The aims of the study are:
1. To identify patients with obesity alone versus with the obesity-hypertension-depression constellation, using over 10 years of retrospective EpicCare EHR data. Although we are primarily interested in patients with all three conditions and tracking their progression, we recognize that few patients go directly from being healthy to having all three conditions. The timing of occurrence may also have clinical and treatment implications. For example, depression followed by obesity and then hypertension may be appropriately managed with an initial focus on the depression rather in contrast to obesity followed by hypertension and then depression, in which weight management may take priority. These progressions, as well as the proportion of patients with each of the three components of the constellation, may differ by age, sex, and race.
2. To examine the effectiveness of an intensive lifestyle intervention for patients living with various combinations of the three priority conditions. The intervention is currently being delivered in a randomized controlled trial, entitled “Evaluation of Lifestyle Interventions to Treat Elevated Cardiometabolic Risk in Primary Care (E-LITE), jointly funded by NIDDK and American Heart Association and led by Jun Ma, MD, PhD. (Dr Ma is a co-investigator of the Trends Study.) Some of these patients will also have hypertension and depression. We will test if the presence of hypertension and/or depression affects the effectiveness of E-LITE interventions in reducing obesity and cardiometabolic risk.
3. To assess how well the sample of patients and physicians in E-LITE represents the patients and physicians in clinics of the Palo Alto Medical Foundation (PAMF). We will compare characteristics of patient participants in E-LITE with non-participating patients who also have obesity. As with any randomized interventional trial, there are concerns about the degree to which the patients enrolled in the trial are representative of those in a larger population who would otherwise meet the inclusion/exclusion criteria. For example, patients willing to be randomized and participate in the intervention, and consent, and data collection processes may be more adherent than other patients. By accessing data from the EHR from both E-LITE participants and (with de-identified data) non-participants, we will examine this issue. Physicians willing to refer their patients to a trial may also be different from other physicians. We will therefore compare practice styles regarding diagnosing and prescribing patterns of E-LITE physicians with non-–E-LITE physicians at PAMF.
Agency for Healthcare Research & Quality
September 30, 2010
September 29, 2012