Drawn at census-tract level, the NU505 Unit 7 community profile of a composite two-tract neighborhood expresses its gaps as prevalence ratios and names the one to target. Searches like "nu 505 unit 7 assignment example", "nu505 unit 7 sample" and "nu505 unit 7 example" land here.
Community Health Profile: Stillwell Crossing, Two Census Tracts Measured Against Their County in Prevalence Ratios
[Student Name]
Purdue University Global
NU505: Clinical Epidemiology and Population Health Promotion
Unit 7 Assignment
[Instructor Name]
[Date]
Stillwell Crossing and its figures are composite; the data sources named are real public sources.
Why Ratios
Every comparison in this profile is written as a ratio, the tract figure divided by the county figure. A ratio lets a reader rank gaps across measures that use different units, percentages of adults, shares of households, cost burdens, and see at once which gap is largest. Ratios above 1 mean the tracts fare worse; the size of the ratio says how much worse.
Population
Stillwell Crossing is two adjoining census tracts with about 7,940 residents, a figure from the Census Bureau's five-year American Community Survey estimates that carries a margin of error near 610 (U.S. Census Bureau, 2023). The tracts are younger than the county, with a median age of about 33 against 38, and about 41 percent of residents speak a language other than English at home, most often Spanish. The younger age mix matters for reading the diabetes figure below: a younger population would be expected to have less diabetes, not more, so the gap is not an artifact of age.
Tract Against County
Each entry gives the tract value, the county value, the ratio, the source type and the year.
1. Diagnosed diabetes, adults: 17.1 against 10.4 percent; ratio 1.64. Model-based small-area estimate, 2022.
2. No leisure-time physical activity, adults: 36 against 23 percent; ratio 1.57. Model-based estimate, 2022.
3. High blood pressure, adults: 41 against 31 percent; ratio 1.32. Model-based estimate, 2021.
4. Obesity, adults: 42 against 34 percent; ratio 1.24. Model-based estimate, 2022.
5. No health insurance, adults 18 to 64: 18 against 9 percent; ratio 2.0. Survey estimate, five-year.
6. Households with no vehicle: 27 against 8 percent; ratio 3.4. Survey estimate, five-year.
7. Renter households spending 30 percent or more of income on housing: 48 against 31 percent; ratio 1.55. Survey estimate, five-year.
8. Adults with a routine checkup in the past year: 68 against 76 percent; ratio 0.89. Model-based estimate, 2022.
The health measures come from CDC's PLACES project, which produces model-based small-area estimates for census tracts from national survey data and demographic information (Greenlund et al., 2022). Model-based estimates are not local measurements; they predict what the prevalence is likely to be given who lives in a tract, and their intervals are wider than a county survey figure's. For diabetes and inactivity the tract and county intervals do not overlap, so those gaps are unlikely to be artifacts of the model. For obesity and checkups the intervals come close, and those ratios should be read with more caution. None of the estimates is a count of residents with a condition.
Food and Transport
The food and transport data explain the ratios rather than adding new ones. Both tracts are classified as low income and low access at half a mile in the USDA Food Access Research Atlas, meaning a substantial share of residents live more than half a mile from a supermarket (U.S. Department of Agriculture, Economic Research Service, 2025). The nearest full-service grocery store is 1.4 miles away along a four-lane road with no continuous sidewalk. With 27 percent of households lacking a vehicle, that distance is covered by one bus route with a stop every 40 minutes on weekdays and hourly on weekends. The two parks in the tracts have no lighting after dark, and a local community health worker described them as unused in the evening, the only time many shift workers could exercise.
Assets
A profile that lists only deficits misses what a program can build on. Stillwell Crossing has a community center run by the city parks department with a gymnasium used mostly for youth basketball, two churches with fellowship halls and active women's groups, a federally qualified health center at its northern edge that already sees about a third of the tracts' adults, and an elementary school whose parent group meets monthly. The health center employs two community health workers who live in the tracts and speak Spanish. These assets matter for the priority below, because a program that needs a new building, new staff or a new language will take years, while one that uses the gymnasium in the evening and the health workers' existing trust could start within months.
