IHP 525 Module 8 Communication Paper Example

Telling the Board What We Know and How Sure We Are: Communicating the Blood Pressure Results

[Student Name]

Southern New Hampshire University

IHP 525: Biostatistics

Module Eight Paper

[Instructor Name]

[Date]

Telling the Board What We Know and How Sure We Are: Communicating the Blood Pressure Results

Good analysis can still lead to poor decisions if it is misunderstood. The Bayview County board of health meets next month to decide whether to fund expansion of the blood pressure program. Its seven members include a pastor, a retired teacher, two physicians and three elected officials. This paper sets out how the evaluation's results and their uncertainty should be communicated so that each member can weigh them fairly.

Uncertainty Is Part of the Message

Spiegelhalter (2017) reviewed research on communicating risk and uncertainty and argued that being open about uncertainty need not undermine trust; audiences generally respond well to honest ranges when they are presented clearly. He distinguished uncertainty about future events, such as whether a given person will have a stroke, from uncertainty about the underlying facts, such as the true size of a program's effect, and uncertainty arising from the quality of the evidence itself. All three apply here, and the board summary should address each.

Lead With the Size of the Benefit

The first sentence board members read should state the effect in units they recognize: participants lowered their top blood pressure number by about 6 points more than similar patients receiving usual care over six months. The range comes next: our best estimate is 6 points, and the data are consistent with a benefit anywhere from about 4 to 8 points. Only then should the summary say that a benefit this large, sustained across a population, is linked to fewer strokes and heart attacks. P-values are left to the appendix.

Counts Out of 100 Instead of Odds Ratios

Gigerenzer et al. (2007) documented how often doctors, patients and journalists misread health statistics, and they showed that understanding improves when risks are expressed as simple counts in a defined group rather than as conditional probabilities or relative changes. An odds ratio of 2.14 invites the misreading that participants were twice as likely to reach control. Stated as counts, the finding is clearer and more modest: out of every 100 participants, about 46 reached a healthy reading at six months, compared with about 29 of every 100 usual-care patients.

Absolute Before Relative

Gigerenzer and colleagues also warned that relative changes presented alone exaggerate benefits. Saying the program raised control by 58% is technically true, since 46 is about 58% more than 29, but it sounds larger than the absolute gain of 17 people per 100. The board summary will give the absolute figure first and mention the relative figure only if members ask. The same rule applies to costs: the program costs about $410 per participant, or roughly $2,400 for each additional person brought to control.

Table 1. Two Ways to Say the Same Result

Note. All three statements describe the same composite data.

The Women's Result

The most delicate message concerns women. Their estimated benefit was about 4 points, with a range from slightly below zero to 8 points, compared with about 8 points among men. A hurried reading would conclude that women gain nothing from the program, and Altman and Bland (1995) warned against exactly this move: when a study fails to show a clear effect, that means the evidence is inconclusive, not that there is no effect. The honest message is that women likely benefit too, perhaps less than men, and the data cannot yet say how much less.

What the Board Should Not Hear

Three phrases will be avoided. Saying the program was proven effective overstates an observational comparison. Saying it did not help women misreads an inconclusive interval. Saying the results were statistically significant conveys nothing useful to most members and hides the size of the effect. Each will be replaced with a plain statement of what was found and how sure we are.

Being Clear About the Evidence Behind the Numbers

Spiegelhalter's third kind of uncertainty, the quality of the evidence, matters because participants chose to join. The summary will say plainly that the comparison group was similar but not identical, that adjustment for age, sex, starting pressure and medicines did not change the conclusion and that people who volunteer may be more motivated. A short verbal rating, such as moderate confidence that the program helps, gives members a sense of how much weight to place on the numbers without technical terms.

Explaining Home Monitoring Results

The board will also consider offering home monitors countywide. Here counts are essential. Among 100 residents flagged by a home monitor in the program, about 77 truly have high pressure. Among 100 flagged if monitors were handed to all adults, only about 47 would, because high pressure is less common in the general public. Members should hear that a countywide offer means many follow-up visits for people who turn out to be fine, and that nurse capacity must be budgeted for that.

Using Visuals

A single chart will show the estimated benefit as a dot with a horizontal bar for its range, with a shaded band marking the 5-point benefit clinicians consider important. A second graphic will use 100 small figures in two rows to show 46 versus 29 people reaching control. Both will carry one-line captions in plain words. Axes will start at zero where a bar is used, so differences are not exaggerated.

One-Page Board Summary

The summary will contain five short statements. First, participants lowered blood pressure about 6 points more than similar patients, likely between 4 and 8. Second, for each 100 people enrolled, roughly 17 extra reached a healthy reading. Third, women probably benefit, but we are less sure how much. Fourth, we have moderate confidence because people chose to join. Fifth, expansion to four clinics would cost about $390,000 a year and, if results hold, bring roughly 160 more people to control.

Conclusion

Communicating uncertainty well means leading with the size of an effect, expressing it in counts and absolute terms, stating the range honestly, avoiding the trap of reading an inconclusive result as no effect and being candid about the quality of the evidence. Done this way, the board can make an informed decision and will be better prepared if later results differ.

References

Altman, D. G., & Bland, J. M. (1995). Absence of evidence is not evidence of absence. BMJ, 311(7003), 485. https://doi.org/10.1136/bmj.311.7003.485

Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., Schwartz, L. M., & Woloshin, S. (2007). Helping doctors and patients make sense of health statistics. Psychological Science in the Public Interest, 8(2), 53-96. https://doi.org/10.1111/j.1539-6053.2008.00033.x

Spiegelhalter, D. (2017). Risk and uncertainty communication. Annual Review of Statistics and Its Application, 4, 31-60. https://doi.org/10.1146/annurev-statistics-010814-020148