| Course | MHA 507 Using Informatics in the Health Sector (MHA/507) |
|---|---|
| Week | 4 |
| Paper type | City and age analysis report |
| Length | about 1,215 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 4
Canton, Cleveland, Toledo and Akron by the Numbers: A City and Age Analysis of Lung Disease Risk and Respiratory Hospitalizations for a Northern Ohio Health System
[Student Name]
University of Phoenix
MHA/507: Using Informatics in the Health Sector
Week 4 Assignment
[Instructor Name]
[Date]
The health system and its planning decisions are composites written for a model paper; city and age estimates come from the CDC data sets cited, retrieved in September 2026.
The board of a composite four-hospital health system serving northern Ohio approved two new pulmonary rehabilitation programs for people with chronic obstructive pulmonary disease and asked the performance improvement manager where to put them. The system's hospitals serve Canton, Cleveland, Toledo and Akron. The manager answered with an analysis of public data by city and by age. This paper reports it.
The Questions
The board's decision required two kinds of information: which cities have the highest burden of chronic lung disease, and which age groups are most affected by respiratory illness severe enough to require hospitalization. The first called for data by place, the second for data by age.
Data by Place
CDC's local health data program publishes modeled estimates of how common chronic illnesses, risk behaviors and preventive services are in cities and other small areas across the country (Greenlund et al., 2022). The manager downloaded the program's 2025 place-level release for Ohio, selecting the four service cities and two comparison cities, Columbus and Cincinnati (Centers for Disease Control and Prevention, 2025).
Why Age-Adjusted Values
Cities differ in age structure, and chronic lung disease is more common in older adults. Comparing crude prevalence could make an older city look worse simply because of age. The manager used age-adjusted prevalence, which estimates what each city's rate would be with a standard age distribution.
Findings: COPD
Age-adjusted COPD prevalence among adults was 10.8% in Canton, 9.8% in Cleveland, 9.4% in Toledo and 9.2% in Akron, compared with 7.8% in Cincinnati and 7.1% in Columbus. All four service cities exceeded both comparison cities.
Findings: Asthma and Smoking
Current asthma prevalence ranged from 11.6% in Columbus to 13.0% in Cleveland, a narrower spread. Current smoking was 24.7% in Canton, 21.9% in Cleveland, 20.5% in Toledo and 20.3% in Akron, compared with 17.1% in Cincinnati and 15.8% in Columbus. The cities with the most smoking were those with the most COPD, as expected given smoking's role in the disease. Where smoking runs highest, lung disease follows, and so should the programs.
A Suburb Inside the Pattern
The comparison also showed variation within one metropolitan area. Parma, a suburb bordering Cleveland, had age-adjusted COPD prevalence of 6.7% and smoking of 15.9%, lower than Columbus and far below Cleveland itself. The Cleveland burden is concentrated in the city rather than spread evenly across the county, which matters for deciding where in the Cleveland area a program should sit.
Rates Versus Numbers of People
Rates show where risk is highest, but planners also need to know how many people are affected. Applying each city's COPD prevalence to its approximate adult population from census estimates, Cleveland, with about 373,000 residents, has roughly 28,000 adults with COPD, while Canton, with about 71,000 residents, has fewer than 6,000. Canton has the highest rate; Cleveland has far more patients. Both facts shape the decision: Cleveland's program needs more capacity, while Canton's need is concentrated in a smaller population that is easier to reach through its primary care practices.
Presenting the Results
The report presents a single table with the six cities sorted by COPD prevalence, with columns for COPD, asthma and smoking and a note that the values are model-based estimates, age-adjusted, for adults.
Data by Age
Place-level estimates do not show which ages are hospitalized. For that, the manager used CDC's respiratory virus hospitalization surveillance, which for the 2024-25 season showed combined influenza, RSV and COVID-19 hospitalization rates of 2,321 per 100,000 among adults 85 and older, 1,150 among those 75 to 84 and 543 among those 65 to 74, compared with 89 among adults 18 to 49; infants under 1 year also had a very high rate of 1,285 (Centers for Disease Control and Prevention, 2026).
Children Are a Separate Question
The age data also showed a second peak at the other end of life. Combined hospitalization rates were 1,285 per 100,000 among infants under 1 year and 455 among children 1 to 4, driven largely by RSV, before falling to 69 among children 5 to 17. Adults 50 to 64 had a rate of 264. The board's question concerned adult lung disease, so the pediatric findings were passed to the pediatric service line for its winter planning rather than folded into the rehabilitation decision.
What Age and Place Together Show
Combining the two views, the heaviest need falls on older adults in cities with high COPD and smoking prevalence. Respiratory infections are a common cause of COPD exacerbations and hospitalizations, so the older population in Canton and Cleveland is where pulmonary rehabilitation and infection prevention could matter most.
How the City Estimates Are Made
The place estimates are not counts of diagnosed patients. They come from statistical models that link answers to a large national telephone health survey with local population characteristics, producing prevalence estimates for areas too small to survey directly (Greenlund et al., 2022). This allows comparisons across many places but means the values carry uncertainty, and small differences may not be meaningful.
