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BackgroundUnderstanding resource utilization patterns among high-cost patients may inform cost reduction strategies.ObjectiveTo identify patterns of high-cost healthcare utilization and associated clinical diagnoses and to quantify the significance of hot-spotters among high-cost users.DesignRetrospective analysis of high-cost patients in 2012 using data from electronic medical records, internal cost accounting, and the Centers for Medicare and Medicaid Services. K-medoids cluster analysis was performed on utilization measures of the highest-cost decile of patients. Clusters were compared using clinical diagnoses. We defined “hot-spotters” as those in the highest-cost decile with ≥4 hospitalizations or ED visits during the study period.Participants and ExposureA total of 14,855 Medicare Fee-for-service beneficiaries identified by the Medicare Quality Resource and Use Report as having received 100 % of inpatient care and ≥90 % of primary care services at Cleveland Clinic Health System (CCHS) in Northeast Ohio. The highest-cost decile was selected from this population.Main MeasuresHealthcare utilization and diagnoses.Key ResultsThe highest-cost decile of patients (n = 1486) accounted for 60 % of total costs. We identified five patient clusters: “Ambulatory,” with 0 admissions; “Surgical,” with a median of 2 surgeries; “Critically Ill,” with a median of 4 ICU days; “Frequent Care,” with a median of 2 admissions, 3 ED visits, and 29 outpatient visits; and “Mixed Utilization,” with 1 median admission and 1 ED visit. Cancer diagnoses were prevalent in the Ambulatory group, care complications in the Surgical group, cardiac diseases in the Critically Ill group, and psychiatric disorders in the Frequent Care group. Most hot-spotters (55 %) were in the “frequent care” cluster. Overall, hot-spotters represented 9 % of the high-cost population and accounted for 19 % of their overall costs.ConclusionsHigh-cost patients are heterogeneous; most are not so-called “hot-spotters” with frequent admissions. Effective interventions to reduce costs will require a more multi-faceted approach to the high-cost population.

This paper explores the effect of equity volatility on corporate bond yields. Panel data for the late 1990s show that idiosyncratic firm-level volatility can explain as much cross-sectional variation in yields as can credit ratings. This finding, together with the upward trend in idiosyncratic equity volatility documented by Campbell, Lettau, Malkiel, and Xu (2001), helps to explain recent increases in corporate bond yields.

Objectives. To assess years of life lost to each cause of death in the United States between 1995 and 2015, and compare it with the number of deaths. Methods. We used Vital Statistics mortality data and defined “life-years lost” as remaining life expectancy for each decedent’s age, sex, and race. We calculated the share of life-years lost to each cause of death in each year, and examined reasons for changes. Results. In 2015, heart disease caused the most deaths, but cancer caused 23% more life-years lost. Life-years lost to heart disease declined 6% since 1995, whereas life-years lost to cancer increased 16%. The increase for cancer was entirely attributable to population growth and longer life expectancy; had these factors remained constant, life-years lost to heart disease and cancer would have fallen 56% and 38%, respectively. Accidents (including overdoses), suicides, and homicides each caused twice the share of life-years lost as deaths. Measuring life-years lost highlighted racial disparities in heart disease, homicides, and perinatal conditions. Conclusions. Life-years lost may provide additional context for understanding long-term mortality trends.

Background The US Preventive Services Task Force recommends 25 primary preventive services for middle-aged adults, but it can be difficult to do them all. Methods The Personalized Disease Prevention (PDP) cluster-randomized clinical trial will evaluate whether patients and their providers benefit from an evidence-based decision tool to prioritize preventive services based on their potential to improve quality-adjusted life expectancy. The decision tool will be individualized for patient risk factors and available in the electronic health record. This Phase III trial seeks to enroll 60 primary care providers (clusters) and 600 patients aged 40–75 years. Half of providers will be assigned to an intervention to utilize the decision tool with approximately 10 patients each, and half will be assigned to usual care. Mixed-methods follow-up will include collection of preventive care utilization from electronic health records, patient and physician surveys, and qualitative interviews. We hypothesize that quality-adjusted life expectancy will increase by more in patients who receive the intervention, as compared with controls. Discussion PDP will test a novel, holistic approach to help patients and providers prioritize the delivery of preventive services, based on patient risk factors in the electronic health record. Trial registration ClinicalTrials.gov NCT05463887. Registered on July 19, 2022.

Importance Cervical cancer screening guidelines are in evolution. Current guidelines do not differentiate recommendations based on individual patient risk. Objective To derive and validate a tool for predicting individualized probability of cervical intraepithelial neoplasia grade 2 or higher (CIN2+) at a single time point, based on demographic factors and medical history. Design The study design consisted of an observational cohort with hierarchical generalized linear regression modeling. Setting The study was conducted in a setting of 33 primary care practices from 2004 to 2010. Participants The participants of the study were women aged ≥ 30 years. Main outcome and measures CIN2+ was the main outcome on biopsy, and the following predictors were included: age, race, marital status, insurance type, smoking history, median income based on zip code, prior human papilloma virus (HPV) results. Results The final dataset included 99,319 women. Of these, 745 (0.75%) had CIN2+. The multivariable model had a C-statistic of 0.81. All factors but race were independently associated with CIN2+. The model categorized women as having below-average CIN2+ risk (0.15% predicted vs. 0.12% observed risk), average CIN2+ risk (0.42% predicted vs. 0.36% observed), and above-average CIN2+ risk (1.76% predicted vs. 1.85% observed). Before screening, women at below-average risk had a risk of CIN2+ well below that of women with ASCUS and HPV negative (0.12 vs. 0.20%). Conclusions and relevance A multivariable model using data from the electronic health record was able to stratify women across a 50-fold gradient of risk for CIN2+. After further validation, use of a similar model could enable more targeted cervical cancer screening.

