NUR 520 Module 9 Final Project Example

Final Project: Reducing the Burden of COPD in Coös County, New Hampshire, Through an Epidemiological Analysis and Population-Level Recommendations

[Student Name]

Southern New Hampshire University

NUR 520: Epidemiological and Biostatistical Applications in Healthcare

Final Project

[Instructor Name]

[Date]

Final Project: Reducing the Burden of COPD in Coös County, New Hampshire, Through an Epidemiological Analysis and Population-Level Recommendations

Chronic obstructive pulmonary disease is a leading reason adults in northern New Hampshire struggle to breathe, lose work and return to the hospital. Over this course, Coös County has served as the case for describing, measuring and explaining that burden. This final project brings the pieces together. It summarizes how large the problem is and how certain those figures are, identifies the determinants that set the county apart, weighs the causal evidence from published studies and turns the analysis into recommendations that nurse leaders can act on. The analysis supports four population-level actions, led by nurse-delivered tobacco treatment, and an evaluation built on local clinical measures, because county survey estimates move too slowly and too imprecisely to show whether a program works.

Burden and Its Uncertainty

In the 2025 PLACES data from CDC (Centers for Disease Control and Prevention, 2025), crude prevalence of diagnosed COPD among Coös County adults is 9.5%, and its 95% interval runs from 7.7% to 11.4%. Applied to the county's roughly 26,300 adults, that estimate means about 2,500 people living with a COPD diagnosis, and the interval puts the count somewhere between about 2,000 and 3,000. Adjusted for age, prevalence is 7.1%, with an interval from 5.9% to 8.5%. Because its lower limit sits above the upper limit for Rockingham County, 4.6% (3.7% to 5.5%), the gap between the two counties is unlikely to be explained by the county's older population or by chance alone.

Two cautions from earlier modules carry into the final analysis. First, these are model-based small-area estimates built from national survey responses and census characteristics, not counts of patients (Greenlund et al., 2022). Second, they describe diagnosed disease only, so they understate the true burden in a place where many adults have limited access to spirometry. The crude figure is the right one for planning services; the age-adjusted figure is the right one for comparing counties.

Determinants

Table 1 sets the county's COPD estimates beside the conditions that most plausibly drive them. The statewide figures are averages of the ten county estimates weighted by adult population, calculated for this project from the same data set.

Table 1

COPD and Related Conditions, Coös County Versus the State

Note. Coös County figures from CDC PLACES 2025 (CDC, 2025). State figures are population-weighted averages of county estimates calculated for this project; ratios are Coös divided by state.

Smoking stands out. The county's crude smoking prevalence is about 40% higher than the state figure, which translates to roughly 4,000 adults who currently smoke; if the county matched the state rate, about 1,150 fewer residents would. The other rows describe a population that is sicker, more often disabled and less able to reach care, which matters less for why COPD begins than for how it progresses once it does. A person who cannot drive forty miles to a hospital-based rehabilitation class three times a week will not complete one, however well the program is designed.

These comparisons are ecological. They show that the county with the most smoking also has the most COPD, but they cannot show that the smokers themselves are the residents who have COPD. That link has to come from studies of individuals.

Causal Evidence

Individual-level studies supply the causal link that county data cannot. In a Copenhagen cohort followed for 25 years, at least a quarter of those who kept smoking developed COPD, while those who quit early in follow-up had a far lower risk (Lokke et al., 2006). The cohort design establishes that smoking came first, and the length of follow-up shows how slowly the disease unfolds. For occupational exposure, a U.S. case-control study found that adults reporting vapors, gas, dust or fumes at work had about twice the odds of COPD, and that the combination of smoking and such exposure carried odds roughly fourteen times higher than neither (Blanc et al., 2009). For a county whose history includes paper mills and logging, that joint effect matters.

Together these studies meet several classic criteria for causation: temporality from the cohort, a strong association, a larger effect when two exposures combine and consistency across designs (Celentano & Szklo, 2019). They also point to where prevention will pay. Population attributable fractions are useful for setting priorities, but they cannot simply be added across exposures that interact, and they describe the share of cases that might be avoided only if the exposure were entirely removed, which no program achieves (Rockhill et al., 1998). The project therefore treats them as a way to rank targets, not as a forecast of cases prevented.

