| Course | NU 780 Population Health |
|---|---|
| Unit | Unit 2 |
| Paper type | Epidemiologic profile |
| Length | About 1,040 words, 6 pages |
| Format | APA 7 student paper |
| School | Herzing University |
| Program | MSN |
| Updated | October 2026 |
Free sample paper for NU 780 Unit 2
Epidemiologic Profile of Pediatric Asthma Emergency Department Visits in a Midwestern County, 2019 to 2023
Student Name
Department of Nursing, Herzing University
NU 780: Population Health
Instructor Name
January 30, 2026
Epidemiologic Profile of Pediatric Asthma Emergency Department Visits in a Midwestern County, 2019 to 2023
The Problem and the Measure
This profile measures emergency department visits for asthma among children aged 0 to 17 in a composite Midwestern county with 48,500 children, using the state's hospital discharge and emergency department dataset. The measure is a visit rate: emergency department visits with asthma as the principal diagnosis per 10,000 children per year. It counts events, not people, so a child with three visits in a year counts three times. It is therefore neither incidence, which would count new cases of asthma, nor prevalence, which would count children living with asthma. It measures the burden of poorly controlled asthma that reaches the emergency department, which is the outcome most sensitive to the quality of outpatient asthma care (Celentano & Szklo, 2019).
Data Source, Definitions, and Their Quality
Visits are drawn from the state's emergency department dataset, which includes every visit to a licensed hospital emergency department in the state and records the patient's county of residence, so visits by county children to hospitals in neighboring counties are captured and visits by nonresidents are excluded. A visit counts when asthma is coded as the principal diagnosis, using the ICD-10-CM J45 codes. Visits that led to hospital admission are included, and transfers between emergency departments are counted once. The denominator is the Census Bureau's population estimate for children in the county for each year. Two quality issues matter. Coding practice can vary between hospitals, so a visit for wheezing coded as bronchiolitis in an infant or as a viral respiratory infection would not be counted, which probably makes the youngest age group's rate an underestimate. And population estimates between censuses carry error that is larger for small groups, which affects the race-specific and ZIP code rates more than the county total. Epidemiologic practice treats both issues by stating them rather than ignoring them (Friis & Sellers, 2021).
Crude Rate and Comparison
In 2023, the county recorded 612 pediatric emergency department visits for asthma. Divided by the 48,500 children in the county's population estimate for that year, the crude rate was 126.2 per 10,000 children. The state's crude rate for the same year was 73.0 per 10,000. For context, the most recent national data show that about 6% of children have current asthma, and that emergency department visits are a common consequence of poorly controlled disease (Zahran et al., 2018). Crude rates can mislead when populations differ in age structure, which is the next question.
Age-Specific Rates and Age Adjustment
Asthma emergency visits are most frequent in young children, and the county has a larger share of children under five than the state, 28.9% compared with 25.0%. Age-specific rates in the county were 190.0 per 10,000 among children 0 to 4 (266 visits among 14,000 children), 120.0 among children 5 to 11 (222 among 18,500), and 77.5 among adolescents 12 to 17 (124 among 16,000).
Direct age adjustment applies the county's age-specific rates to a standard population, here the state's child population, in which 25% are 0 to 4, 40% are 5 to 11, and 35% are 12 to 17. The calculation is 0.25 x 190.0 + 0.40 x 120.0 + 0.35 x 77.5 = 47.5 + 48.0 + 27.1 = 122.6 per 10,000. The age-adjusted rate is slightly lower than the crude rate, because the county's younger age structure had inflated the crude figure. Even after adjustment, the county's rate is about 1.7 times the state's rate of 73.0, so the difference is not explained by age.
How Precise the County Rate Is
A rate based on 612 events has sampling variability. Treating the count as a Poisson variable, the standard error of the rate is the square root of 612, about 24.7, divided by the population and multiplied by 10,000, which gives about 5.1 per 10,000. The approximate 95% confidence interval for the crude rate is therefore 126.2 plus or minus 10.0, or about 116 to 136 per 10,000. The state's rate of 73.0 lies well outside this interval, so the difference is very unlikely to be due to chance variation in a single year. The same calculation for a ZIP code with only 40 visits would give an interval roughly four times wider relative to its rate, which is why the geographic comparison below combines three years.
Trend
County rates per 10,000 children were 138.4 in 2019, 71.2 in 2020, 98.5 in 2021, 117.3 in 2022, and 126.2 in 2023. The sharp fall in 2020 coincides with the first year of the COVID-19 pandemic, when school closures, masking, reduced circulation of respiratory viruses, and avoidance of emergency departments reduced visits nationally. The rise since then returns the county toward its prepandemic level. The 2020 value should not be read as an improvement in asthma control, and comparisons for this problem should use 2019 as the prepandemic baseline.
Differences Within the County
Race-specific rates show the largest difference in the profile. The 2023 rate among Black children was 248 per 10,000, compared with 71 among White children, a rate ratio of about 3.5. Rates by ZIP code, calculated from the patient's residence, were highest in the three ZIP codes that include the county's older inner-ring suburbs, where rates exceeded 200 per 10,000, and lowest in the outer suburban ZIP codes, at about 50. Small numbers in some ZIP codes make single-year rates unstable, so three-year combined rates were used for the geographic comparison.