What Residents Said
Two community health workers were interviewed for this profile, identified here only by role, and a listening session was held after a Sunday service at one of the churches. Three themes recurred. Residents described long and changing shift schedules at two nearby distribution warehouses, which make fixed-time classes hard to attend. They described the walk to the grocery store as unsafe in winter darkness. And several older residents said that diabetes "runs in the family" and that there was little to be done about it, a belief the next unit's plan will have to address directly. These accounts are not measurements, but they explain why the inactivity ratio is as high as it is and point to specific barriers.
A Geography Mismatch
One mismatch needs its own paragraph. Hospital discharge data, including admissions for diabetes complications, are released only by ZIP code, and the ZIP code that contains Stillwell Crossing also contains three other tracts with higher incomes. Using ZIP-level discharge rates here would let county-sized data pose as neighborhood data and would probably understate the tracts' burden. The profile therefore uses discharge data only to describe the ZIP code, and says so, rather than attributing its rates to the two tracts.
Priority
Three findings stand out: the diabetes ratio of 1.64 in a population younger than the county; inactivity at 1.57, the largest gap among modifiable behaviors; and transport and food access barriers that sit behind both. The priority this profile chooses is leisure-time physical inactivity among adults with prediabetes. It is modifiable, it is plausibly connected to the diabetes gap, and the barriers behind it, lighting, walking routes and schedules, are ones a community program can address. Insurance and grocery access matter as much, but they depend on employers, insurers and developers; inactivity is the gap where a nurse-led program on existing assets can move first. Randomized evidence also supports acting at the prediabetes stage: in the Diabetes Prevention Program, the group assigned to intensive lifestyle change had 58 percent fewer new cases of diabetes than the placebo group over about three years (Diabetes Prevention Program Research Group, 2002).
References
Diabetes Prevention Program Research Group. (2002). Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. New England Journal of Medicine, 346(6), 393-403. https://doi.org/10.1056/NEJMoa012512
Greenlund, K. J., Lu, H., Wang, Y., Matthews, K. A., LeClercq, J. M., Lee, B., & Carlson, S. A. (2022). PLACES: Local data for better health. Preventing Chronic Disease, 19, E31. https://doi.org/10.5888/pcd19.210459
U.S. Census Bureau. (2023). American Community Survey 5-year estimates. https://www.census.gov/programs-surveys/acs
U.S. Department of Agriculture, Economic Research Service. (2025). Food Access Research Atlas. https://www.ers.usda.gov/data-products/food-access-research-atlas
How this NU505 Unit 7 example is structured
The profile expresses every comparison as a ratio, and says so at the outset, so a reader can rank gaps across measures that use different units. Population and demographics open briefly, since the tract's age mix affects how its diabetes figure should be read. The table is the center. Each ratio is paired with its source type, because model-based small-area estimates carry uncertainty that a county survey figure does not, and the profile notes where confidence intervals overlap. The food and transport section explains the ratios rather than adding new ones: the tracts qualify as low income and low access at half a mile, and 27 percent of households have no vehicle. The geography mismatch receives its own paragraph because ignoring it would let county-sized data pose as neighborhood data. The priority statement then chooses leisure-time inactivity in adults with prediabetes as the modifiable target.
Get an NU505 Unit 7 example written to your instructions
Name the neighborhood, tract or town your Unit 7 prompt points to, plus the rubric and whichever data sources the section requires. The profile is compiled at the smallest geography those sources publish, with gaps expressed as ratios and one target argued. The first custom sample is free, with delivery inside 24-48h. 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.
NU505 Unit 7 questions, answered
Why express the comparisons as ratios instead of differences?
Ratios put measures with different baselines on a common footing, so a gap in diabetes and a gap in inactivity can be ranked against each other. Differences are also valid and are sometimes easier for a lay audience. The example reports ratios in the table and gives one or two absolute differences in the text where a reader would want a count.
Are census tract estimates reliable enough to base a plan on?
They are the best public small-area data available, with real limits. American Community Survey figures carry margins of error that can be wide for small tracts, and PLACES health estimates are modeled rather than measured. A profile that reports those limits, notes where intervals overlap, and still draws a cautious conclusion shows the judgment instructors look for.
What should a profile do when the data it needs are not published at tract level?
Use the smallest available unit, state its boundaries and say how far it extends beyond the community. The example does this with ZIP-level emergency visit data, naming the three extra tracts the code covers. Where no local figure exists, the county number can stand in if it is labeled clearly, and the gap itself becomes a recommendation for better local data.