| Course | PUBH 1000 Foundations of Public Health |
|---|---|
| Week | Week 3 |
| Paper type | Rate exercise |
| Length | About 570 words, 5 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | BS in Public Health |
| Updated | October 2026 |
Free sample paper for PUBH 1000 Week 3
More Cases, Lower Rate: A Worked Rate Exercise Comparing Reported Pertussis in Two Composite Counties and Explaining Why the Larger Count Belongs to the Healthier Population
Student Name
Walden University
PUBH 1000: Foundations of Public Health
Instructor Name
Month Day, Year
More Cases, Lower Rate: A Worked Rate Exercise Comparing Reported Pertussis in Two Composite Counties and Explaining Why the Larger Count Belongs to the Healthier Population
The Question
Two composite counties reported pertussis, or whooping cough, cases to the state last year. Riverbend County reported 54 cases and Hollis County reported 31. A local news story described Riverbend as having "the worse outbreak." This exercise calculates a rate for each county, shows every step, and explains whether the story's claim holds.
Why a Count Is Not Enough
A count tells how many people had a condition. It does not tell how common the condition is, because it ignores how many people could have had it. A rate divides the count by the population at risk over a stated period, which allows fair comparison between places of different size (Centers for Disease Control and Prevention [CDC], 2012).
The Calculations
For Riverbend, 54 divided by 180,000 is 0.0003. Multiplying by 100,000 gives 30.0 cases per 100,000 people. For Hollis, 31 divided by 62,000 is 0.0005, and multiplying by 100,000 gives 50.0 cases per 100,000 people. The multiplier of 100,000 is chosen only to make small decimals easier to read; any multiplier works if both counties use the same one.
Table 1
Pertussis Cases and Rates in Two Composite Counties, One Year
| County | Reported cases | Population | Calculation | Rate per 100,000 | |
|---|---|---|---|---|
| Riverbend | 54 | 180,000 | 54 ÷ 180,000 × 100,000 | 30.0 | |
| Hollis | 31 | 62,000 | 31 ÷ 62,000 × 100,000 | 50.0 | |
Note. Composite figures for one calendar year; population from mid-year estimates.
What the Rates Show
Riverbend had more cases because it has almost three times as many people. Hollis had fewer cases but a higher rate: a resident of Hollis was about two-thirds more likely to be reported with pertussis than a resident of Riverbend, since 50.0 divided by 30.0 is about 1.7. The news story compared counts, so it reached the wrong conclusion about where pertussis was more common.
Three Cautions
Reporting Is Not the Same as Occurrence
Rates built on reported cases depend on how often people see a clinician, get tested and are reported. If Hollis has a pediatric clinic that tests coughing children more often, its higher rate might reflect more testing rather than more disease. Surveillance data describe what the system captures, which can differ from what actually happens in a community (Lee et al., 2010).
Small Numbers Move Easily
With 31 cases, a few more or fewer would change Hollis's rate noticeably. National vital statistics reports treat rates built on fewer than 20 events as unreliable for this reason (Xu et al., 2021), and Hollis is not far above that line. A single year's rate in a small county should be read with caution and, where possible, compared with several years.
Age Matters
Pertussis is most serious in infants, and counties differ in how many young children they have. A county with more infants could have a higher rate for that reason alone. Comparing age-specific rates, or age-adjusted rates, would show whether the difference holds within age groups (CDC, 2012).
Answer
Hollis, not Riverbend, had the higher rate of reported pertussis, 50.0 versus 30.0 cases per 100,000 people. The larger count in Riverbend reflects its larger population. Before concluding that pertussis is more common in Hollis, a health department would want to check testing practices, look at several years and compare rates by age.
References
Centers for Disease Control and Prevention. (2012). Principles of epidemiology in public health practice (3rd ed.). https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/index.html
Lee, L. M., Teutsch, S. M., Thacker, S. B., & St. Louis, M. E. (Eds.). (2010). Principles and practice of public health surveillance (3rd ed.). Oxford University Press.
Xu, J., Murphy, S. L., Kochanek, K. D., & Arias, E. (2021). Deaths: Final data for 2019. National Vital Statistics Reports, 70(8), 1-87. https://doi.org/10.15620/cdc:106058
The Walden PUBH 1000 Week 3 instructions
Rate exercises in foundations courses ask students to calculate incidence or prevalence rates from counts and populations and to interpret them. Instructors expect every step shown: the formula, the numbers substituted, the multiplier and the units. Prompts often present two places with different population sizes so that students see why counts mislead. Many also ask for a short interpretation and for limitations of the data, such as reporting practices or small numbers. The deliverable is usually brief, one or two pages, with a table. Citations to a standard epidemiology reference for the definition of a rate are expected, and some instructors ask students to mention age adjustment even when they do not calculate it.
Inside the PUBH 1000 Week 3 example
The exercise opens with a claim from a composite news story and explains why a count cannot settle it. Table 1 lists cases, population, the calculation and the rate for each county. Text walks through the division and multiplication for each and explains the multiplier. A section compares the rates and gives the rate ratio. Three short sections raise cautions about reporting, small numbers, with a reference to the convention in national vital statistics, and age structure, with a note on age-specific and age-adjusted rates. An answer section states the conclusion in one paragraph and lists what a health department would check next.
Where the marks sit in the PUBH 1000 Week 3 rubric
Rate exercises earn points mainly for correct computation, shown work and sound interpretation. The sample's arithmetic is correct and every step is visible, which secures the computation criterion. Units and the multiplier are explained, which instructors check closely. Interpretation credit comes from explaining why the larger count belongs to the healthier population and giving a rate ratio in words. Limitations earn a further share because they show the student knows a crude rate is not the whole story. Clear labels in the table and correct references complete the marks. Exercises that report rates without units, or that omit the denominator, lose most of the computation credit. The rate ratio in plain words, about two-thirds more likely, is the kind of interpretation instructors want to see.
Common PUBH 1000 Week 3 mistakes, and how to avoid them
Rate exercises usually go wrong in small ways: a missing multiplier, the wrong denominator or no units. Write the formula, substitute the numbers and keep the units in every line. Use the same multiplier for both places. Compare rates with a ratio and say what it means in a sentence. Name what the figures leave unanswered, for instance testing habits or age mix. Watch for small counts. Label table columns clearly. If you have your own numbers to work through, send them and a first worked exercise is free. Round only at the end, so intermediate steps stay exact. Keep the number of decimal places the same for both rates so readers can compare them.
Get a PUBH 1000 Week 3 example written to your instructions
Forward the Week 3 prompt with the rubric your classroom posted and any figures the assignment supplies, and a worked exercise comes back using those figures. The first sample is free and lands within 24-48 hours. Numbers from your own state or county can stand in for the composites, and the arithmetic gets shown either way.
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PUBH 1000 Week 3 questions, answered
Where can I find a free PUBH 1000 Week 3 sample paper?
This page holds the full Week 3 rate exercise, calculating pertussis rates for two composite counties with every step shown and three cautions explained.
How is a rate per 100,000 worked out?
Divide the count of cases by the population at risk over the same period, then multiply the result by 100,000; for example, 54 cases in 180,000 people gives 30.0.
Why can a county with more cases have a lower rate?
Because it has a larger population. Rates divide cases by population, so a big county can have more cases but a smaller share of residents affected.
Why are rates based on small numbers unreliable?
A few more or fewer cases can change them sharply. National vital statistics reports flag rates based on fewer than 20 events as unreliable.
What is an age-adjusted rate?
A rate recalculated as if the populations being compared had the same age structure, so differences in age do not explain the comparison.