What Screening Can and Cannot Do

Finding undiagnosed COPD earlier is an appealing goal in a county with poor access. The evidence argues against screening adults who have no symptoms: the U.S. Preventive Services Task Force reaffirmed a D recommendation in 2022 because finding COPD in people without symptoms has not been shown to improve their health (US Preventive Services Task Force et al., 2022). The CAPTURE tool, studied in U.S. primary care, identified only 48.2% of patients with undiagnosed clinically significant COPD, though its specificity was 88.6% (Martinez et al., 2023). The recommendation leaves ample room for case finding, which means evaluating adults who already report cough, sputum or breathlessness, especially smokers and former mill workers.

Recommendations

The analysis leads to four actions, ordered by the size of the determinant each one addresses and by how firmly the evidence supports it. Table 2 summarizes them.

Table 2

Recommended Actions, Evidence and Measures

The first action is tobacco treatment delivered by nurses across clinics, home health agencies and inpatient units. A Cochrane review of 44 trials involving more than 20,000 participants found that nursing interventions increased the likelihood of quitting, with a risk ratio of 1.29 (95% CI 1.21 to 1.38), and that brief interventions worked about as well as intensive ones (Rice et al., 2017). To show the scale, suppose, as an illustration only, that 10% of smokers quit in a year with usual care. A risk ratio of 1.29 would raise that to about 13%, or roughly three extra quitters for every 100 smokers reached. If nurses reached 1,000 of the county's smokers in a year, about 29 more people would stop smoking than under usual care. That is modest in one year and large over a decade, because each quitter lowers the future COPD burden.

The second action is case finding. Nurses should ask every adult who smokes or once worked in dusty trades about cough, sputum and breathlessness, and route those who report symptoms to spirometry, including a portable spirometer that travels between the county's clinics so that confirmation does not depend on a trip south.

The third action addresses the transportation barrier directly. Pulmonary rehabilitation improves breathlessness, fatigue and quality of life in COPD, with benefits large enough to be clinically meaningful across 65 randomized trials (McCarthy et al., 2015). A later review of 15 studies found that rehabilitation delivered by video or telephone produced outcomes similar to those of center-based programs, with no safety concerns identified, though the certainty of that evidence was limited (Cox et al., 2021). Offering a video option, with a nurse or respiratory therapist leading from the hospital, lets patients in the county's remote towns complete a program they would otherwise never start.

The fourth action builds the data the county lacks. Clinics and the hospital should agree on a small shared measure set: tobacco use status and treatment offered, spirometry-confirmed diagnoses, rehabilitation referrals and completions, and COPD admissions and emergency visits per 1,000 adults, reported quarterly with counts under ten suppressed to protect privacy.

Evaluation Plan

It would be natural to judge the program by watching the county's PLACES smoking and COPD estimates fall. That approach would fail. The age-adjusted smoking estimate for Coös already carries a confidence interval running from 12.4% to 19.1%, nearly seven points wide, and a successful program might lower true prevalence by one or two points over several years. A change of that size would be invisible inside the uncertainty of a model-based estimate, and it could appear or disappear for reasons unrelated to anything the program did (Greenlund et al., 2022).

The evaluation will therefore rely on local measures. Process measures come first: the share of adult smokers seen in participating clinics whose chart shows tobacco treatment offered, the number of symptomatic adults referred for spirometry and the number of rehabilitation referrals and completed programs. Short-term outcomes follow: self-reported quit status at six months among those treated, and rehabilitation completion rates for video and in-person participants. The long-term outcome is the rate of COPD admissions and emergency visits per 1,000 adults, tracked quarterly for three years before and after launch. Comparing that trend with the same measure in a similar rural county without the program would help separate the program's effect from changes happening everywhere. PLACES estimates will still be reviewed each year, but as context, not as the test of success.

Limitations

Several limits apply. The county figures are modeled estimates and describe diagnosed disease only. The state comparison is a calculated average of county estimates rather than a published state figure. The causal studies were conducted in Denmark and in a California health plan population, whose smoking patterns and workplaces differ from those in northern New Hampshire. The practice datasets used in earlier milestones illustrated the calculations and play no part in the conclusions here. Finally, the illustrative quit calculation depends on an assumed baseline rate; the real local rate should replace it once clinic data are collected.

