BackgroundOne in 3 US adults has high blood pressure, or hypertension. As prior projections suggest hypertension is the costliest of all cardiovascular diseases, it is important to define the current state of healthcare expenditures related to hypertension.Methods and ResultsWe used a nationally representative database, the Medical Expenditure Panel Survey, to calculate the estimated annual healthcare expenditure for patients with hypertension and to measure trends in expenditure longitudinally over a 12‐year period. A 2‐part model was used to estimate adjusted incremental expenditures for individuals with hypertension versus those without hypertension. Sex, race/ethnicity, education, insurance status, census region, income, marital status, Charlson Comorbidity Index, and year category were included as covariates. The 2003–2014 pooled data include a total sample of 224 920 adults, of whom 36.9% had hypertension. Unadjusted mean annual medical expenditure attributable to patients with hypertension was $9089. Relative to individuals without hypertension, individuals with hypertension had $1920 higher annual adjusted incremental expenditure, 2.5 times the inpatient cost, almost double the outpatient cost, and nearly triple the prescription medication expenditure. Based on the prevalence of hypertension in the United States, the estimated adjusted annual incremental cost is $131 billion per year higher for the hypertensive adult population compared with the nonhypertensive population.ConclusionsIndividuals with hypertension are estimated to face nearly $2000 higher annual healthcare expenditure compared with their nonhypertensive peers. This trend has been relatively stable over 12 years. Healthcare costs associated with hypertension account for about $131 billion. This warrants intense effort toward hypertension prevention and management.

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BACKGROUND: National administrative datasets have demonstrated increased risk-adjusted mortality among patients undergoing interhospital transfer (IHT) compared to patients admitted through the emergency department (ED). OBJECTIVE: To investigate the impact of patient-level data not available in larger administrative datasets on the association between IHT status and in-hospital mortality. DESIGN: Retrospective cohort study with logistic regression analyses to examine the association between IHT status and in-hospital mortality, controlling for covariates that were potential confounders. Model 1: IHT status, admit service. Model 2: model 1 and patient demographics. Model 3: model 2 and disease-specific conditions. Model 4: model 3 and vital signs and laboratory data. PARTICIPANTS: Nine thousand three hundred twentyeight adults admitted to Medicine services. MAIN MEASURES: Interhospital transfer status, coded as an unordered categorical variable (IHT vs ED vs clinic), was the independent variable. The primary outcome was in-hospital mortality. Secondary outcomes included unadjusted length of stay and total cost. KEY RESULTS: IHT patients accounted for 180 out of 484 (37%) in-hospital deaths, despite accounting for only 17% of total admissions. Unadjusted mean length of stay was 8.4 days vs 5.6 days (p < 0.0001) and mean total cost was $22,647 vs $12,968 (p < 0.0001) for patients admitted via IHT vs ED respectively. The odds ratios (OR) for inhospital mortality for patients admitted via IHT compared to the ED were as follows: model 1 OR, 2.06 (95% CI 1.66-2.56, p < 0.0001); model 2 OR, 2.07 (95% CI 1.66-2.58, p < 0.0001); model 3 OR, 2.07 (95% CI 1.63-2.61, p < 0.0001); model 4 OR, 1.70 (95% CI 1.31-2.19, p < 0.0001). The AUCs of the models were as follows: model 1, 0.74; model 2, 0.76; model 3, 0.83; model 4, 0.88, consistent with a good prediction model. CONCLUSIONS: Patient-level characteristics affect the association between IHT and in-hospital mortality. After adjusting for patient-level clinical characteristics, IHT status remains associated with in-hospital mortality.

Background: Remote physiological monitoring (RPM) is accessible, convenient, relatively inexpensive, and can improve clinical outcomes. Yet, it is unclear in which clinical setting or target population RPM is maximally effective. Objective: To determine whether patients' demographic characteristics or clinical settings are associated with data transmission and engagement. Methods: This is a prospective cohort study of adults enrolled in a diabetes RPM program for a minimum of 12 months as of April 2020. We developed a multivariable logistic regression model for engagement with age, gender, race, income, and primary care clinic type as variables and a second model to include first-order interactions for all demographic variables by time. The participants included 549 adults (mean age 53 years, 63% female, 54% Black, and 75% very low income) with baseline hemoglobin A1c ‡8.0% and enrolled in a statewide diabetes RPM program. The main measure was the transmission engagement over time, where engagement is defined as a minimum of three distinct days per week in which remote data are transmitted. Results: Significant predictors of transmission engagement included increasing age, academic clinic type, higher annual household income, and shorter time-in-program (p < 0.001 for each). Self-identified race and gender were not significantly associated with transmission engagement (p = 0.729 and 0.237, respectively). Conclusions: RPM appears to be an accessible tool for minority racial groups and for the aging population, yet engagement is impacted by primary care location setting and socioeconomic status. These results should inform implementation of future RPM studies, guide advocacy efforts, and highlight the need to focus efforts on maintaining engagement over time.

As the prevalence of obesity increases, the prevalence of associated comorbid diseases, obesity‐related mortality rates and healthcare costs rise concordantly. Two main factors that hinder efforts to treat obesity include a lack of recognition by patients and documentation by physicians. This study evaluates the relationship between patient perception of obese weight and physician documentation of obesity. This quality improvement observational study surveyed patients of an academic internal medicine clinic on their perception of obesity. Responses were compared to longitudinal physician documentation of obesity and body mass index (BMI). A total of 59.9% of patients with obesity perceived their weight as obese. While 33.7% of patients with a BMI of 30 to 34.9 kg/m2 perceived themselves as having obesity, 71.4% of patients with a BMI of 45 to 49.9 kg/m2 perceived themselves as having obesity. A total of 42.4% of patients with obesity had physician documentation of obesity in the last year. While 25% of patients with a BMI of 30 to 34.9 kg/m2 had physician documentation of obesity, 85.7% of patients with a BMI of 45 to 49.9 kg/m2 had physician documentation of obesity. For patients with a BMI ≥50 kg/m2, 52.9% perceived their weight to be obese and 76.5% had physician documentation of obesity in the last year. Both patient perception and physician documentation of obesity were significantly less than the prevalence of obesity. Patient perception of obesity and provider documentation of obesity increased as BMI increased until a BMI ≥50 kg/m2. Both patients and providers must improve recognition of this disease.