| Course | NUR-7630 Biostatistics and Clinical Epidemiology |
|---|---|
| Module | Module 5 |
| Paper type | Regression and confounding |
| Length | About 1,033 words, 6 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | Doctor of Nursing Practice |
| Updated | October 2026 |
Free sample paper for NUR 7630 Module 5
An Odds Ratio of 1.068 Per Patient: Reading Adjusted Estimates, Confounding and Causal Claims in Two Nurse Staffing Studies
Student Name
Doctor of Nursing Practice, Thomas Edison State University
NUR-7630: Biostatistics and Clinical Epidemiology
Mentor Name
Month Day, Year
An Odds Ratio of 1.068 Per Patient: Reading Adjusted Estimates, Confounding and Causal Claims in Two Nurse Staffing Studies
The Request
Our chief nursing officer is preparing a budget presentation to the hospital board that argues against cuts to registered nurse positions. She has two studies with odds ratios in their abstracts and asked me, as quality director and a DNP student, to explain what the numbers mean, how much they can be trusted and how to describe them honestly to a board that includes skeptical finance members. This paper answers those questions.
Odds and Odds Ratios
In logistic regression, the outcome is binary, such as whether a patient died within 30 days. Odds here means the chance of dying divided by the chance of surviving, and the model reports how each factor shifts those odds. An odds ratio compares the odds under two conditions. An odds ratio of 1.068 for each additional patient in a nurse's workload means that each extra patient multiplies a patient's odds of dying by 1.068, an increase of about 7 percent. Because only a small share of these patients die, odds and risk move almost together, so calling it about 7 percent more risk is fair.
Because the effect is multiplicative, it compounds. Two extra patients multiply the odds by 1.068 squared, about 1.14, and three by about 1.22.
A worked example makes this concrete for the board. If a unit's baseline 30-day mortality is 2 percent and its nurses each carry five patients, raising the load to seven multiplies the odds by about 1.14, which moves the expected mortality from 2.0 to roughly 2.3 percent. For a unit discharging 3,000 patients a year, that difference would mean about nine more deaths, a figure that is illustrative rather than a prediction, since our hospital's own baseline and case mix would change it.
The European Study
Aiken et al. (2014) linked surveys of more than 26,000 nurses with discharge data on more than 420,000 surgical patients in 300 hospitals in nine European countries. Their models held constant each patient's age, sex, emergency or planned admission and other illnesses, and each hospital's bed count, teaching role and technology; with all that fixed, one more patient on the average nurse's list carried an odds ratio for death within 30 days of 1.068, interval 1.031 to 1.106. Education pointed the other way: every ten-point rise in the proportion of degree-holding nurses went with an odds ratio of 0.929 (0.886 to 0.973), roughly 7 percent lower odds. Combining both estimates, the authors calculated that a hospital where each nurse looked after six people and three in five nurses held the degree would lose almost 30 percent fewer surgical patients than a hospital running eight to a nurse with fewer than a third of nurses degree-prepared.
The Illinois Study
Lasater et al. (2021) turned to Illinois, where Medicare patients in 87 hospitals faced roughly 16 percent higher odds of dying within a month for each extra patient on a medical-surgical nurse's assignment, an adjusted odds ratio of 1.16 (1.04 to 1.28).
| Study | Exposure | Adjusted odds ratio (95 percent CI) |
|---|---|---|
| Aiken et al. (2014) | Each extra patient per nurse | 1.068 (1.031 to 1.106) |
| Aiken et al. (2014) | Each 10 percent more bachelor's nurses | 0.929 (0.886 to 0.973) |
| Lasater et al. (2021) | Each extra patient per nurse | 1.16 (1.04 to 1.28) |
What Adjustment Does
In this setting a confounder would be any hospital or patient trait that both shapes staffing levels and, separately, shapes who dies, without being a step through which staffing acts. In staffing research, a hospital's size, teaching status, technology and the sickness of its patients may affect both how many nurses it employs and how many patients die. If a large teaching hospital with sicker patients also has better staffing, an unadjusted analysis could make staffing look less protective than it is, or more. Multivariable regression estimates the association of staffing with mortality while holding the measured confounders constant. Skelly et al. (2012) describe adjustment as one of several strategies for controlling confounding, alongside restriction, matching and stratification, each controlling only for the factors that are measured and included.
What Adjustment Cannot Do
Residual confounding remains whenever important factors are unmeasured or measured crudely. A hospital with strong leadership, good management and a culture of safety might both staff well and achieve low mortality for reasons the studies did not capture. Reverse causation is also possible in principle: a hospital in financial trouble might cut nurses and also cut other resources that affect outcomes. Because these studies are observational, they show that better staffing is consistently associated with lower mortality after accounting for many factors, not that staffing alone causes the difference.
Strengthening the Case
Three features make the association more persuasive. It appears across countries, years and data sets. It shows a dose-response pattern, with each additional patient adding risk. And it is biologically and clinically plausible, since nurses with fewer patients can detect deterioration sooner. A prospective study of ratio legislation in Queensland, which compared hospitals subject to the policy with similar hospitals over time, adds evidence from a stronger design. Together, these support a causal interpretation more strongly than any single cross-sectional study can.
