| Course | MHA 507 Using Informatics in the Health Sector (MHA/507) |
|---|---|
| Week | 2 |
| Paper type | Public data set report |
| Length | about 1,194 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | MHA |
| Updated | September 2026 |
Free sample paper for MHA 507 Week 2
Planning for Winter With Someone Else's Data: Using CDC Respiratory Hospitalization Surveillance to Estimate a Health System's Flu, RSV and COVID-19 Admissions by Age
[Student Name]
University of Phoenix
MHA/507: Using Informatics in the Health Sector
Week 2 Assignment
[Instructor Name]
[Date]
The health system, its service area population and estimates are composites written for a model paper; surveillance rates come from the CDC data set cited and were retrieved in September 2026.
In August, the chief nursing officer of a composite four-hospital system in Ohio asked the performance improvement manager a planning question: how many patients with influenza, RSV and COVID-19 should the hospitals expect next winter, and in which age groups? The system's own history was one guide, but the manager wanted an independent estimate based on public data. This paper reports how she chose a data set, evaluated it and used it.
The Question
The question had three parts: the expected number of respiratory virus admissions over a full season, their distribution by age and the peak months. Each part pointed to data with age-specific hospitalization rates over time.
Candidate Data Sets
The manager considered several public sources. Federal hospital capacity data report bed use but not age-specific rates. Emergency department syndromic data are timely but not public at the needed level. State health department dashboards vary in detail. CDC's respiratory virus hospitalization surveillance network reports laboratory-confirmed hospitalization rates by age, week and state for influenza, RSV and COVID-19, and offers downloadable data (Centers for Disease Control and Prevention, 2026). It was the best fit.
How the Data Are Collected
Understanding a data set begins with its methods. The influenza component, one of the network's three parts, identifies laboratory-confirmed influenza hospitalizations among residents of defined catchment areas in participating states, reviews medical records and calculates population-based rates, a system that has been used to monitor season severity and guide treatment and vaccination recommendations (Chaves et al., 2015). Catchment areas cover selected counties, not whole states, and cases require a positive laboratory test.
Judging Data Quality
The manager applied a standard set of data quality questions: completeness, correctness, concordance with other sources, plausibility and currency (Weiskopf & Weng, 2013). The data are population-based and use consistent case definitions, which support comparisons. But they undercount infections never tested, cover only certain counties and are revised as records are reviewed.
What the 2024-25 Season Showed
The manager downloaded cumulative observed rates for the 2024-25 season. For influenza, the cumulative rate across participating sites was 126 hospitalizations per 100,000 people, ranging among states from 89 to 171; Ohio's rate was 145. For RSV, the overall rate was 55 per 100,000, but among infants under 1 year it was 939. For COVID-19, the overall rate was 103, rising to 1,211 among adults 85 and older. For all three combined, the overall rate was 284, and among adults 85 and older it was 2,321 per 100,000 (Centers for Disease Control and Prevention, 2026). The same season looked mild to a healthy 40-year-old and severe to anyone over 85 or under 1.
Why Rates Matter
Rates adjust for population size. The system's service area has far more adults aged 18 to 49 than adults over 85, but the older group's rate is dozens of times higher, so a large share of admissions comes from a small population.
Applying Rates to the Service Area
The service area has about 780,000 residents. Using census estimates, the manager multiplied the population in each age group by that group's combined cumulative rate. The largest expected numbers came from adults 65 to 74, 75 to 84 and 85 and older, together producing more than half of expected admissions, followed by infants and young children, driven by RSV. The total estimate was about 2,200 respiratory virus admissions for a season like 2024-25.
Comparing With the System's Own History
The system admitted about 1,900 patients with these viruses in the 2024-25 season, most of them between December and February. The public-data estimate of 2,200 is higher, consistent with the system capturing only part of the service area's hospitalizations, since some residents use other hospitals.
Other Public Data That Helped
The surveillance rates answered the core question, but other public data filled gaps. Census population estimates by age for the service area's counties supplied the denominators. Federal hospital data on intensive care capacity helped the manager judge whether regional beds would be stretched during a peak. County-level estimates of chronic disease prevalence from another CDC data set showed that two counties in the service area had higher rates of chronic lung disease, which could raise respiratory admissions there above the average.
Timing the Peak
Cumulative rates describe a whole season, but staffing depends on timing. The weekly data showed that RSV hospitalizations among infants and COVID-19 hospitalizations overall both peaked in the week ending January 4, 2025, while influenza hospitalizations rose through January and peaked in the weeks ending February 1 and February 8. Combined, the three viruses kept weekly rates high from late December into mid-February. The staffing plan follows these patterns: pediatric capacity ready by mid-December and adult medical surge capacity from late December through February.
Communicating the Estimate
The manager presented the estimate as a range, 1,900 to 2,500 admissions depending on season severity, rather than a single number, and explained to nurse managers what drove it. Presenting uncertainty honestly helped managers plan flexible staffing rather than fixed numbers that might prove wrong.
Limits
Several limits apply. Rates from one season may not predict the next; severity varies. Ohio's catchment counties may differ from the system's service area. Laboratory-confirmed counts miss untested cases. Rates are revised after the season. The estimate is therefore a planning range, not a forecast.
