NUR 520 Module 2 Data Sources Paper Example

Where the Numbers Come From: Evaluating Data Sources for COPD in a Small Rural County

[Student Name]

Southern New Hampshire University

NUR 520: Epidemiological and Biostatistical Applications in Healthcare

Module Two Paper

[Instructor Name]

[Date]

Where the Numbers Come From: Evaluating Data Sources for COPD in a Small Rural County

Module 1 described COPD in Coös County, New Hampshire, using a single number: an estimated 9.5% of adults with diagnosed COPD, the highest in the state. A number like that is only as trustworthy as the process that produced it. This paper evaluates four sources of data on COPD for a small rural county and asks, for each, how the data were collected, what they actually measure and how much confidence a nurse leader should place in them. It argues that no single source is adequate, that model-based estimates are the best available picture of prevalence in a county this size, and that they must be read alongside mortality data and local clinical records that show different parts of the problem.

Source 1: PLACES Model-Based Estimates

PLACES, a collaboration between CDC, the Robert Wood Johnson Foundation and the CDC Foundation, publishes modeled figures for dozens of health measures at the county, place and census tract level nationwide (Greenlund et al., 2022). The estimates are not direct counts. They combine national survey responses with census population data in a statistical model, a method known as multilevel regression and poststratification, to predict prevalence in small areas where the survey has too few respondents for a direct estimate. When this approach was first tested for COPD, model-based county estimates correlated with direct survey estimates at 0.88 to 0.95 across counties where direct estimates were possible (Zhang et al., 2014).

For Coös County, PLACES is the most practical source of prevalence data, and the 2025 release reports a confidence interval with each estimate, 7.7% to 11.4% for crude COPD prevalence (Centers for Disease Control and Prevention [CDC], 2025). Its limits follow from its design. Because the model borrows strength from similar populations elsewhere, a county's estimate partly reflects its demographic makeup rather than only local conditions, and local factors the model does not include, such as a history of mill work, will not appear in the estimate.

Source 2: The Behavioral Risk Factor Surveillance System

PLACES is built on the Behavioral Risk Factor Surveillance System (BRFSS), a state-based telephone survey of adults that asks, among other questions, whether a health professional has ever told the respondent they have COPD, emphysema or chronic bronchitis. BRFSS data underlie national trend reports, including the finding that age-standardized COPD prevalence was about 6% from 2011 to 2021 (Liu et al., 2023). Its strengths are a consistent method across states and years and data on risk factors such as smoking alongside disease. Its weaknesses matter for COPD. Prevalence is self-reported and depends on diagnosis, so it misses people with undiagnosed disease, which may be common in areas with limited access to spirometry. Telephone surveys also have low response rates, and results depend on weighting to represent the population.

Source 3: Mortality Data

Death certificate data, available through CDC WONDER, record COPD as an underlying or contributing cause of death by county and year (CDC, n.d.). Mortality data are complete in the sense that nearly all deaths are registered, and they capture the most severe end of the disease. For a county of about 31,000 people, however, annual COPD deaths are small numbers, so rates fluctuate from year to year and may be suppressed when counts are very low. Several years must be combined for a stable rate. Death certificates also depend on how physicians record causes, and COPD is often listed as a contributing rather than underlying cause, which can lead to undercounting when only underlying causes are used.

Source 4: Local Clinical and Claims Records

Hospitals, clinics and insurers in the county hold data that no public source provides: emergency visits and admissions for COPD exacerbations, readmissions, spirometry use and prescriptions. These records are timely and detailed and can show patterns by town or clinic. They describe only people who reached care, however, and each organization's data cover only its own patients. Access requires data agreements, and privacy rules limit what can be shared. For an MSN nurse leading a local program, these records are essential for measuring the program's own effect, but they cannot describe the county's full burden.

Comparing the Sources

Taken together, the sources answer different questions. PLACES answers how common diagnosed COPD and smoking are in the county compared with elsewhere, with stated uncertainty. BRFSS explains how those estimates were produced and supports state and national comparisons. Mortality data show how often COPD contributes to death, averaged across years. Local records show how people with COPD use care and whether a program changes that. None of the sources measures undiagnosed COPD, and none captures occupational or household exposures well. Those gaps suggest questions a local survey or clinic screening project would have to answer.

Ethics, Privacy and Small Numbers

Data about a small county raise privacy questions that national data do not. When a town has a few hundred adults, a table of COPD admissions by age and sex can point to identifiable people, which is why public sources suppress small counts and why local clinical data must be shared only under agreements that protect patients. A nurse leader using those records should report aggregated figures, suppress cells below an agreed threshold and avoid naming towns where counts are tiny. There is also an ethical duty in how data are presented. Describing Coös County only through its high disease rates can stigmatize a community that has strengths the data do not show, so reports should pair burden with context, such as access barriers, and with what residents and local organizations are already doing.

Conclusion

The estimate that 9.5% of Coös adults have diagnosed COPD is a well-built model-based figure with a stated confidence interval, drawn from a self-reported survey that likely misses undiagnosed disease. It is the best available picture of prevalence for a county this small, but it should be used with mortality data averaged over several years and with local clinical records that show how people with COPD use care. Knowing where each number comes from is what allows a nurse leader to use it without overclaiming.

References

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

Centers for Disease Control and Prevention. (n.d.). CDC WONDER. https://wonder.cdc.gov/

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

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

Zhang, X., Holt, J. B., Lu, H., Wheaton, A. G., Ford, E. S., Greenlund, K. J., & Croft, J. B. (2014). Multilevel regression and poststratification for small-area estimation of population health outcomes: A case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology, 179(8), 1025-1033. https://doi.org/10.1093/aje/kwu018