be analyzed the same way on request.
Why did the gap narrow and widen at once in NU713 Unit 6?
The poorest fifth fell by more admissions per 100,000 than the least poor fifth, narrowing the absolute gap, but by a smaller percentage, about 25 against 32 percent, so the ratio between them grew.