NUR 520 Module 6 Study Designs Paper Example

Four Questions, Four Designs: Choosing Epidemiologic Study Designs for COPD in a Rural County

[Student Name]

Southern New Hampshire University

NUR 520: Epidemiological and Biostatistical Applications in Healthcare

Module Six Assignment

[Instructor Name]

[Date]

Four Questions, Four Designs: Choosing Epidemiologic Study Designs for COPD in a Rural County

Every epidemiologic study is shaped by its design, and the design determines what kind of conclusion the study can support. Textbooks often present designs as a hierarchy; in practice the right design depends on the question. This paper takes four questions an MSN-prepared nurse might ask about COPD in Coös County, New Hampshire, and matches each to a design, with a published study to show what that design produces (Celentano & Szklo, 2019). It argues that the county data already available answer only the first kind of question, how common the disease is, and that the questions most useful for prevention require designs that follow people over time or compare people with and without disease.

Question 1: How Common Is COPD Here? Cross-Sectional Design

A cross-sectional study takes a single snapshot of a defined population, recording each person's exposures and health status together. It is the natural design for prevalence. The county estimates used in earlier modules come from a cross-sectional survey modeled to small areas, and national trends come from repeated cross-sectional surveys, such as the analysis showing age-standardized COPD prevalence near 6% from 2011 to 2021 (Liu et al., 2023). Cross-sectional data are relatively fast and inexpensive and show where the burden lies. Their main weakness for causal questions is temporality: because smoking status and COPD are measured together, the data cannot show which came first, and people who quit smoking after diagnosis may appear as former smokers with disease, blurring the association.

Question 2: How Much Does Smoking Raise the Risk? Cohort Design

A cohort study follows people without the outcome, grouped by exposure, and counts new cases over time. It establishes temporality and allows direct calculation of incidence and relative risk. The Copenhagen City Heart Study provides a clear example. Lokke et al. (2006) followed 8,045 adults aged 30 to 60 with normal lung function at baseline for 25 years. Among men, the share with normal lung function at the end ranged from 96% of never smokers to 59% of continuous smokers, and among women from 91% to 69%. The authors concluded that the absolute risk of developing COPD among continuous smokers is at least 25%, and that quitting, especially early, substantially reduced the risk. Cohort studies are powerful but slow and expensive; no county health department could run a 25-year cohort to answer a local question, which is why nurse leaders rely on published cohorts for evidence about risk.

Question 3: Did Mill Work Contribute? Case-Control Design

The case-control design runs in the opposite direction from a cohort: investigators first assemble people with COPD and a similar group without it, and only then compare what each group was exposed to in the past. It is efficient for rare outcomes or when exposures happened long ago, and it produces an odds ratio. Blanc et al. (2009) compared 1,202 adults with COPD with 302 matched referents from a managed care population and found that self-reported workplace exposure to vapors, gas, dust or fumes was associated with an odds ratio of 2.11 for COPD after adjusting for smoking and other factors, with an estimated 31% of COPD in the population attributable to such exposure; people with both smoking and occupational exposure had an odds ratio of 14.1. For Coös, where many adults worked in paper mills and forestry, a local case-control study could test whether those exposures contribute to the county's burden. Its weaknesses would be recall bias, since people with COPD may remember exposures differently, and the difficulty of choosing a comparable control group in a small population.

Question 4: Does an Intervention Work? Experimental Design

A randomized controlled trial assigns participants to an intervention or comparison by chance and measures outcomes, which controls for known and unknown confounders and supports causal conclusions about the intervention. Trials are unethical for testing harmful exposures, so no one would randomize people to smoke, but they are the right design for testing responses, such as whether a nurse-delivered telephone cessation program or home-based pulmonary rehabilitation improves outcomes. When randomization is not feasible in a small county, a quasi-experimental design, such as comparing outcomes before and after a program with a comparison county, is a reasonable alternative with weaker protection against bias.

The Ecological Fallacy

One more design issue affects county data. An ecological study compares groups, such as counties, rather than individuals. Coös has both the highest smoking estimate and the highest COPD estimate among New Hampshire counties, which is consistent with smoking causing COPD, but group-level association alone cannot show that the individuals who smoke are the ones with COPD. Assuming that it does is the ecological fallacy (Celentano & Szklo, 2019). Here, individual-level cohort and case-control studies supply the evidence that smoking causes COPD; the county data show where that relationship is producing the most disease.

Bias and Confounding Across Designs

Every design is vulnerable to bias, but in different ways. Cross-sectional surveys suffer when people with disease are more or less likely to respond, or when diagnosis depends on access to care, which may lead to undercounting in rural areas. Cohort studies lose participants over long follow-up, and if those who drop out differ in health or smoking, results can shift. Case-control studies depend on memory and on choosing controls from the same population that produced the cases. Confounding affects all of them: age, for example, is related to both smoking history and COPD, which is why the studies cited here adjusted for age and why earlier modules used age-adjusted prevalence. Recognizing the likely bias in each design is part of reading any study responsibly.

Choosing Designs for the Final Project

For the final project, the county's cross-sectional estimates will describe the burden, published cohort and case-control evidence will support the causal links between smoking, occupational exposure and COPD, and any proposed intervention will be framed as a quasi-experimental evaluation with a comparison period and, if possible, a comparison county. This combination uses each design for what it does best and avoids asking county prevalence data to answer causal questions they cannot address.

Conclusion

Cross-sectional data show how common COPD is in Coös County; cohort studies show how much smoking raises the risk over decades; case-control studies can test whether local exposures like mill work contribute; and trials or quasi-experiments show whether interventions help. Matching the design to the question, and resisting conclusions a design cannot support, is what makes epidemiologic evidence useful to nurse leaders.

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.

Liu, Y., Carlson, S. A., Watson, K. B., Xu, F., & Greenlund, K. J. (2023). Trends in the prevalence of chronic obstructive pulmonary disease among adults aged 18 years and older: United States, 2011-2021. Morbidity and Mortality Weekly Report, 72(46), 1250-1256. https://doi.org/10.15585/mmwr.mm7246a1

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