Framing the Numbers for Trustees
The chief nursing officer should say that large studies in Europe and Illinois found each additional patient per nurse associated with roughly 7 to 16 percent higher odds of a surgical or medical patient dying within a month, after accounting for hospital and patient differences, and that the pattern is consistent across studies. She should not say that cutting nurses will cause a specific number of deaths, because the studies cannot support that precision for our hospital. Presenting the confidence intervals and the consistency of the evidence is more credible to skeptical listeners than overstating certainty.
Conclusion
Adjusted odds ratios of 1.068 and 1.16 per additional patient mean modest increases in odds for each patient that compound across a heavier workload. Adjustment for measured confounders strengthens confidence, but residual confounding means the studies show association rather than proof. The consistency, dose-response pattern and plausibility of the evidence justify using it in the budget argument, presented honestly with its limits.
References
Aiken, L. H., Sloane, D. M., Bruyneel, L., Van den Heede, K., Griffiths, P., Busse, R., Diomidous, M., Kinnunen, J., Kózka, M., Lesaffre, E., McHugh, M. D., Moreno-Casbas, M. T., Rafferty, A. M., Schwendimann, R., Scott, P. A., Tishelman, C., van Achterberg, T., & Sermeus, W. (2014). Nurse staffing and education and hospital mortality in nine European countries: A retrospective observational study. The Lancet, 383(9931), 1824-1830. https://doi.org/10.1016/S0140-6736(13)62631-8
Lasater, K. B., Aiken, L. H., Sloane, D., French, R., Martin, B., Alexander, M., & McHugh, M. D. (2021). Patient outcomes and cost savings associated with hospital safe nurse staffing legislation: An observational study. BMJ Open, 11(12), Article e052899. https://doi.org/10.1136/bmjopen-2021-052899
Skelly, A. C., Dettori, J. R., & Brodt, E. D. (2012). Assessing bias: The importance of considering confounding. Evidence-Based Spine-Care Journal, 3(1), 9-12. https://doi.org/10.1055/s-0031-1298595
NUR 7630 Module 5 instructions, in plain terms
The determinants of health and disease in populations appear in TESU's NUR-7630 catalog description, and with the syllabus kept in the classroom, this fifth module reads determinants through multivariable regression. Expect to interpret coefficients or odds ratios from a published model, explain what adjustment controls and what remains uncontrolled, judge whether a causal claim is warranted and translate the findings for a nonstatistical audience. Some sections supply regression output to interpret directly. Confirm whether you must discuss a specific causal framework, and whether the audience for your translation is named. Pick a study whose estimates someone in your organization wants to use, so the interpretation has stakes and an audience.
How the NUR 7630 Module 5 example is put together
A chief nursing officer's request for help with a board presentation opens the paper. A section explains odds, odds ratios and how per-unit effects compound. Next come the European estimates for workload and degree mix and the Illinois workload estimate, laid side by side in a small table. A section explains what adjustment does, using hospital size and patient severity as examples and a review of confounding control. Another explains residual confounding with named unmeasured factors. Consistency, dose-response and a stronger design strengthen the case, and a section scripts how to present the findings honestly to the board. A worked example converts the odds ratio into deaths for a unit of 3,000 discharges.
Reading the NUR 7630 Module 5 grading rubric
Regression interpretation papers are graded on correct interpretation of coefficients or odds ratios and their intervals, a clear explanation of confounding and adjustment, recognition of residual confounding and careful language about causation. Mentors look for the distinction between odds ratios and risk ratios where it matters and for compounding explained correctly, ideally with a number of patients rather than a percentage alone. Translating results for a nonstatistical audience without overstating them shows professional judgment, and a worked example from a realistic baseline makes the translation concrete. Using criteria such as consistency and dose-response to weigh causal interpretation adds depth. APA references to the primary studies finish the paper.
Common NUR 7630 Module 5 mistakes, and how to avoid them
Students often write that an adjusted odds ratio proves cause because confounders were controlled. Adjustment covers only what was measured. Another frequent error is treating an odds ratio of 1.068 as trivial without noting that it applies per patient and compounds. Interpret confidence intervals, not just point estimates, and give both ends a meaning for the audience you are writing for. Name plausible unmeasured confounders. When writing for decision makers, avoid precise predictions the data cannot support. Explain whether odds ratios approximate relative risks in your example, and try one worked example with a realistic baseline rate. The desk will interpret the regression results from your own NUR-7630 article in a first draft, free of charge.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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NUR 7630 Module 5 questions, answered
Where can I find a free NUR 7630 Module 5 sample paper?
The staffing regression paper is printed above, with the table of adjusted odds ratios and a note on each sheet.
What does an odds ratio of 1.068 per patient mean?
Each additional patient in a nurse's workload multiplies a patient's odds of death by 1.068, about 7 percent higher, and the effect compounds as patients are added.
What is residual confounding?
Confounding that remains after adjustment because important factors were not measured or were measured poorly, such as leadership or safety culture in staffing studies.
When does an odds ratio approximate a relative risk?
When the outcome is uncommon, as with 30-day mortality, the odds ratio and relative risk are close; with common outcomes the odds ratio exaggerates the relative risk.
Do nurse staffing studies prove that more nurses save lives?
They show a consistent, dose-related association after adjustment for many factors, which supports but does not prove a causal effect.