What the Profile Can and Cannot Say
The profile establishes that the county's burden of pediatric asthma emergency visits is high, not explained by age structure, rising since 2020, and concentrated among Black children and in the inner-ring suburbs. It cannot say whether the excess reflects more children with asthma, more severe asthma, poorer asthma control, or less access to primary care, because visit data do not separate these. The state's survey estimates of asthma prevalence among children are available only at the regional level and cannot be used to calculate a county-level visit rate per child with asthma. The next step is a determinants analysis of why the burden falls where it does, using school absence data, housing age, and primary care access as candidate explanations.
References
Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.
Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett Learning.
Zahran, H. S., Bailey, C. M., Damon, S. A., Garbe, P. L., & Breysse, P. N. (2018). Vital signs: Asthma in children, United States, 2001-2016. MMWR. Morbidity and Mortality Weekly Report, 67(5), 149-155. https://doi.org/10.15585/mmwr.mm6705e1
NU 780 Unit 2 instructions, in plain terms
In Unit 2 you build an epidemiologic profile of a health problem in a defined population. Expect to define the measure, identify the data source and its limits, calculate rates such as incidence, prevalence or visit rates, compare them with state or national figures, examine trends and differences by subgroup, and interpret the findings. Many versions ask for age adjustment or confidence intervals when comparing populations with different structures. Check which measures your section requires and whether you must show calculations. Use public data from state health departments, CDC WONDER or similar sources. Show your arithmetic so the grader can follow it. A clear statement of what the data cannot tell you, such as cause, is part of a complete profile.
How this NU 780 Unit 2 example is built
The profile starts by naming the population, the outcome and the data source, then explains how visits are coded and where coding could mislead. The crude rate is calculated in the text, count over population times 10,000, so the arithmetic is visible. Age-specific rates follow in a table, and age adjustment to the state's age structure shows the excess is not explained by more young children. The precision section computes a standard error and interval in two sentences. The trend section reads five years of rates and links the 2020 drop to the pandemic. Subgroup differences are presented as rate ratios. The final section is careful about inference, stating that the profile shows burden and pattern but not cause, which is what the next units must explore.
Reading the NU 780 Unit 2 rubric
Herzing's rubric for an epidemiologic profile usually awards points for the description of the problem and population, the data sources, the accuracy of calculations, comparisons and trends, interpretation, and writing. Data source points require a credible, cited source with its limitations noted. Calculation criteria are often the most heavily weighted, giving full credit only for correct rates shown with numerators and denominators. Comparison points go to appropriate benchmarks and, where needed, age adjustment. Trend analysis rewards attention to unusual years and their causes. Interpretation earns full marks when it stays within what the data can support. The writing criterion covers tables, clear labels on every rate and APA 7 citations, including Celentano and Szklo for methods.
Common NU 780 Unit 2 mistakes, and how to avoid them
Unit 2 profiles commonly lose points on rates reported without a denominator or unit, such as per 10,000 children. Another frequent problem is comparing crude rates across populations with different age structures. Students often confuse incidence and prevalence. Trends read from two years only can mislead, especially around 2020. Subgroup differences reported as percentages instead of rates or ratios are hard to interpret. Interpretation sections sometimes claim causes the data cannot show. Small numbers without confidence intervals invite overreading. Finally, describe the data source's limits, such as coding errors or missing visits to out-of-state hospitals, since graders expect you to know how the numbers were produced before you draw conclusions from them.
Get a NU 780 Unit 2 example written to your instructions
Send the Unit 2 instructions and the rubric from your NU 780 classroom, plus the condition and community your profile covers. We write a custom example with rates, denominators, age adjustment and a defended comparison group, returned in 24 to 48 hours. The first custom sample is free.
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NU 780 Unit 2 questions, answered
What is the difference between incidence and prevalence?
Incidence counts new cases arising over a period; prevalence counts everybody who has the condition at a point. They answer different questions and move for different reasons, so a prevalence rise can mean better survival rather than more disease. Using the wrong one, or the two interchangeably, is the error faculty in this course notice first.
When do I need age adjustment?
Whenever you compare populations whose age structures differ, which is most of the time. A retirement community will show higher crude rates for almost anything, and a comparison that does not adjust is measuring age rather than health. Use age-adjusted figures where the source provides them, and say so, rather than adjusting them yourself unless the assignment asks.
What do I do when the numbers are very small?
Report them as small and say what that means. A rate built on four cases will swing wildly year to year, and presenting it to a decimal place suggests a stability it does not have. Combine years if the data permits, report a range, and note explicitly that the estimate is unstable rather than letting the figure stand unqualified.
Where can I find a free NU 780 Unit 2 sample paper?
This page shows the complete NU 780 Unit 2 pediatric asthma profile for free, every calculation and the interpretation included. A custom profile can be built for another condition or county using public data your section accepts.
When do I need to age-adjust rates in an epidemiology paper?
Age-adjust when you compare populations with different age structures and the outcome varies by age. This sample adjusts the county's asthma visit rate to the state's age structure because the county has more children under five, who visit most often.