BackgroundEvidence-based preventive care in the USA is underutilized, diminishing population health and worsening health disparities. We developed Project ACTIVE, a program to improve adherence with preventive care goals through personalized and patient-centered care.ObjectiveTo determine whether Project ACTIVE improved utilization of preventive care and/or estimated life expectancy compared to usual care.Design: Single-site randomized controlled trial.ParticipantsCluster-randomized 140 English or Spanish speaking adult patients in primary care with at least one of twelve unfulfilled preventive care goals based on USPSTF grade A and B recommendations.InterventionProject ACTIVE employs a validated mathematical model to predict and rank individualized estimates of health benefit that would arise from improved adherence to different preventive care guidelines. Clinical staff engaged the participant in a shared medical decision-making (SMD) process to identify highest priority unfulfilled clinical goals, and health coaching staff engaged the participant to develop and monitor action steps to reach those goals.Main MeasuresChange in number of unfulfilled preventive care goals from USPSTF grade A and B recommendations and change in overall gain in estimated life expectancy.Key ResultsIn an intent-to-treat analysis, Project ACTIVE increased the average number of fulfilled preventive care goals out of 12 by 0.68 in the intervention arm compared with 0.15 in the control arm (mean difference [95% CI] 0.53 [0.19–0.86]), yielding a gain in estimated life expectancy of 8.8 months (3.8, 14.2). In a per-protocol analysis, Project ACTIVE increased fulfilled preventive care goals by 0.80 in the intervention arm compared with 0.16 in the control arm (mean difference [95% CI], 0.65 [0.25–1.04]), yielding a gain in estimated life expectancy of 13.7 months (6.2, 21.2). Among the 12 preventive care goals, more improvement occurred for alcohol use, hypertension, hyperlipidemia, depression, and smoking.ConclusionsProject ACTIVE improved unfulfilled preventive care goals and improved estimated life expectancy.Clinical Trial Registration NumberNCT04211883

Background Hospitalized older adults spend as much as 95% of their time in bed, which can result in adverse events and delay recovery while increasing costs. Observational studies have shown that general mobility interventions (e.g., ambulation) can mitigate adverse events and improve patients’ functional status. Mobility technicians (MTs) may address the need for patients to engage in mobility interventions without overburdening nurses. There is no data, however, on the effect of MT-assisted ambulation on adverse events or functional status, or on the cost tradeoffs if a MT were employed. The AMBULATE study aims to determine whether MT-assisted ambulation improves mobility status and decreases adverse events for older medical inpatients. It will also include analyses to identify the patients that benefit most from MT-assisted mobility and assess the cost-effectiveness of employing a MT. Methods The AMBULATE study is a multicenter, single-blind, parallel control design, individual-level randomized trial. It will include patients admitted to a medical service in five hospitals in two regions of the USA. Patients over age 65 with mild functional deficits will be randomized using a block randomization scheme. Those in the intervention group will ambulate with the MT up to three times daily, guided by the Johns Hopkins Mobility Goal Calculator. The intervention will conclude at hospital discharge, or after 10 days if the hospitalization is prolonged. The primary outcome is the Short Physical Performance Battery score at discharge. Secondary outcomes are discharge disposition, length of stay, hospital-acquired complications (falls, venous thromboembolism, pressure ulcers, and hospital-acquired pneumonia), and post-hospital functional status. Discussion While functional decline in the hospital is multifactorial, ambulation is a modifiable factor for many patients. The AMBULATE study will be the largest randomized controlled trial to test the clinical effects of dedicating a single care team member to facilitating mobility for older hospitalized patients. It will also provide a useful estimation of cost implications to help hospital administrators assess the feasibility and utility of employing MTs. Trial registration Registered in the United States National Library of Medicine clinicaltrials.gov (# NCT05725928). February 13, 2023.

BACKGROUND: Studies rarely find fewer calories purchased following calorie labeling implementation. However, few studies consider whether estimates of the number of calories purchased improved following calorie labeling legislation. FINDINGS: Researchers surveyed customers and collected purchase receipts at fast food restaurants in the United States cities of Philadelphia (which implemented calorie labeling policies) and Baltimore (a matched comparison city) in December 2009 (pre-implementation) and June 2010 (post-implementation). A difference-in-difference design was used to examine the difference between estimated and actual calories purchased, and the odds of underestimating calories. Participants in both cities, both pre- and post-calorie labeling, tended to underestimate calories purchased, by an average 216–409 calories. Adjusted difference-in-differences in estimated-actual calories were significant for individuals who ordered small meals and those with some college education (accuracy in Philadelphia improved by 78 and 231 calories, respectively, relative to Baltimore, p = 0.03-0.04). However, categorical accuracy was similar; the adjusted odds ratio [AOR] for underestimation by >100 calories was 0.90 (p = 0.48) in difference-in-difference models. Accuracy was most improved for subjects with a BA or higher education (AOR = 0.25, p < 0.001) and for individuals ordering small meals (AOR = 0.54, p = 0.001). Accuracy worsened for females (AOR = 1.38, p < 0.001) and for individuals ordering large meals (AOR = 1.27, p = 0.028). CONCLUSIONS: We concluded that the odds of underestimating calories varied by subgroup, suggesting that at some level, consumers may incorporate labeling information.