NUR 8050 Module 2 Quantitative Appraisal: A Quasi-Experimental Oral Care Study Example

A 37 Percent Drop Without a Control Group: Appraising the Sutter Health Study of Basic Nursing Care and Hospital Pneumonia

Student Name

Doctor of Nursing Practice, Thomas Edison State University

NUR-8050: Scholarly Inquiry: The Basis for Evidence-Based Practice

Mentor Name

Month Day, Year

A 37 Percent Drop Without a Control Group: Appraising the Sutter Health Study of Basic Nursing Care and Hospital Pneumonia

Introduction

My question, set in the first module, is whether reliable toothbrushing cuts pneumonia among never-ventilated patients on my unit. The search identified one study that most closely matches the question: Quinn et al. (2014), who introduced an oral care initiative across a Sacramento medical center and reported a large fall in pneumonia. Because this study is likely to be the strongest direct evidence for my intervention, it deserves a careful appraisal rather than a summary. This paper asks whether the study's design can support its conclusion and how much weight it should carry in my project.

What the Study Did

The study had two aims: to estimate how often the condition occurred and to test whether reliably delivered oral care reduced it. The authors identified cases using diagnosis codes for pneumonia not present on admission, then confirmed each against the CDC surveillance definition. They completed a gap analysis, designed a site-specific oral care initiative and introduced it, guided by a behavior change model and participatory action research, and compared outcomes in the year after implementation with the period before. The design is described as descriptive and quasi-experimental, using retrospective comparison.

The Results Reported

Quinn et al. (2014) reported that cases per 100 patient-days slid from 0.49 to 0.30 once the initiative was running, close to a 39 percent relative drop, and that total cases over the intervention year were 37 percent fewer. By their own arithmetic the fall in cases meant eight deaths prevented, 500 bed-days not used and roughly $1.72 million not spent, set against $117,600 of brushes, paste and suction kits.

Appraising the Design

Comparing one year with the next, with no similar hospital measured alongside, leaves every other change of that year tangled up with the initiative. Hospitals change constantly: staffing, case mix, coding practices, infection prevention campaigns and seasonal patterns of respiratory illness all shift from year to year. Penfold and Zhang (2013) explain that a simple two-period comparison cannot account for an underlying trend; if pneumonia rates were already falling, the comparison would credit the intervention with a decline that would have happened anyway. An interrupted time series with multiple data points before and after would test whether the change departed from the existing trend. The study does not report this kind of analysis, which is the most important limitation for my purposes.

The intervention itself was also a bundle of changes, including new supplies, education, a behavior change approach and attention from leaders. Even if the fall was caused by the initiative, the study cannot say which part mattered. For my project this is less of a problem than it seems, since I intend to adopt the whole approach, but it limits what I can claim about toothbrushing alone.

The headline findings concern pneumonia rates and costs, and a reader planning to copy the initiative will also want a clear figure for how often patients actually received oral care afterward. Without knowing how often patients actually received oral care after the initiative, a reader cannot tell whether a smaller effect at another site would mean the intervention fails or that it was delivered less reliably.

Appraising Case Finding

Case finding through diagnosis codes depends on documentation and coding, which can change when staff become aware of a condition. Confirming each case against the CDC definition strengthens the outcome measure. Even so, an intervention that raises awareness can increase recognition and coding of the condition, which would bias the result toward no effect, or change documentation habits in ways that reduce coded cases, which would bias it toward benefit. The direction of any bias is uncertain, and the study does not report whether the people confirming cases knew whether they came from before or after the initiative.

The incidence aim of the study raises a related issue. In the course of looking for unreported cases, the team found many that had never been counted. A program that finds hidden cases in its first months and then counts reliably afterward can produce a pattern in either direction, depending on when the search intensified. For my project, the lesson is to settle the case-finding method before the baseline period is measured and keep it fixed throughout, with the same infection preventionist applying the same definition to every month.

Appraising the Cost Claims

The cost and lives saved figures are estimates built on assumptions about the cost and mortality of each case, applied to the number of cases avoided. They are useful for persuading leaders but inherit every uncertainty in the case reduction itself. If part of the fall was due to secular trend, part of the savings was too. I will cite these figures as the authors' estimates and not as measured outcomes.

Fit With My Setting

Several features match my unit: adult medical-surgical patients, a nursing-led intervention and basic supplies. Others differ: a large teaching medical center in California with a quality department able to support a system-wide initiative, compared with my single unit in a community hospital. The baseline rate of 0.49 per 100 patient-days, or 4.9 per 1,000, was higher than my unit's estimated 1.2 per 1,000, and the national range reported by Baker and Quinn (2018) suggests rates vary widely with case finding methods. A smaller starting rate may leave less room for reduction and make a change harder to detect.

Evidence Level and Use

On a typical hierarchy for nursing evidence, a quasi-experimental study without a concurrent control sits at level II to III, depending on the tool. The quality is moderate: a clear question, a defined outcome confirmed against a standard definition and a meaningful sample, weakened by the lack of a control group or trend analysis. My judgment is that the study makes the intervention plausible and worth testing, with low risk and low cost, but does not prove that it works. My project will therefore use the study to justify the intervention and will adopt a design, an interrupted time series, that addresses its main weakness.

Conclusion

Quinn et al. (2014) reported a substantial fall in pneumonia after an oral care initiative, and their study is the most direct evidence for my intervention. Its before and after design cannot rule out secular trend, its case finding may have been affected by awareness and its cost figures are estimates. It supports trying the change and shapes the design I will use to evaluate it.

References

Baker, D., & Quinn, B. (2018). Hospital Acquired Pneumonia Prevention Initiative-2: Incidence of nonventilator hospital-acquired pneumonia in the United States. American Journal of Infection Control, 46(1), 2-7. https://doi.org/10.1016/j.ajic.2017.08.036

Penfold, R. B., & Zhang, F. (2013). Use of interrupted time series analysis in evaluating health care quality improvements. Academic Pediatrics, 13(6 Suppl.), S38-S44. https://doi.org/10.1016/j.acap.2013.08.002

Quinn, B., Baker, D. L., Cohen, S., Stewart, J. L., Lima, C. A., & Parise, C. (2014). Basic nursing care to prevent nonventilator hospital-acquired pneumonia. Journal of Nursing Scholarship, 46(1), 11-19. https://doi.org/10.1111/jnu.12050