| Course | NUR-4430 Public Health Nursing |
|---|---|
| Module | Module 2 |
| Paper type | Epidemiology application paper |
| Length | About 1,054 words, 6 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Bachelor of Science in Nursing |
| Updated | October 2026 |
Free sample paper for NUR 4430 Module 2
Twelve Percent of Whom? Rates, Denominators and Study Designs Behind One County's Diabetes Figure
Student Name
Bachelor of Science in Nursing, Thomas Edison State University
NUR-4430: Public Health Nursing
Mentor Name
Month Day, Year
Twelve Percent of Whom? Rates, Denominators and Study Designs Behind One County's Diabetes Figure
In the first module I reported that Cumberland County, New Jersey, had the highest estimated adult diabetes prevalence of the state's twenty-one counties, at 12.1 percent. That figure is a good place to learn epidemiology, because almost every word in the sentence needs explaining. Twelve percent of whom? Measured how, and when? Compared with what? This paper uses the county's diabetes data to work through the basic measures of disease frequency, the difference between crude and age-adjusted rates, the comparison of rates between places, and the study designs that produce different kinds of evidence. Every figure comes from a named public source, and every calculation is shown.
Counts, Rates and Denominators
Epidemiology begins with counting cases, yet fifty cases in a town of two thousand and fifty in a city of two million describe completely different situations. Celentano and Szklo (2019) treat a true rate as speed, the pace at which new cases arise among the people able to become cases, and they insist that no rate is complete without its numerator, its denominator and its window of time. The numerator must come from the denominator; counting diabetes cases among all adults but dividing by adults aged eighteen to sixty-four would produce a meaningless number.
For Cumberland County, the 2023 PLACES estimate (Centers for Disease Control and Prevention, 2025) gives a crude adult diabetes prevalence of 13.5 percent (95 percent confidence interval 11.8 to 15.4) among an adult population of 115,216. Multiplying gives an estimated 15,554 adults living with diagnosed diabetes, with a plausible range of roughly 13,600 to 17,700. That figure is prevalence: diagnosed adults of every duration, from last month's new diagnosis to a twenty-year history, divided by all adults. It says how large the burden is today. It does not say how fast new cases are appearing, which is incidence, and a county can have high prevalence because people with diabetes live longer with good care as well as because new cases are frequent.
Why Age Adjustment Changes the Picture
The same source reports two figures for Cumberland: the crude prevalence of 13.5 percent and an age-adjusted prevalence of 12.1 percent. The crude figure is simply cases divided by adults. The age-adjusted figure answers a different question: what would the prevalence be if Cumberland's adults had the same age distribution as a standard population? Because diabetes becomes more common with age, a county with older residents will have a higher crude rate even if its age-specific risk is no worse than its neighbors'. Age adjustment removes that difference so that places can be compared fairly.
Comparing two counties shows why this matters. Hunterdon County, which has the state's lowest estimate, has a crude diabetes prevalence of 9.2 percent and an age-adjusted prevalence of 7.0 percent. On crude rates, Cumberland's prevalence is 13.5 divided by 9.2, or about 1.47 times Hunterdon's. On age-adjusted rates, the ratio is 12.1 divided by 7.0, or about 1.73. The gap widens after adjustment, which tells us that Hunterdon's crude rate is inflated by an older population. Measured fairly, the difference between the two counties is larger than the raw figures suggest.
| Measure | Cumberland | Hunterdon | Ratio |
|---|---|---|---|
| Crude adult diabetes prevalence, 2023 | 13.5% | 9.2% | 1.47 |
| Age-adjusted adult diabetes prevalence, 2023 | 12.1% | 7.0% | 1.73 |
| Adults aged 18 and over | 115,216 | 105,457 | Not applicable |
| Estimated adults with diagnosed diabetes | About 15,550 | About 9,700 | Not applicable |
Where the Numbers Come From
It is also important to know how an estimate was made. PLACES estimates are not counts from medical records. They are model-based estimates produced from the Behavioral Risk Factor Surveillance System, a national telephone survey, combined with census population data through a method called multilevel regression and poststratification. Zhang et al. (2014) described and tested this method for estimating local prevalence where survey samples are too small to report directly, and reported close agreement between the modeled figures and the survey's own figures in the places large enough to produce both. The method allows every county to have an estimate, but the estimates depend on self-reported diagnoses and on the model, so they describe diagnosed diabetes and carry wider uncertainty for small places. A public health nurse should cite them as estimates, with their intervals, and should prefer local surveillance data where they exist.
Study Designs and What They Can Show
Different questions need different designs. The PLACES figures come from a cross-sectional survey, which measures exposure and outcome at the same moment. Cross-sectional data are excellent for describing burden and planning services, but they cannot show which came first, so they cannot establish that, for example, food insecurity causes diabetes rather than the reverse. Cohort studies follow people over time from exposure to outcome and can measure incidence directly. Case-control studies work backward, collecting people with the disease and similar people without it, then compare their past exposures; this suits rare conditions. Randomized controlled trials assign an intervention and are the strongest design for testing whether something prevents disease.
