Motivated Patients Go to Class: Testing Whether Diabetes Education Causes Fewer Emergency Visits Against Healthy-User Bias, Confounding and Hill's Viewpoints, With the Flu Vaccine Lesson in Mind
[Student Name]
University of Phoenix
DNP/701: Biostatistics and Epidemiology
Week 3 Assignment
[Instructor Name]
[Date]
The clinic and its figures are a composite written for a model paper.
My Week 2 analysis showed that, in our rural clinic network, referred patients who went to diabetes education had 40% less risk of a diabetes-related emergency visit over a year than referred patients who stayed away. Before recommending that our director expand the program, I need to ask whether education caused the difference or whether something else did. This paper examines bias, confounding and causation for this finding.
Bias
Grimes and Schulz (2002) describe bias in observational research as systematic error that leads to incorrect estimates of association, grouping it into selection bias, from how participants are chosen or choose themselves, and information bias, from how data are measured. They emphasize that bias cannot be fixed by larger samples and must be addressed by design. They also describe confounding, in which a third factor linked to both the exposure and the outcome distorts the association.
Selection Bias in Our Analysis
Patients were not assigned to education; they chose whether to attend. Those who attended may be more motivated, more organized, have reliable transportation and have fewer mental health or social problems. Each of these traits could lower emergency visits independently of education.
The Influenza Vaccine Lesson
Jackson et al. (2006) studied seniors and found that vaccinated people had a relative risk of death of 0.39 compared with unvaccinated people before influenza season began, when the vaccine could not have had any effect. The association weakened during and after the season. The authors concluded that healthier seniors were more likely to be vaccinated and that this bias was large enough to account entirely for the apparent benefit during influenza season. Adjusting for diagnosis codes did not remove the bias.
If the vaccinated were less likely to die before the flu even arrived, the vaccine was not the reason; the same test can be applied to my education program.
Applying the Test
Following the logic of Jackson et al. (2006), I compared emergency visits in the 12 months before referral. Attendees had a rate of 16% compared with 23% for nonattenders. Attendees already had fewer emergency visits before education, which suggests that at least part of the difference after education reflects who chose to attend.
Why Larger Samples Do Not Help
If our clinic doubled its sample, the confidence interval around the relative risk would narrow, but the bias would remain. A precise estimate of a biased association is still wrong. Grimes and Schulz (2002) stress that bias must be prevented by design or addressed in analysis, not overcome by numbers.
Reverse Causation
Could the outcome influence the exposure? A patient with a recent emergency visit might be too ill, or too discouraged, to attend education. In that case, early emergencies would reduce attendance rather than attendance reducing emergencies. Limiting the analysis to emergencies that occurred after the education period would help separate these directions.
Information Bias
Our outcome, diabetes-related emergency visits, comes from records. If attendees visit our clinic more often, their emergency visits may be more completely recorded, or less. Visits to emergency departments outside our network may be missed for both groups. Misclassification that differs between groups could bias the result in either direction.
Confounding
Grimes and Schulz (2002) note that confounding can be addressed through restriction, matching, stratification or multivariable adjustment. Baseline A1c is a candidate confounder: patients with very high A1c may be less likely to attend and more likely to have emergencies. When I stratified by baseline A1c, the relative risk was 0.72 among patients with A1c below 9% and 0.68 among those at 9% or higher. Both are closer to 1 than the crude 0.60, suggesting that baseline control explains part of the association.
Residual Confounding
Even after stratification, unmeasured confounders such as motivation, health literacy and social support remain. These cannot be adjusted for because they are not in our records.
Hill's Viewpoints
Hill (1965) proposed considerations for moving from association to causation. Strength: the adjusted association is modest. Consistency: other studies of diabetes education show benefits in A1c, but evidence on emergency use is mixed. Temporality: education preceded the outcome. Biological gradient: patients who attended all six sessions had fewer visits than those who attended four, which supports a dose-response relationship, though motivation could also explain it. Plausibility: education could reduce emergencies through better self-management of glucose. Experiment: randomized evidence would be the strongest test. Hill emphasized that none of these considerations is required or sufficient alone.
My Conclusion on Causation
Education may reduce emergency visits, but the size of our observed effect is likely inflated by selection and confounding. The before-referral difference shows that attendees were already at lower risk.
Effect Modification Is Not Confounding
The stratified relative risks, 0.72 and 0.68, are similar, so baseline A1c appears to confound rather than modify the association. If they had differed greatly, education might work differently at different levels of control, and a single summary estimate would be misleading.
Information From Patients
A short survey of nonattenders found that transportation and work hours were the most common reasons for not attending. These factors are also linked to emergency use, which reinforces the concern about confounding by social circumstances.
A Better Design
To estimate the true effect, our clinic could randomize the order in which sites offer education, or use an active outreach approach so that attendance depends less on patient choice. A comparison of changes within patients before and after education would also control for stable differences between people.
A Negative Control Outcome
The influenza study used a period when the vaccine could not work as a check (Jackson et al., 2006). I can use an outcome that education should not affect, such as emergency visits for injuries. If attendees also have fewer injury visits, that points to general differences in health and behavior rather than an effect of diabetes education. In our data, attendees had 30% fewer injury visits, which strengthens the case for bias.
Propensity Methods
With more data, a propensity score, the predicted probability of attending based on measured characteristics, could be used to match attendees with similar nonattenders. Propensity methods balance measured confounders but, like stratification, cannot account for unmeasured ones such as motivation.
What I Will Tell the Director
The program is associated with fewer emergency visits, but part of that difference reflects who chooses to attend. Expanding the program and evaluating it with a stronger design would answer the question more reliably than the current data.
Conclusion
The association between diabetes education and fewer emergency visits is vulnerable to healthy-user bias, as a similar pattern exposed in influenza vaccine research shows, and to confounding by baseline A1c. Stratification weakened the association, and a pre-referral comparison showed attendees were at lower risk from the start. Applying Hill's viewpoints supports a plausible effect but not a confident causal claim.
References
Grimes, D. A., & Schulz, K. F. (2002). Bias and causal associations in observational research. The Lancet, 359(9302), 248-252. https://doi.org/10.1016/S0140-6736(02)07451-2
Hill, A. B. (1965). The environment and disease: Association or causation? Proceedings of the Royal Society of Medicine, 58(5), 295-300. https://doi.org/10.1177/003591576505800503
Jackson, L. A., Jackson, M. L., Nelson, J. C., Neuzil, K. M., & Weiss, N. S. (2006). Evidence of bias in estimates of influenza vaccine effectiveness in seniors. International Journal of Epidemiology, 35(2), 337-344. https://doi.org/10.1093/ije/dyi274
How this DNP 701 Week 3 example is structured
The DNP/701 Week 3 work usually addresses bias, confounding and causation. This paper takes one association from practice data and challenges it systematically, showing how each threat could produce the association and what analysis or design could address it. Students search this week as DNP 701 Week 3, DNP701 Wk 3 or DNP/701 Wk 3; all three are the same assignment.
DNP/701 Week 3 questions, answered
What does DNP/701 Week 3 usually ask for?
Many sections ask students to identify bias and confounding in a study or practice analysis and to evaluate whether an association is likely to be causal.
What is healthy-user bias?
A form of selection bias in which people who choose a preventive intervention are healthier or more health-conscious than those who do not, making the intervention appear more effective than it is.
What are Hill's viewpoints?
Nine considerations proposed by Austin Bradford Hill for judging whether an association is causal, including strength, consistency, specificity, temporality, biological gradient, plausibility, coherence, experiment and analogy.
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