Association of obesity with arterial stiffness: The Multi-Ethnic Study of Atherosclerosis (MESA).

Arterial stiffness (AS) and obesity are recognized as important risk factors of cardiovascular disease (CVD). The purpose of this study was to investigate the relationship between AS and obesity. AS was defined as high augmentation index (AIx) and low elasticity (C1, large artery elasticity; C2, small artery elasticity) in participants enrolled in the Multi-Ethnic Study of Atherosclerosis at baseline. We compared AIx, C1, and C2 by body mass index (BMI) (< 25, 25-29.9, 30-39.9, ⩾ 40 kg/m2) and waist-hip ratio (WHR) (< 0.85, 0.85-0.99, ⩾ 1). The obesity-AS association was tested across 10-year age intervals. Among 6177 participants (62 ± 10 years old, 52% female), a significant inverse relationship was observed between obesity and AS. After adjustments for CVD risk factors, participants with a BMI > 40 kg/m2 had 5.4% lower AIx (mean difference [Δ] = -0.82%; 95% CI: -1.10, -0.53), 15.4% higher C1 (Δ = 1.66 mL/mmHg ×10; 95% CI: 1.00, 2.33), and 40.2% higher C2 (Δ = 1.49 mL/mmHg ×100; 95% CI: 1.15, 1.83) compared to those with a BMI < 25 kg/m2 (all p for trend < 0.001). Participants with a WHR ⩾ 1 had 5.6% higher C1 (∆ = 0.92 mL/mmHg ×10; 95% CI: 0.47, 1.37) compared to those with a WHR < 0.85. The WHR had a significant interaction with age on AIx and C2, but not with BMI; the inverse relationships of the WHR with AIx and C2 were observed only in participants < 55 years between the normal (WHR < 0.85) and the overweight (0.85 ⩽ WHR < 0.99) groups. Different associations of WHR and BMI with arterial stiffness among older adults should be further investigated.

Regional and racial disparities in major amputation rates among medicare beneficiaries with diabetes: a retrospective study in the southeastern USA.

Objective: While rates for non-traumatic lower extremity amputations (LEA) have been declining, concerns exist over disparities. Our objectives are to track major LEA (MLEA) rates over time among Medicare beneficiaries residing in a high diabetes prevalence region in the southeastern USA (the diabetes belt) and surrounding areas. Methods: We used Medicare claims files for ~900 000 fee-for-service beneficiaries aged ≥65 years in 2006-2015 to track MLEA rates per 1000 patients with diabetes. We additionally conducted a cross-sectional analysis of data for 2015 to compare regional and racial disparities in major amputation risks after adjusting for demographic, socioeconomic, access-to-care and foot complications and other health factors. The Centers for Disease Control and Prevention defined the diabetes belt as 644 counties across Appalachian and southeastern US counties with high prevalence. Results: MLEA rates were 3.9 per 1000 in the Belt compared with 2.8 in the surrounding counties in 2006 and decreased to 2.3 and 1.6 in 2015. Non-Hispanic black patients had 8.5 and 6.9 MLEAs per 1000 in 2006 and 4.8 and 3.5 in 2015 in the Belt and surrounding counties, respectively, while the rates were similar for non-Hispanic white patients in the two areas. Although amputation rates declined rapidly in both areas, non-Hispanic black patients in the Belt consistently had >3 times higher rates than non-Hispanic whites in the Belt. After adjusting for patient demographics, foot complications and healthcare access, non-Hispanic blacks in the Belt had about twice higher odds of MLEAs compared with non-Hispanic whites in the surrounding areas. Discussion: Our data show persistent disparities in major amputation rates between the diabetes belt and surrounding counties. Racial disparities were much larger in the Belt. Targeted policies to prevent MLEAs among non-Hispanic black patients are needed to reduce persistent disparities in the Belt.

Evaluating efficiency of counties in providing diabetes preventive care using data envelopment analysis.

For patients with diabetes, annual preventive care is essential to reduce the risk of complications. Local healthcare resources affect the utilization of diabetes preventive care. Our objectives were to evaluate the relative efficiency of counties in providing diabetes preventive care and explore potential to improve efficiencies. The study setting is public and private healthcare providers in US counties with available data. County-level demographics were extracted from the Area Health Resources File using data from 2010 to 2013, and individual-level information of diabetes preventive service use was obtained from the 2010 Behavioral Risk Factor Surveillance System. 1112 US counties were analyzed. Cluster analysis was used to place counties into three similar groups in terms of economic wellbeing and population characteristics. Group 1 consisted of metropolitan counties with prosperous or comfortable economic levels. Group 2 mostly consisted of non-metropolitan areas between distress and mid-tier levels, while Group 3 were mostly prosperous or comfortable counties in metropolitan areas. We used data enveopement analysis to assess efficiencies within each group. The majority of counties had modest efficiency in providing diabetes preventive care; 36 counties (57.1%), 345 counties (61.1%), and 263 counties (54.3%) were inefficient (efficiency scores < 1) in Group 1, Group 2, and Group 3, respectively. For inefficient counties, foot and eye exams were often identified as sources of inefficiency. Available health professionals in some counties were not fully utilized to provide diabetes preventive care. Identifying benchmarking targets from counties with similar resources can help counties and policy makers develop actionable strategies to improve performance.

A Method for Balancing Provider Schedules in Outpatient Specialty Clinics.

