| Course | PUBH 8546 Advanced Analysis of Community Health Data and Surveillance in Public Health |
|---|---|
| Week | Week 5 |
| Paper type | Geographic analysis |
| Length | About 550 words, 4 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | PhD in Public Health |
| Updated | October 2026 |
Free sample paper for PUBH 8546 Week 5
Test Before Naming: A Geographic Analysis of Cold-Related Emergency Visits Across a Composite County's ZIP Code Areas, Using Three Pooled Winters, Smoothed Rates and a Formal Check for Concentration
Student Name
Walden University
PUBH 8546: Advanced Analysis of Community Health Data and Surveillance in Public Health
Instructor Name
Month Day, Year
Test Before Naming: A Geographic Analysis of Cold-Related Emergency Visits Across a Composite County's ZIP Code Areas, Using Three Pooled Winters, Smoothed Rates and a Formal Check for Concentration
Choosing the Areas
Emergency records in Northwater County, a composite northern county, carry a residential ZIP code but no census tract. ZIP codes are delivery routes, not areas with populations, so each was converted to a ZIP Code Tabulation Area through a published crosswalk, giving every area a census population. Three winters were pooled, December through March, because a single winter produced too few cold-related visits in most areas for a stable rate.
The Ranked Table
Table 1
Cold-related emergency visits by area, three winters pooled (illustrative)
| Area | Population | Visits | Crude rate per 10,000 per winter | Smoothed rate | Weight on own data | |
|---|---|---|---|---|---|
| East End A | 5,200 | 19 | 12.2 | 10.0 | 0.68 | |
| East End B | 3,900 | 13 | 11.1 | 9.0 | 0.61 | |
| Lakeside Township | 7,400 | 17 | 7.7 | 7.1 | 0.75 | |
| Pike Falls | 6,100 | 9 | 4.9 | 5.1 | 0.71 | |
| West Farms | 2,300 | 3 | 4.3 | 5.0 | 0.48 | |
| College City South | 12,600 | 14 | 3.7 | 4.0 | 0.84 | |
| College City North | 14,800 | 15 | 3.4 | 3.7 | 0.86 | |
| North Ridge | 1,700 | 0 | 0.0 | 3.3 | 0.41 | |
County rate 5.6 per 10,000 per winter (90 visits, 54,000 residents). Counts are illustrative.
Why Smooth
Crude rates in small areas swing widely. North Ridge's rate of zero and West Farms' 4.3 rest on zero and three visits; one more visit in North Ridge would move its rate from zero to about two per 10,000. Empirical Bayes smoothing pulls each area's rate toward the county average in proportion to how little information the area holds (Clayton & Kaldor, 1987). The last column shows the weight each area's own data receive: the two College City areas, with large populations, keep most of their own rate, while North Ridge keeps less than half. Smoothing changes the order little at the top, which is reassuring, but it lifts North Ridge from last to near the others, a reminder that its zero was mostly chance. For any area cited by name, exact Poisson intervals will accompany the crude rate (Ulm, 1990).
Testing for Concentration
Before naming any area as different, the analysis asked whether rates vary across areas more than chance would produce. A chi-square test comparing each area's observed visits with the number expected at the county rate gave a value of 29.9 on 7 degrees of freedom (p < .001), so variation across areas is unlikely to be chance alone. A spatial scan statistic, which searches for clusters of adjacent areas with elevated rates and adjusts for the many windows examined (Kulldorff, 1997), identified the two East End areas together as the most likely cluster.
What Can Be Said
Cold-related visits are concentrated in the East End, where both areas have smoothed rates nearly twice the county rate, and the concentration survives a formal test. Lakeside Township is also above average. The College City areas are below average, perhaps because many students leave in winter. These patterns describe where visits come from, not why, and they should be reported with the warming centers, shelters and housing conditions in each area before anyone draws conclusions about the residents. The analysis will be repeated after next winter to see whether the East End pattern persists.
