Blood pressure reductions: correcting for regression to the mean

Reductions in high blood pressure (BP) from participating in screening and treatment programs are often assessed by comparing BP measurements before and after participation. The interpretation of such changes in measured blood pressure is confounded by the tendency of high pressures to decline as a result of a statistical artifact--regression to the mean. The problem arises whenever baseline measurements are used both for selection of participants and for comparisons with pressures obtained later. We developed a statistical model which predicts the average decline due to regression for participants in a screening or treatment program. This regression effect must be subtracted from the observed reduction in BP (the difference between baseline and later measurements) to obtain the average net reduction in BP from the program. The regression effect is estimated as the product of two factors. The first factor is the proportion of the variance in the baseline (preprogram) measurement due to measurement error and the short-term variation (e.g., 0.24 for two replications averaged). The second factor is the difference between the mean baseline pressure of full participants and that of the underlying population of potential participants. The model was first illustrated with successive BP measurements from community screening programs, where the "program" was only remeasurement. The mean observed decline in diastolic BP between screens for 145 persons with elevated baseline BP was 7 mm Hg. After adjustment for regression to the mean, the net decline between screens was estimated to be 2 mm Hg. This decline is apparently due to the pressor effect, or stress of screening, and agrees with findings from other studies. Next the model was applied to the treatment phase of the Hypertension Detection and Follow-up Program. Overall, net reductions predicted by the model agree with those from independent measurements to within 0.1 mm Hg. The findings indicate the one can compute net reductions in BP from before-and-after comparisons in screening and treatment programs with reasonable accuracy, and these net reductions are generally much smaller than the crude BP declines.