Judging Whether Differences Are Real
The city data set publishes a confidence interval with each estimate. Before ranking the cities, the manager checked that the intervals for Canton and Cleveland did not overlap with those for Columbus and Parma, so the contrast between high and low cities was not an artifact of modeling. Differences of a few tenths of a point, such as those between Toledo and Akron, were treated as equivalent, and the report says so plainly rather than ranking them.
Limits
The estimates describe adults and do not report age groups within cities. The surveillance rates come from selected catchment areas nationally, not from these cities specifically. Neither data set shows how many residents use the system's hospitals. The manager therefore checked the system's own records, which showed COPD admissions per 1,000 residents highest from Canton and Cleveland, consistent with the public data.
Recommendations
The two programs will be placed in Canton and Cleveland. Each will be paired with a smoking cessation service, since smoking prevalence there exceeds 20%, and with respiratory vaccination outreach for patients 65 and older before winter. Toledo and Akron will receive a mobile pulmonary education program and referral pathways to the nearest site.
Sharing the Analysis
The manager presented the table and a two-panel chart, cities on one side and ages on the other, to the board and then to the pulmonary physicians and nurse navigators who would run the programs. Clinicians added local knowledge that the numbers could not: a large public housing community in Canton with many older smokers and limited transportation, which led to a van service for the Canton program.
Monitoring
The system will track program enrollment by city, COPD readmissions and emergency visits and vaccination rates among enrolled patients, and will update the place estimates when the next release appears.
Conclusion
Age-adjusted city estimates showed that the system's four cities, especially Canton and Cleveland, carry more COPD and smoking than comparison cities, while age-specific surveillance showed that severe respiratory illness concentrates among the oldest adults. Explaining how the estimates are made kept the interpretation honest, and combining place and age pointed the new programs to where they are most needed.
References
Centers for Disease Control and Prevention. (2025). PLACES: Place data (GIS friendly format), 2025 release [Data set]. data.cdc.gov. https://data.cdc.gov/d/vgc8-iyc4
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
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
What the MHA 507 Week 4 instructions ask
MHA 507 Week 4 usually asks students to analyze health data by geography and age to identify where needs are greatest. Prompts may ask students to obtain data for several cities or areas and age groups, compare them using appropriate measures, present results in tables or charts, interpret differences and recommend actions. Some versions supply a data set of cases by city and age. Strong papers use rates or prevalence rather than counts, choose age-adjusted measures when comparing places with different age structures, explain where the data come from and how they were estimated, avoid overinterpreting small differences and connect findings to specific, targeted actions.
How this MHA 507 Week 4 example is built
The paper opens with the system's board asking where to place two new pulmonary rehabilitation programs. City-level estimates for the system's four cities and two comparison cities are pulled from CDC's local health data program: age-adjusted COPD prevalence, current asthma and smoking. Canton and Cleveland show the highest COPD prevalence and smoking. Age-specific surveillance rates show respiratory hospitalizations concentrated among adults 75 and older and infants. The model-based methods behind city estimates are explained. The programs are placed in Canton and Cleveland, with smoking cessation and older adult outreach, and limits close the paper, along with a monitoring plan for the programs' first year.
MHA 507 Week 4 grading rubric: where the points go
The city and age analysis week is typically graded on correct use of measures and sound interpretation. Graders look for data from credible sources with dates, rates or prevalence rather than raw counts, age adjustment when comparing places, clear tables or charts, explanation of how the data were produced and their limits, cautious interpretation of differences and recommendations targeted to the places and ages most affected. Documenting how data were retrieved earns credit. Methods papers and official documentation strengthen the report. Organization and APA formatting make up the remaining points. Reports that compare raw counts across cities of very different sizes, or ignore age structure, usually lose points, as do reports that present a table without saying what it means for the decision at hand.
MHA 507 Week 4 help: mistakes to avoid
A common weakness in MHA 507 Week 4 is comparing raw numbers across places of very different sizes and ages. Use rates or prevalence, and use age-adjusted values when comparing places, since an older city will have more chronic disease simply because of age. Present results in a simple table sorted in a meaningful order. Explain where the numbers came from and whether they are measured or modeled. Do not overinterpret small differences; check confidence intervals where available. Connect findings to both place and age, since both shape need. Finally, recommend actions targeted to the groups and places the data identify, and say which numbers you will watch to see whether those actions work.
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MHA 507 Week 4 questions, answered
What does MHA/507 Week 4 usually ask for?
Prompts usually ask students to analyze health data by city or area and age group, compare them with appropriate measures and recommend targeted actions.
Where can I find a free MHA 507 Week 4 sample paper?
The northern Ohio city and age paper is posted above without charge, and margin notes explain each comparison. For an analysis of cities and ages in your own service area, the first paper is free.
What is age-adjusted prevalence?
A prevalence estimate recalculated as if each place had the same age distribution, so differences reflect more than one place simply having older residents.
What is CDC PLACES?
A CDC program publishing modeled local figures for illnesses, risk behaviors and preventive care at several geographic levels, from counties down to cities and small neighborhoods, nationwide.
Which age groups have the highest respiratory virus hospitalization rates?
CDC surveillance for 2024-25 showed the highest combined influenza, RSV and COVID-19 hospitalization rates among adults 85 and older and infants under 1 year.
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