References
Clayton, D., & Kaldor, J. (1987). Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics, 43(3), 671-681. https://doi.org/10.2307/2532003
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26(6), 1481-1496. https://doi.org/10.1080/03610929708831995
Ulm, K. (1990). Simple method to calculate the confidence interval of a standardized mortality ratio (SMR). American Journal of Epidemiology, 131(2), 373-375. https://doi.org/10.1093/oxfordjournals.aje.a115507
What the PUBH 8546 Week 5 instructions ask for
Geographic analysis assignments ask students to describe how a condition varies across places in their jurisdiction. Prompts usually require choosing a geographic unit, calculating rates, dealing with small-area instability and deciding whether apparent differences are real. Many instructors ask for a formal test of clustering or heterogeneity before any area is singled out. Some prefer tables to maps for small populations. Analyses that rank areas by crude rates based on a handful of events, or that name neighborhoods as high-risk without testing, tend to lose credit, since such claims can mislead decision makers and stigmatize residents.
How the PUBH 8546 Week 5 example is put together
The analysis begins by explaining why ZIP codes were converted to tabulation areas and why three winters were pooled. A ranked table shows population, visits, crude rate, smoothed rate and the weight on each area's own data. A section explains smoothing, showing how an area with zero visits moves toward the county average and noting exact intervals for any named area. A testing section reports a chi-square test of heterogeneity and a spatial scan statistic that identified the two East End areas together. A closing section states what can be said and calls for local context before conclusions. The table orders areas by crude rate but shows the smoothed rate beside each, so the reader can see where smoothing changes the picture and where it does not. Population and visit counts are shown in every row, so no rate appears without its basis.
Reading the PUBH 8546 Week 5 rubric
Graders generally assess the choice of geographic unit, handling of small numbers, appropriate testing and careful interpretation. Explaining the ZIP-to-tabulation-area conversion earns credit because it shows attention to how populations are assigned. Showing smoothing weights makes the method transparent. Testing for heterogeneity and clusters before naming areas demonstrates statistical discipline. Interpretation that separates where events occur from why, and that acknowledges possible stigma, shows professional judgment. Papers that pair numbers with plain explanations, so a nonstatistician can follow them, usually receive higher marks. A closing paragraph that lists contextual factors, such as shelters, housing and warming centers, to examine next shows that the analysis is a starting point for local inquiry rather than a verdict on residents.
Common PUBH 8546 Week 5 mistakes, and how to avoid them
Choose a geographic unit your records can actually support and that has population counts. Pool time periods if single periods give too few events. Calculate crude rates, then smoothed rates, and look at how much each area moves. Before calling any area different, test whether variation across areas exceeds chance, and consider a cluster test. Report intervals for any area you name. Describe local context before drawing conclusions about residents. If you have small-area counts and are unsure how to analyze them, we can prepare a sample analysis with your areas.
Get a PUBH 8546 Week 5 example written to your instructions
Share the Week 5 assignment, its rubric and the geographic levels your data actually come in. The analysis argues for one unit, rates each area crude and smoothed, and tests concentration before naming anywhere, delivered in 24-48h with the first sample free. No real ZIP area appears in it; your boundaries depend on what your records carry.
More PUBH 8546 papers and PhD in Public Health samples
- PUBH 8546 Week 1: Discussion Post
- PUBH 8546 Week 2: Case Definition Memo
- PUBH 8546 Week 3: Data Source Appraisal
- PUBH 8546 Week 4: Denominator Note
- PUBH 8546 Week 6: Small numbers memo
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- PUBH 8546 Week 8: Threshold memo
- PUBH 8546 Week 9: Signal assessment
- PUBH 8546 Week 10: Peer critique
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PUBH 8546 Week 5 questions, answered
Where can I find a free PUBH 8546 Week 5 sample paper?
The complete Week 5 geographic analysis is shown above, for cold-related visits across a composite county.
Why convert ZIP codes to tabulation areas?
ZIP codes are delivery routes without populations, while tabulation areas have census counts.
Why smooth small-area rates?
Crude rates from a few events swing widely; smoothing pulls them toward the county average in proportion to their uncertainty.
How was concentration tested?
With a chi-square test of heterogeneity and a spatial scan statistic.
Which areas form a cluster?
The two East End areas, whose smoothed rates are nearly twice the county rate.