Conclusion

Coös County carries one of the heaviest COPD burdens in New Hampshire, with an estimated 2,500 adults diagnosed and a smoking rate well above the state's. Cohort and case-control evidence confirms that smoking, and in some workers occupational dust and fumes, drive that burden, while disability, distance and cost shape how it progresses. The recommended response puts nurses at the center: treating tobacco use at every contact, confirming COPD in adults with symptoms, bringing rehabilitation to patients who cannot travel to it and building the local data needed to see whether any of it works. Epidemiology has described the problem; nursing practice can now change its course.

References

Blanc, P. D., Iribarren, C., Trupin, L., Earnest, G., Katz, P. P., Balmes, J., Sidney, S., & Eisner, M. D. (2009). Occupational exposures and the risk of COPD: Dusty trades revisited. Thorax, 64(1), 6-12. https://doi.org/10.1136/thx.2008.099390

Celentano, D. D., & Szklo, M. (2019). Gordis epidemiology (6th ed.). Elsevier.

Centers for Disease Control and Prevention. (2025). PLACES: County data (GIS friendly format), 2025 release [Data set]. https://data.cdc.gov/d/i46a-9kgh

Cox, N. S., Dal Corso, S., Hansen, H., McDonald, C. F., Hill, C. J., Zanaboni, P., Alison, J. A., O'Halloran, P., Macdonald, H., & Holland, A. E. (2021). Telerehabilitation for chronic respiratory disease. Cochrane Database of Systematic Reviews, 2021(1), Article CD013040. https://doi.org/10.1002/14651858.CD013040.pub2

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, Article 210459. https://doi.org/10.5888/pcd19.210459

Lokke, A., Lange, P., Scharling, H., Fabricius, P., & Vestbo, J. (2006). Developing COPD: A 25 year follow up study of the general population. Thorax, 61(11), 935-939. https://doi.org/10.1136/thx.2006.062802

Martinez, F. J., Han, M. K., Lopez, C., Murray, S., Mannino, D., Anderson, S., Brown, R., Dolor, R., Elder, N., Joo, M., Khan, I., Knox, L. M., Meldrum, C., Peters, E., Spino, C., Tapp, H., Thomashow, B., Zittleman, L., Make, B., . . . CAPTURE Study Group. (2023). Discriminative accuracy of the CAPTURE tool for identifying chronic obstructive pulmonary disease in US primary care settings. JAMA, 329(6), 490-501. https://doi.org/10.1001/jama.2023.0128

McCarthy, B., Casey, D., Devane, D., Murphy, K., Murphy, E., & Lacasse, Y. (2015). Pulmonary rehabilitation for chronic obstructive pulmonary disease. Cochrane Database of Systematic Reviews, 2015(2), Article CD003793. https://doi.org/10.1002/14651858.CD003793.pub3

Rice, V. H., Heath, L., Livingstone-Banks, J., & Hartmann-Boyce, J. (2017). Nursing interventions for smoking cessation. Cochrane Database of Systematic Reviews, 2017(12), Article CD001188. https://doi.org/10.1002/14651858.CD001188.pub5

Rockhill, B., Newman, B., & Weinberg, C. (1998). Use and misuse of population attributable fractions. American Journal of Public Health, 88(1), 15-19. https://doi.org/10.2105/AJPH.88.1.15

US Preventive Services Task Force, Mangione, C. M., Barry, M. J., Nicholson, W. K., Cabana, M., Caughey, A. B., Chelmow, D., Coker, T. R., Davis, E. M., Donahue, K. E., Jaén, C. R., Kubik, M., Li, L., Ogedegbe, G., Pbert, L., Ruiz, J. M., Stevermer, J., Tseng, C.-W., & Wong, J. B. (2022). Screening for chronic obstructive pulmonary disease: US Preventive Services Task Force reaffirmation recommendation statement. JAMA, 327(18), 1806-1811. https://doi.org/10.1001/jama.2022.5692