Background. Variability in outpatient specialty clinic schedules contributes to numerous adverse effects including chaotic clinic settings, provider burnout, increased patient waiting times, and inefficient use of resources. This research measures the benefit of balancing provider schedules in an outpatient specialty clinic. Design. We developed a constrained optimization model to minimize the variability in provider schedules in an outpatient specialty clinic. Schedule variability was defined as the variance in the number of providers scheduled for clinic during each hour the clinic is open. We compared the variance in the number of providers scheduled per hour resulting from the constrained optimization schedule with the actual schedule for three reference scenarios used in practice at M Health Fairview's Clinics and Surgery Center as a case study. Results. Compared to the actual schedules, use of constrained optimization modeling reduced the variance in the number of providers scheduled per hour by 92% (1.70-0.14), 88% (1.98-0.24), and 94% (1.98-0.12). When compared with the reference scenarios, the total, and per provider, assigned clinic hours remained the same. Use of constrained optimization modeling also reduced the maximum number of providers scheduled during each of the actual schedules for each of the reference scenarios. The constrained optimization schedules utilized 100% of the available clinic time compared to the reference scenario schedules where providers were scheduled during 87%, 92%, and 82% of the open clinic time, respectively. Limitations. The scheduling model's use requires a centralized provider scheduling process in the clinic. Conclusions. Constrained optimization can help balance provider schedules in outpatient specialty clinics, thereby reducing the risk of negative effects associated with highly variable clinic settings.

Cost-related medication non-adherence among U.S. adults with diabetes.

AIMS: To examine factors that affect cost-related medication non-adherence (CRN), defined as taking medication less than as prescribed because of cost, among adults with diabetes and to determine their relative contribution in explaining CRN. METHODS: Behavioral Risk Factor Surveillance System data for 2013-2014 were used to identify individuals with diabetes and their CRN. We modeled CRN as a function of financial factors, regimen complexity, and other contextual factors including diabetes care, lifestyle, and health factors. Dominance analysis was performed to rank these factors by relative importance. RESULTS: CRN among U.S. adults with diabetes was 16.5%. Respondents with annual income <$50,000 and without health insurance were more likely to report CRN, compared to those with income ≥$50,000 and those with insurance, respectively. Insulin users had 1.24 times higher risk of CRN compared to those not on insulin. Contextual factors that significantly affected CRN included diabetes care factors, lifestyle factors, and comorbid depression, arthritis, and COPD/asthma. Dominance analysis showed health insurance was the most important factor for respondents <65 and depression was the most important factor for respondents ≥65. CONCLUSIONS: In addition to traditional risk factors of CRN, compliance with annual recommendations for diabetes and healthy lifestyle were associated with lower CRN. Policies and social supports that address these contextual factors may help improve CRN.

Actigraphy-based sleep characteristics and aortic stiffness: the Multi-Ethnic Study of Atherosclerosis.

The study aimed to examine the association between objective estimates of sleep duration and quality and aortic stiffness while accounting for the potential confounding effect of sleep-disordered breathing. Participants were part of the Multi-Ethnic Study of Atherosclerosis Sleep study. Sleep duration and quality were assessed by 7-day wrist actigraphy, sleep-disordered breathing by home polysomnography, and aortic stiffness by magnetic resonance imaging-based aortic pulse wave velocity (aPWV), ascending and descending aorta distensibility. Aortic stiffness of participants with "normal" sleep duration (6-8 hours) were compared with those of "short" (<6 hours) and "long" sleep duration (>8 hours) adjusting for common cardiovascular risk factors and apnea-hypopnea index. The sample consisted of 908 participants (mean age 68.4 ± 9.1 years, 55.3% female). There was a significant linear trend of increased aPWV across short (n = 252), normal (n = 552), and long sleep durations (n = 104) (P for trend = .008). Multivariable analysis showed that people with short sleep duration had 0.94 m/s lower aPWV (95% CI: -1.54, -0.35), compared with those with normal sleep duration. In this ethnically diverse community cohort, habitual short sleep duration as estimated by actigraphy was associated with lower aortic stiffness.

From Data to Improved Decisions: Operations Research in Healthcare Delivery.

BACKGROUND: The Operations Research Interest Group (ORIG) within the Society of Medical Decision Making (SMDM) is a multidisciplinary interest group of professionals that specializes in taking an analytical approach to medical decision making and healthcare delivery. ORIG is interested in leveraging mathematical methods associated with the field of Operations Research (OR) to obtain data-driven solutions to complex healthcare problems and encourage collaborations across disciplines. This paper introduces OR for the non-expert and draws attention to opportunities where OR can be utilized to facilitate solutions to healthcare problems. METHODS: Decision making is the process of choosing between possible solutions to a problem with respect to certain metrics. OR concepts can help systematically improve decision making through efficient modeling techniques while accounting for relevant constraints. Depending on the problem, methods that are part of OR (e.g., linear programming, Markov Decision Processes) or methods that are derived from related fields (e.g., regression from statistics) can be incorporated into the solution approach. This paper highlights the characteristics of different OR methods that have been applied to healthcare decision making and provides examples of emerging research opportunities. EXAMPLES: We illustrate OR applications in healthcare using previous studies, including diagnosis and treatment of diseases, organ transplants, and patient flow decisions. Further, we provide a selection of emerging areas for utilizing OR. CONCLUSIONS: There is a timely need to inform practitioners and policy makers of the benefits of using OR techniques in solving healthcare problems. OR methods can support the development of sustainable long-term solutions across disease management, service delivery, and health policies by optimizing the performance of system elements and analyzing their interaction while considering relevant constraints.

Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.

Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis.

BACKGROUND: Currently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes. METHODS: We developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individual-level risk of cancer progression, and for genomics-based care versus usual care treatment recommendations. RESULTS: Cancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death. CONCLUSIONS: The use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.