Implications for Staffing
The estimate supports three decisions: a winter staffing plan for medical units with surge beds staffed from December through February; extra pediatric capacity for RSV in infants, coordinated with the regional children's hospital; and early vaccination outreach for older adults through the system's primary care clinics.
Watching the Season
The manager will download weekly rates each Friday during the season and compare them with the system's admissions, adjusting staffing if the season runs higher or lower than planned.
Equity Considerations
Rates also differ by race, ethnicity and income, and the surveillance data report some of these differences. Older adults in the service area's lower-income neighborhoods have less access to primary care and vaccination, which could raise their hospitalization rates above the averages used here. The vaccination outreach plan therefore targets clinics serving those neighborhoods first, and the manager will compare admissions by neighborhood after the season to check whether the gap narrows.
What the Data Cannot Tell
The data describe how many people were hospitalized, not how sick they were once admitted, how long they stayed or how many needed intensive care. For those questions, the system's own records are better. The public data set the scale and timing; internal data will set the mix of beds and staff.
Reproducibility
The manager documented the data set, the filters used, including season, rate type and age groups, the retrieval date and the census figures, so another analyst could repeat the analysis.
Conclusion
Public surveillance data gave the system an independent, age-specific estimate of winter respiratory admissions. Understanding the network's methods and limits, reporting rates with dates and applying them carefully to the service area turned someone else's data into a staffing plan, with weekly monitoring to adjust as the season unfolds.
References
Centers for Disease Control and Prevention. (2026). RESP-NET rates and clinical data [Data set]. data.cdc.gov. https://data.cdc.gov/d/kvib-3txy
Chaves, S. S., Lynfield, R., Lindegren, M. L., Bresee, J., & Finelli, L. (2015). The US Influenza Hospitalization Surveillance Network. Emerging Infectious Diseases, 21(9), 1543-1550. https://doi.org/10.3201/eid2109.141912
Weiskopf, N. G., & Weng, C. (2013). Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research. Journal of the American Medical Informatics Association, 20(1), 144-151. https://doi.org/10.1136/amiajnl-2011-000681
What the MHA 507 Week 2 instructions ask
MHA 507 Week 2 usually asks students to locate and use a public health care data set to answer a question for an organization. Prompts may ask students to identify appropriate public data sources, describe how the data were collected, evaluate their quality and limitations, analyze the data and report findings and implications for decision making. Some versions supply a data set; others ask students to find one. Strong papers state the question first, choose a data set suited to it, explain the collection methods and coverage from official documentation, report specific values with dates, adjust for population differences using rates, acknowledge limits honestly and translate findings into a decision.
How this MHA 507 Week 2 example is built
The paper opens with the chief nursing officer asking how many respiratory virus admissions to expect next winter. Candidate public data sets are compared against the question. CDC's surveillance network for influenza, RSV and COVID-19 hospitalizations is chosen, and its methods are described. The 2024-25 season's cumulative rates by age are reported, from 939 RSV hospitalizations per 100,000 infants to 1,211 COVID-19 hospitalizations per 100,000 adults 85 and older. Applied to the service area's 780,000 residents by age group, the rates suggest about 2,200 respiratory virus admissions in a similar season. Limits, a comparison with the system's own admissions and a winter staffing plan close the paper.
MHA 507 Week 2 grading rubric: where the points go
The public data week is generally graded on the choice, evaluation and use of the data set. Graders look for a clear question, an appropriate data set with its source documented, an accurate description of collection methods and coverage, specific findings with dates, use of rates rather than raw counts, honest limitations and implications for a real decision. Reproducible steps, such as the filters used and the retrieval date, earn credit. Official documentation and published methods papers strengthen the evaluation considerably. Organization and APA formatting account for the remainder. Reports that quote numbers without explaining where they came from, or apply national data without considering local differences, usually lose points.
MHA 507 Week 2 help: mistakes to avoid
A common weakness in MHA 507 Week 2 is grabbing a number from a public website without understanding it. Start with the question you need answered. Choose a data set designed for that kind of question and read its documentation: who collects it, where, how cases are defined and how current it is. Report values with their dates, units and the filters you used. Use rates, not counts, to compare places and age groups of very different sizes. Apply national or state rates to your population carefully and say what could make your area different. Finally, explain the decision the data support and what you would watch to update the estimate as new data arrive.
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MHA 507 Week 2 questions, answered
What does MHA/507 Week 2 usually ask for?
Prompts usually ask students to locate a public health care data set, evaluate its collection methods and limits, analyze it and explain implications for a decision.
Where can I find a free MHA 507 Week 2 sample paper?
Read the complete respiratory surveillance paper above without charge; margin comments walk through each analytic step. Bring your own planning question, and we write the first data report for you free.
What is RESP-NET?
A CDC network that tracks laboratory-confirmed hospitalizations for influenza, RSV and COVID-19 in selected counties across participating states, reporting rates by age and other characteristics.
How high were flu hospitalization rates in 2024-25?
CDC surveillance data showed a cumulative influenza hospitalization rate of 126 per 100,000 people across participating sites for the 2024-25 season, rising to 845 per 100,000 among adults 85 and older.
Why use rates instead of counts when comparing data?
Rates adjust for population size, so an age group or area with more people does not appear worse simply because it is larger.
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