Residential ZIP areas, three pooled years and smoothed rates carry the PUBH 8546 geographic analysis, which tests concentration formally before naming any part of the county as different. Searches like "pubh 8546 week 5 assignment example", "pubh8546 week 5 sample" and "pubh 8546 week 5 example" land here.
What a finished PUBH 8546 Week 5 geographic analysis looks like
Roughly three pages, built around a ranked table rather than a picture. A boundary section explains the choice: emergency records carry a residential ZIP code but no tract, so ZIP codes are converted to ZIP Code Tabulation Areas through a published crosswalk, giving each area a census population. The table lists every area with its pooled count, its crude rate, a smoothed rate pulled toward the county average in proportion to how few events it holds, and the change in rank that smoothing produced. A separate section reports a spatial scan statistic of the kind Kulldorff described, which searches circles of varying size for more events than expected and judges the strongest by simulation. Visits with post office box or missing ZIP codes are counted and set aside, with a note on who they likely are.
How a PUBH 8546 Week 5 example is structured
The analysis is sequenced so that its question fixes its unit, and its unit fixes what the estimates can mean. The question is stated as where residents' overdose visits run higher than the county's, not where overdoses occur, which rules out the hospital's own location as a unit. Units follow, with the reason ZIP-based areas beat townships for this question and the price paid, since their edges follow mail routes rather than neighborhoods. Estimation comes next, crude beside smoothed, and a paragraph explains why the smallest areas move furthest under smoothing. The scan test closes the analysis and is read carefully: a significant circle says events cluster more than chance would produce, not why, and the analysis names a shelter address and a residential program inside it as reasons unrelated to neighborhood risk. A limits paragraph records the unplaceable visits and the areas too small to rate.
Placed by home address
The question concerns residents, so each visit is placed by residential ZIP code. Assigning visits to the treating hospital would mostly show where the hospitals are.
From ZIP code to tabulation area
A published crosswalk converts ZIP codes to ZIP Code Tabulation Areas so each has a census population. The analysis notes where the two do not line up.
Crude beside smoothed
Each area's rate is shown raw and pulled toward the county mean, the pull strongest where events are fewest. Rank changes between the two columns are reported.
A scan across circles
The scan statistic searches many circle sizes for excess events and judges the strongest by simulation. A significant result is read as concentration, never as cause.
Addresses that bend the pattern
A shelter and a residential program can place many visits at one address. The analysis flags both before reading any cluster that contains them.
Where marks go in PUBH 8546 Week 5
The boundary argument is weighed first: a geographic analysis choosing ZIP-based areas because they were available, without saying what they cost or what they fit, has left the week's central decision unmade. Graders then check that visits were placed by residence. Rates by treating hospital draw a firm deduction, because they measure hospital location. Smoothing earns analytic credit when the table shows crude and smoothed rates together and the text explains why small areas move most. The scan test is marked on interpretation, and calling a significant circle a hot spot of risk without examining shelters, treatment programs or reporting differences inside it overreaches. Accounting for visits that could not be placed shows care. The crosswalk and the scan method each need a citation of their own.
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.
PUBH 8546 Week 5 questions, answered
Why smooth rates instead of reporting them as calculated?
Because small areas can post extreme rates on very few events, and such extremes usually reflect chance. Empirical Bayes smoothing pulls each rate toward the overall mean in proportion to its instability, so areas with few events move most. The crude rates stay in the table for transparency. Smoothing is a way of reading unstable numbers honestly, not a way of hiding them.
Is a scan statistic required for this week?
Rarely required, often welcome. Any method that asks whether apparent concentration exceeds what chance would produce is stronger than eyeballing a ranked list. The scan approach is widely used in surveillance because it does not fix the size of the cluster in advance. Whatever method your section uses, the interpretation matters more than the output: concentration is a pattern to explain, not an explanation.
What if my data only come at the county level?
Then the geographic question changes, and the analysis should say so. County data can support comparisons between counties, ideally with age adjustment, but cannot locate concentration inside one. You might propose the finer data that would be needed and who holds them. Treating county figures as if they described neighborhoods is the error to avoid, and graders notice it quickly.