The national diabetes prevention trial shows what a randomized design adds (Diabetes Prevention Program Research Group, 2002). Adults with prediabetes were assigned to an intensive lifestyle program, metformin or placebo and followed for an average of 2.8 years. Diabetes developed at 11.0 cases per 100 person-years with placebo and 4.8 per 100 person-years with the lifestyle program, a reduction of 58 percent; put another way, roughly one case was prevented over three years for every seven participants who went through the program. Those figures are incidence rates, built on person-years as the denominator, which is exactly the measure prevalence cannot provide. For a county with Cumberland's burden, the trial supplies the evidence that prevention can work, while the survey estimates show where it is most needed.
What This Means for Practice
For a public health nurse, careful reading of a single figure is a practical skill. It determines whether a presentation to a county board compares the county fairly with its neighbors, whether a grant application states the burden in people as well as percentages, and whether a program is judged by an outcome it could plausibly change. The 12.1 percent at the start of this paper is real and serious, but it becomes useful only when its denominator, its method and its limits are understood.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
Centers for Disease Control and Prevention. (2025). PLACES: Local data for better health, county data, 2025 release [Data set]. https://data.cdc.gov/500-Cities-Places/PLACES-Local-Data-for-Better-Health-County-Data-20/swc5-untb
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
Zhang, X., Holt, J. B., Lu, H., Wheaton, A. G., Ford, E. S., Greenlund, K. J., & Croft, J. B. (2014). Multilevel regression and poststratification for small-area estimation of population health outcomes: A case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology, 179(8), 1025-1033. https://doi.org/10.1093/aje/kwu018
NUR 4430 Module 2 instructions, in plain terms
Module 2 of NUR-4430 is usually an epidemiology module, and the written work in many terms asks you to apply basic measures of disease frequency to a health problem in a population. Directions often ask you to define incidence and prevalence, calculate or interpret rates from real data, explain crude and adjusted rates, describe the epidemiologic triangle or web of causation, and identify study designs suited to a question. Some mentors hand out a data set; in other sections, gathering the county or state figures is part of the task. Thomas Edison State University keeps the exact prompt in Moodle, so confirm whether calculations must be shown, which data sources are acceptable, and whether a table counts toward the length.
How the NUR 4430 Module 2 example is put together
The paper begins with the figure from the first module and the questions it raises. A section on rates defines the three parts of a rate, then converts the county's crude prevalence into an estimated count with its interval and explains why prevalence is not incidence. The age adjustment section explains the method in plain terms and compares two counties, showing that the ratio grows from 1.47 to 1.73 after adjustment, with a table. A short section explains how PLACES estimates are modeled and what that means for their use. The design section compares four designs and uses the landmark prevention trial to illustrate incidence per person-years and the number needed to treat. The close explains why these skills matter in practice.
NUR 4430 Module 2 rubric: what earns full marks
Marks in an epidemiology paper tend to follow six items: measures defined correctly, accurate calculations, correct interpretation of results, understanding of study designs, use of credible data and sources, and writing. Calculation and interpretation tend to carry the most weight: a correct rate with no interpretation earns less than a correct rate explained in context. Mentors check denominators carefully, so state who is in the population and over what period. Age adjustment is a common point of confusion, and explaining why adjusted and crude figures differ shows real understanding. In the design row, the strongest papers say what each design can and cannot establish, rather than only defining it.
NUR 4430 Module 2 help: mistakes that cost marks
The most common errors in this module are rates without denominators, prevalence called incidence, and comparisons between places using crude rates when age-adjusted rates are available. Write the numerator, denominator and time period for every rate you report. Another frequent problem is overstating survey data: cross-sectional estimates describe burden but cannot show cause. Students also forget confidence intervals, which matter for small counties. Show your arithmetic, round sensibly and keep the table and text consistent. If you are unsure which figures to use, county estimates from CDC PLACES and census data are reliable starting points. The desk can draft this paper around your own county and condition if you send the directions.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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NUR 4430 Module 2 questions, answered
Where can I find a free NUR 4430 Module 2 sample paper?
Read the full epidemiology paper on this page, with its rate table, references and margin notes. It uses Cumberland County diabetes data; apply the same steps to the county and condition your directions ask for.
What is the difference between incidence and prevalence?
Incidence tracks how many people develop the condition for the first time during a set period, out of those who could have developed it, so it measures speed. Prevalence takes a snapshot of everyone living with the condition on a given date, whenever they got it, so it measures load. Both need a clear denominator and time frame.
Why use age-adjusted rates in NUR-4430?
Because many conditions climb with age, a county full of retirees can post a high crude figure while each age band is no riskier than anywhere else. Age-adjusted rates apply a standard age distribution so that places can be compared fairly. Use them for comparisons and crude rates for counting people affected.
Where do county health estimates come from?
CDC PLACES produces county and smaller-area estimates by combining national survey data with census data in a statistical model. They are estimates, not record counts, so report them with their confidence intervals and data year, and use local surveillance data when available.
Which study design is best for a public health question?
It depends on the question. Cross-sectional surveys describe burden, cohort studies measure incidence and risk over time, case-control studies suit rare outcomes, and randomized trials test interventions. Explain why the design fits the question you are asking.