Over 78 million American adults, or about one third of the population over 20 years old, suffer from hypertension, with an even higher prevalence in priority populations such as African American (AA) adults at 44%. Left untreated, hypertension can lead to a range of serious and costly health concerns, such as cardiovascular disease, stroke, and renal disease. While 82% of adults with hypertension are aware of their condition, only about half have their blood pressure (BP) under control. Among the many factors associated with a lack of BP control, patients' non-adherence to prescribed antihypertensive medications is a major concern. While little doubt exists that patients who more poorly adhere to their prescribed antihypertensive medication are at risk for worse BP outcomes, it is less clear how to accurately measure the impact of time- varying patient adherence on BP levels, and how to make predictions of BP trajectories that would be helpful for clinical-decision making. This proposed project intends to address these questions. In this study, we will develop a novel approach to measuring BP control based on daily measurements of adherence through Bayesian dynamic linear models (DLM), and apply the approach to a pre-existing cohort of hypertensive patients. DLMs have a long history as a statistical framework for forecasting and measuring trajectories in many contexts, including real-time missile tracking as well as financial securities forecasting. The application of DLMs to measuring BP control is novel, but has a natural logic because BP can be tracked over time as adherence data accumulate.
The aims of our study involve (1) developing a Bayesian dynamic linear model framework for predicting BP levels from daily antihypertensive medication adherence, and assess the fit and applicability of the models, (2) assessing the extent of improvement of the DLM over more traditional medication adherence summaries to measure the effects of different socio-demographic patient characteristics including race, different comorbid conditions, and different antihypertensive medication regimens, on BP levels, and (3) developing an updating algorithm from the DLM that predicts credible ranges of future BP as a function of anticipated medication adherence, given current BP levels and covariate information. The results of the third aim, in particular, would be invaluable to clinicians through a web-based or PDA tool as a means to monitor whether a patient's BP levels were too high relative to what might be expected based on good medication adherence, and would therefore be potentially useful as a clinical-decision making tool. If successful, this final aim could form the basis of a future larger-scale study.
With a large fraction of the adult population suffering from hypertension, it is important to have the proper statistical tools to measure the impact of different patterns of antihypertensive medication adherence on blood pressure control for the purpose of measuring medication effectiveness and gaining understanding into health disparities. The goal of this project is to develop such tools, and demonstrate their applicability to measuring effects of antihypertensive medication on changes in blood pressure. The results of this project will pave the way towards a clinical-decision making tool that may help providers in the management of hypertensive patients' blood pressure.
